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Disclaimer: I have no ownership over Twilight, but Stephenie does however. I also do not own the restaurant Island Prime. :\
I'd first like to give a very big thanks to everyone who added this story to their favorites/story alerts list! Also to the people who added me to their favorite authors list! :] It really does amaze me to see that so many of you guys like this story.
A huge thanks to everyone who reviewed! We are now officially over 100 reviews! YAY! :] A special thank you goes out to .screenname. for leaving such a nice review, and for having a cool name! :] Another special thanks goes out to StefaniJo for making me laugh.
I'm glad you're ready for the 'big mac and fries' because…here it is! ;]
"Hello, I am James, and I will be your server for the night."
"Hello James," Edward greeted him politely. I smiled at him and then went back to looking around the restaurant.
The place was basically split up into two sections. On one side, was the C-level lounge that looked more casual then the rest of the restaurant. The side that we were currently in however, was not as casual. The lights from above lighted the restaurant with a warm glow, and it made a huge affect on the rest of the place. Everything was arranged and placed in just the right places, creating a very nice affect of a calm and open feeling.
The only thing that seemed to be shared by this side and the lounge was a full bar.
It was absolutely beautiful.
But the best part of the restaurant was the view.
The skyline view was enchanting, to say the least. The sun was just beginning to set as the lights from the buildings and sun sparkled across the water.
I stopped staring at the breathtaking view when I finally realized that Edward and James were speaking to each other, and only caught the very last part of the question James had asked.
"-bottle of wine?"
Edward nodded. "Yes, please bring a bottle of your finest wine," he said smoothly.
The food was absolutely delicious. The flavor, texture, and all-around taste was magnificent. Edward had ordered a very nice bottle of wine; so nice that I wasn't sure if I wanted to even know the price of it. For an appetizer, we had both gotten the Lobster claws that James had suggested. As for my meal, I had ordered the fillet mignon. Both had been entirely tasty.
The rest of our dishes had been cleared away as Edward and I were now sharing a rather large slice of cheesecake for dessert. Throughout the whole meal, we had conversed a great deal, and I had gotten to know a lot about Edward.
For instance, next year would be Edward's third in college just like me, and his birthday is June 20.
However, I still didn't know one thing that I had been wondering about for a while now.
"Hey Edward? What are you planning to be when you graduate college?" I asked curiously. I ate a small piece of the cheesecake, savoring the sweet taste.
"I plan to become a doctor. A pediatrician, most probably," he said. He seemed to be thinking about something as he smiled.
Wow, a pediatrician. Could he be any more perfect?
"What about you Bella?" he asked me as he ate some of the cheesecake.
Suddenly, my goals for my job seemed to be pretty low compared to Edward being a pediatrician. Oh, well.
"I would like to become a journalist. Writing is my passion, and I think having a job of doing just that would be perfect," I said.
Edward gave me one of his dazzling smiles. "That's great, Bella! I'm sure you'll do wonderful," he said with full honesty in his voice.
This time I was the one who smiled.
Did I mention that Edward was perfect?
I was about to spoon out another piece of cheesecake when Edward stopped me.
I looked at him, confused.
"May I…may I feed the cheesecake to you?" he asked gently as he stared into my eyes.
I gulped. "Um, sure…"
He smiled at me as he took a piece of cheesecake off with his spoon. Without realizing it, I leaned closer towards Edward as he leaned towards me. Slowly, he reached over to me and stopped right in front of my lips. I parted my mouth open and watched him lean even closer as he put the spoon in my mouth.
I closed my eyes as I ate the sweet and delicious food.
When I opened my eyes, I saw Edward starting to get more cheesecake to feed me.
I stopped him, just like he had stopped me before. And just like before, this time Edward was the one to look curiously at me.
I smiled. "My turn," I said as I reached over to the cheesecake and took of some of it into my spoon.
I slowly leaned towards him as he did the same towards me, until we were both leaning in quite closely.
I reached over the very short distance and fed Edward the piece of cheesecake.
He ate it slowly, as if he were savoring the taste as I had done before. He didn't blink once as I stared into his eyes. His green eyes were sparkling as he gave me another dazzling smile.
Forgetting all about the cheesecake, I started leaning even closer towards him until we were directly face-to-face, just a few inches away.
Edward stayed frozen for just a moment, and then began to lean further towards me, until we were just a couple of inches away.
Just a couple of inches away from those perfect, soft lips.
I wanted them. I needed them.
So I went for them. I pressed my lips against Edward's. He immediately responded by deepening the kiss just a bit.
It was truly an amazing kiss. Probably even better then the first two kisses I shared with Edward. It seemed that each kiss we shared seemed to get better and better.
I would definitely not mind kissing Edward again and again. I mean, practice makes perfect, right?
But our kiss was interrupted in what felt like 2 seconds when I heard a voice coming from my left.
"Oh, look Harold! Aren't they just the cutest couple?" I looked over to see an elderly woman looking our way as she held another older man's hand.
He nodded in agreement as he smiled.
My face turned bright red. Did we really just look like we were a couple?
Huh, I could get used to that. It seemed that being Edward's girlfriend would just be amazing.
When Edward and I had finally finished feeding (try saying that three times fast?) each other, we of course had a small fight over who would pay the check. Of course, Edward had managed to win easily. Darn those dazzling skills.
We were now in his Volvo on our way back. It was a pretty short ride to my apartment since I live so close to the harbor. Although, I'm pretty sure it also had to do with the fact that Edward and I had been talking throughout the whole ride. Not to mention holding hands.
When we reached the parking lot of my apartment, Edward had gotten out of the car with me. "You don't have to come," I said. I didn't want him to feel obligated to bring me to my room.
"But I want to," he said sweetly as he smiled at me. He opened the main door of the apartment and held it out for me to go in first.
"Thank you," I said as I smiled at him as well.
We headed up the apartment in the elevator and to the door of apartment room Alice and I shared. I turned to Edward, a little sad that our special night was coming to an end.
"Thank you, Edward, for everything. Tonight was truly wonderful and special to me," I said.
He gave me a crooked smile as he said, "It was truly my pleasure. Thank you, for sharing the night with me," he said sweetly.
Sigh. Was he dreamy or what?
Now holding both of my hands in his, he leaned toward me and without any hesitation, pressed his lips fully on to mine.
It was a very gentle and simple kiss. Yet when we pulled away, Edward had still managed to leave me standing there, dazed.
"Bye, Bella. I will see you soon," he said with one last dazzling smile.
"Bye Edward," I breathed out.
We stared at each other for a moment longer, until he slowly made his way to the elevator. He waved to me when he had reached the end of the hall, in which I waved back.
When Edward was no longer in sight, the door to the apartment room flung open.
"Wow, Alice, you have perfect timing! You didn't happen to be eavesdropping, were you?" I asked as I giggled.
"Er, maybe. Sorry Bella! But I couldn't pass up the opportunity," she said sheepishly.
When I headed towards the living room with Alice trailing behind me excitedly, I saw Rosalie sitting one of the couches once I walked in.
"Hey Rosalie, " I said, a little surprised to see her here. I sat down on the couch next to her.
Before Rosalie could respond, I heard an extremely loud squeal. Alice attacked me with questions as I stared at her wide-eyed.
"Calm down, Alice," Rosalie said with a laugh. "I think you're scaring Bella." Alice huffed, but listened to Rosalie and stopped talking to breathe deeply.
When she had finished calming down, she immediately pounced on me again.
"So tell us what happened!"
Rosalie grinned as she added, "yeah, I came here just to hear what happened!"
I rolled my eyes. "Nothing really happened…" I said innocently.
"Ooohh, that means something happened!" Alice said knowingly. She plopped herself on the floor and stared at me as if I were telling the most fascinating story.
"Please, tell us what happened Bella?" Rosalie whined.
"Okay, fine. I'll start from the beginning," I said, bursting with as much excitement.
Then I told them everything. From being seated at our window booth, to the amazing kiss, and then to the car ride home.
Rosalie and Alice had "awed" when I told them about my amazing kiss experience, and everything that Edward had done sweetly.
"Well, I know one thing's for sure," Alice said once I had finished.
I looked at her curiously.
"You've got it bad," she said. She smiled me in a knowing way as Rosalie nodded her head in agreement. They both stood up and started to head out of the room. "Meet us in my room after you brush your teeth," she said happily.
I stared at their retreating forms as I thought over what Alice had said.
Could she be right? Did I really have it bad for Edward? Were the feelings that I were getting, even stronger then a small crush?
Then it hit me like a ton of bricks. The jealousy I felt when other girls had hit on Edward. The way he made my stomach fill with butterflies. The over-whelming amount of attraction I felt towards him.
I just couldn't believe it. But the more I thought about it, the more I realized that it made sense.
I was in love with Edward Cullen.
Well, what do you guys think? I really hope that you guys liked it! Thank you so much for reading everyone!


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To help realise its potential for speed, the X5 has the 4.4-litre, variably- valve-timed V8 already seen in grander BMW Fives and Sevens. It also has four-wheel drive, and is made in South Carolina alongside the Z3 sports car. Sounds good and sporty so far, then. However, that lofty body does not quite fit the deal, somehow. It's a sort of 5-series Touring on stilts, and those wheels are unfeasibly big.
There's a reason for this. The X5 is an off-roader.
To describe the X5 simply as an off-roader, though, is to miss the point. Americans describe cars such as the Jeep Grand Cherokee or Land Rover Discovery as "Sport Utility Vehicles" (SUVs). The sport part relates more to the activities that the car facilitates, than to the car itself. After all, few would describe a Discovery as "sporty".
The X5, however, is more than a mere SUV - BMW describe it is a SAV, or "Sports Activity Vehicle". This subtle distinction suggests that the X5 is itself an integral part of this sportiness thing. Hence the racetrack interlude above.
In a sense, though, the X5 has been designed back-to-front. You would expect an engineering-led company like BMW to think about function first, then devise a form to suit. Not here: the company's California-based Design Works studio began by deciding what a BMW-flavoured SUV/SAV should look like - a tricky task, given the apparent mutual exclusivity of sporty BMWs and muddy tracks - and only then did BMW work out how to make the form function.
It could have been a massive visual joke, but the designers got away with it. The same goes for the interior, in which you sit SUV-high but surrounded by familiar furnishings - a cosy BMW bungalow which has cleverly morphed into an open-plan house.
Among the house's mod cons are an electric reclining mechanism for the rear seat-backs, which you can operate while standing at the boot door so you can alter the load space to suit the shape of your load, and an automatic garage door-opener with three separate codes. X5 owners are expected to have at least one country retreat as well as their main abode.
And the driving? The X5 blows its rivals right away. At least it does so on metalled roads, with cornering so flat and fuss-free that it defies the normal expectations one holds about the physical laws' limits of jurisdiction.
Most big 4x4s feel woolly in their steering compared with a normal car, and hint that they might trip over their outside front wheels if cornering gets too enthusiastic. The X5 does nothing of this kind, helped by a bank of electronic minders which apply the brakes individually and modulate engine power to keep you pointing in the right direction.
It feels, broadly, like a bigger 5-series with a better view. Rigid, car-like unitary construction helps, partly because it makes dynamic forces easier to control accurately than they are with the truck-like, separate- chassis designs of most 4x4s (which, conversely, are tougher for yanking and towing).
That said, the standard suspension is a touch soft and queasiness-inducing, and it makes the steering unimpressive at straightening itself up after a tight corner. You have to feed the steering wheel back yourself, or else you might drive round in an unintended circle.
Neither criticism applies to the firmer Sport suspension and the attendant bigger wheels, which still give a good ride. It makes the X5 go faster - the standard version is electronically limited to 129mph instead of 143mph, simply because its tyres aren't up to higher speeds.
The V8 is a fine, lusty, but rather thirsty, engine. It emits a great sound from its four tail pipes - a sort of American V8 muscle car sound filtered through ear plugs - and channels its huge thrust via a smooth- shifting automatic transmission with an amusing, but ultimately unnecessary, Steptronic manual override.
There's no secondary set of ultra-low, off-road gear ratios, so owners of Jeeps and larger Land Rover products can retain a little smugness regarding their off-road greater potential, but the X5 copes pretty well off-road anyway. The X5 uses those electronic brake controls to calm spinning wheels, and - in the guise of the Hill Descent Control, first seen in the Freelander - to tip-toe gently down those slippery slopes.
It's no Land Rover Discovery, but it will do for most people's adventures. We'll get the X5 in Britain next autumn - with the less-dipsomaniac 3.0- litre, six-cylinder versions, both petrol and diesel, following the year after.
Prices will be just above those for same-engined, SE-specification 5- series Tourings. For the world's best-driving big 4x4, that's probably not too high a price to pay - even if it is a heck of a lot cheaper in its native America.
Model: BMW X5
Price: pounds 45,000 approx
Engine: 4,398cc V8, 32 valves, 286bhp at 5,400rpm
Transmission: 5-speed automatic gearbox, four-wheel drive
Performance: 143mph, 0-60 in 7.3sec, 17-21mpg
Jeep Grand Cherokee V8: pounds 34,995. Less agile and sophisticated than the X5 but ultimately less stoppable off-road. The V8 Cherokee is brash, fast, very capable.
Land Rover Discovery V8 ES: pounds 35,075. Like the Jeep, the top range Discovery makes the X5 seem expensive. But even its clever flat-cornering suspension can't make it match the BMW's on-road prowess.
Mercedes-Benz ML430: pounds 43,390: BMW money but, like the X5, the M-class is built in the US where cars are cheap. Pleasing drive and adept off- road. The interior is plasticky.
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USD/JPY: forecast for September 14-20
By Elizabeth Belugina
Japanese yen has weakened versus the US currency in the past week because of the expectations of the Bank of Japan's additional monetary policy easing. These expectations revived as the adviser to Japanese premier Shinzo Abe said that the central bank's October meeting presents a "good opportunity" for further quantitative easing (QE). Such comments hit the markers, which were worried because of the recent weak Japan's economic figures and the volatility in stocks.
For now, the Bank of Japan's Governor Kuroda stubbornly sticks to the positive view on Japan's economic prospects underlining that the regulator's policy is loose enough. Next week we will hear from Kuroda on Tuesday at the Bank of Japan's press conference and on Thursday when he is due to make a speech. Still, as the comments of Japanese central bank come before the meeting of the US Federal Reserve, we do not think that the position of Japanese regulator may seriously change. So, we believe that the Bank of Japan's Tuesday meeting will not seriously affect the market.
Note that during the most part of the upcoming trading week players will be in the situation of the uncertainty trying to foresee whether the Fed announces a rate hike on Thursday or not. Another source of uncertainty is China, which will publish industrial production data on Sunday, September 13. This uncertainty will limit USD/JPY on the upside.
USD/JPY may consolidate in the 120.70/119.00 area ahead of the Fed. On the upside, further resistance lies at 121.70, 122.00 and 122.50. On the downside, further support is at 118.30/00 and in the 116.00 zone.


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--Publishers Weekly
"A masterful storyteller, carefully crafting tales that keep readers on the edge of their seats."
--USA Today
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For a better shopping experience, please upgrade now.
The adopted daughter of the most powerful man in town, Schyler Crandall left Heaven, Louisiana, a broken-hearted
A woman returns home to deal with a family crisis, only to discover the situation is far worse than she realized. Grappling with hidden secrets, forbidden passions, and a business in peril, she must confront her past to bring peace back to her hometown.
The adopted daughter of the most powerful man in town, Schyler Crandall left Heaven, Louisiana, a broken-hearted girl. Now a crisis has brought her home to a family in conflict, a logging empire on the brink of disaster, and seething secrets that make Heaven hotter than hell.
Still there are her beautiful younger sister, Tricia, with her cruel lies. . .Ken, Tricia's handsome husband, who married the wrong sister. . .Jigger, the pimp and ruffian with plans of his own. . .and Cash, proud, mysterious, and complex. It is dangerous for Schyler to even be near him, yet she must dare to confront the past -- if there is to be any peace in Heaven.
"A masterful storyteller, carefully crafting tales that keep readers on the edge of their seats."
--USA Today
Sandra Brown is the author of sixty-eight New York Times bestsellers. There are over eighty million copies of her books in print worldwide, and her work has been translated into thirty-four languages. She lives in Texas. For more information you can visit www.SandraBrown.net.
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I love Sandra Brown and her books, Slow Heat in Heaven is by far my favorite yet. I know it was written years ago, but I wish Ms. Brown would write a sequal to it. I would love to read about Cash and Schyler and the children they might have together and what happens to them. It was totallyone of the best love stories and I was so anxious to get to the end tosee what happened but then I did not want it to end. Please please make this book a trilogy!!!!!
I have to disagree with most reveiwers here. I did not like this novel at all. I have read almost every single other novle written by Sandra Brown and this one was a complete dissapointment. I figured out the 'mysteries' very early in the book and the characters were very superficial. I couldn't stand Cash; I didn't think he had any redeeming qualities. Brown has written much better stories and much better characters in my opinion, including The Silken Web (Erik Gudjohnsen is the guy every woman wants to meet!), A Secret Splendor, Mirror Image, French Silk, among many others. Slow Heat in Heaven is possible the worst Sandra Brown book I've read.
By far, this is one of the BEST romance novel's I've ever read. This could've been a true story for how well it was written...very moving at times I practically cried. Great plot: suspense, mystery, scorching hot scenes, and intense situations. I will definitely read more from Sandra Brown.
This is my first Sandra Brown novel and I love it! The characters have stayed with me even though I am done reading the book. Schyler and Cash are great I truly enjoyed their adventures, and the rawness of it all. Just goes to show that romance isn't always mush. Tiki Cook
Unbelievable, No Way - How could Wonder Woman from an upper class Southern family tolerate and even enjoy demeaning interaction and rough sex with Mr. Hyde – a world class red-neck from the bayou? And yet, you'll read it to the end hoping to see if the bad guys (really bad) get hammered.
Sandra Brown has done it again! This story sucked me in from the beginning and kept me guessing with all the twists and turns. Just when you think you have it all figured out something new is revealed.
I too read this book when it was first realeased. I have since reread it many times over. When I need a shot of "Cash", I revisit this book! The story NEVER gets old. Would love it to be a movie but I don't think they would cast it perfectly or film the love scenes as the book is written. If you're looking for a great but realistic love story, read this book!!
Fantastic book. Good pacing, interesting characters and twists. Recommend.
Holy smokes! The sexual tension between schylar & cash is almost unbearable! Betrayed, & heart broken, schylar left heaven, louisiana 6 yrs ago, living in london.But she's back because her wealthy, powerful adoptive father is near death. She is determined to meet her old love head on, but cash is there, a man that her family has looked down on since childhood. Cash is dangerously sensual, bedding any woman that is willing & there have been many that were willing. He is embitttered & mean fed by his rejection as a child , a young man., & a mysteriuos secret that has been forced upon him. Schylar is a wonderful mix of lady like mannerisms yet compassionate & willing to fight for what is right. She is frightened by her attraction to cash & cash is not one to reciprocate with any kind of tenderness. But they must partner up to save her father's logging company & the livelihoods of the men who work for her father. There is a cast of fascinating characters, perfect for the beautiful yet dangerous bayou country. much like cash, who lives in & is able to move like a ghost throughout the bayou. There is an atmosphere of greed & evil that tragically affect ones that are not able to defend themselves & schylar is determined to right those wrongs even if it puts her in danger.
I loved this book. It was more thrilling than i thought it would be. I barely put it down!
From page 1 could,nt put it down. Wish there was more books like this,the location,setting,characters were so vivid it was like watching a movie
What an author! Have not read a dissappointing book by her. What an imagination this woman has. Cash is one sexy man. Yes he has a giant chip on his shoulder, but you csn tell from the beginning he has a soft spot for skyler. Great love story, great mystery
Great character development....I KNEW these people. Really enjoyed the relationship between Cash and Schyler.
This book kept me guessing who the bad guy was until the end!
This book contained so many troubled characters that it should spawn a separate book. I really enjoyed reading about Schyler and Cash. Sandra Brown is my new favorite author.
One of the best books ive ever read!


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- 1 cup all-purpose flour
- 3/4 cup salted macadamia nuts
- 1 teaspoon salt
- 1 teaspoon coarsely ground pepper
- 4 tablespoons cold unsalted butter
- 2 tablespoons water
- 1/4 cup freshly grated Parmesan cheese
- 1 large egg yolk, lightly beaten with 1 tablespoon water
In a food processor, pulse the flour with 1/2 cup of the nuts, the salt and pepper until the nuts are finely chopped. Add the butter and process until the mixture resembles fine meal. Add the water and process until the dough comes together. Shape the dough into an 8-inch log, wrap in plastic wrap and refrigerate until firm.
Preheat the oven to 375°. Line 2 baking sheets with parchment or wax paper. Finely chop the remaining 1/4 cup of macadamia nuts and mix with the Parmesan. Cut the log into scant 1/4-inch-thick slices and arrange them on the prepared baking sheets. Brush the slices with the egg wash and sprinkle with the Parmesan mixture. Bake the crackers for about 17 minutes, or until golden brown. Let cool on the baking sheets. Transfer the crackers to a large plate and serve.


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Firestone Destination A/T
I was offered a great deal on Firestone Destination A/T tires 235/75/15 for my 2001 TJ, was just looking for some feedback from anyone that has any experience with these tires. Good or Bad. I'm going to mainly be using the jeep for highway travel, with an occasional trip to my house upstate, no real mudding or 4 wheeling... any feedback is appreciated.
Consumer Survey Results By Category
I don't have them... but next set of tires I get will be those.
Ran them on my 04 ram.. Did fine.. as I recall did very well on wet roads too.
i have had them on my 03 TJ for about 25000 miles and still tread. i am 95% on highway and the rest is anywhere i can find play. They work good, can climb good with them. Deep mud is never good on any A/T but yeah they are good all around.
I have a set of 33's on mine they seem to handle pretty good but I still prefer the BFG's
When i bought tires i debated over the firestones destination ATs and goodyears silent armors. I have the same size as those, (235/ 75R15). From research that i have done both the firestones and goodyears are about the same. The only reason i went with goodyear is because i got a good deal from a guy i know who works at the shop. Untill he offered me the deal i was going to get the firestones. I still might try those once these wear out.
My buddy put them on his truck and loves them. Good ride plus their not to loud.
I put the Desitnations M/Ts on my Jeep and love them. Louder than the ATs, but it's a Jeep so who cares.
Good luck with the ATs.
Great tires go any where with them
lovin em
1 Attachment(s)
have only put about 100 miles or so on them, but i'm very happy so far. Noise level is much lower, I had a slight wobble at 60 mph, gone, and the 235's look much better
Looks good :punk:
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The Celestron T-2 Ring is designed to work with all Nikon 35mm cameras.
The Celestron T-2 Ring for Nikon couples your 35mm camera body to a T-Adapter, Radial Guider or Tele-Extender. This accessory is required if you want to do any type of photography through a telescope or spotting scope. 12 month Warranty.
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|Publication number
|Publication type
|Application number
|Publication date
|Apr 24, 2001
|Filing date
|Jun 25, 1997
|Priority date
|Jun 25, 1996
|Fee status
|Also published as
|Publication number
|Laurenz Wiskott, Christoph von der Malsburg
|Original Assignee
|Eyematic Interfaces, Inc.
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (25), Non-Patent Citations (55), Referenced by (79), Classifications (15), Legal Events (7)
|External Links: USPTO, USPTO Assignment, Espacenet
This application claim benefit to Provisional Application 60/020,810 filed Jun. 25, 1996.
The present invention relates to technology for recognizing and analyzing objects that are shown in camera or video images. In particular, the present invention relates to technology which compensates for variability of images for different objects of the same type, or for images of the same object (due to differences in position, scale, orientation, pose, illumination, deformation, noise etc.).
Techniques exist for analyzing captured images in order to perform image matching or other functions, such as the extraction of a fixed, sparse image representation from each image in an image gallery and for each new image to be recognized. Conventional techniques, however, require a great deal of computational power and time. When comparing a new image with all images stored in a gallery of thousands of images, each comparison requires a new, computationally-expensive matching process. There is a need for an image analysis technique that is more easily implemented.
It is therefore an objective of the present invention to provide a technique for image analysis that is more easily implemented than are conventional techniques, and which requires less computational power and time.
The present invention provides a process for image analysis. The process includes selecting a number M of images, forming a model graph from each of the number of images, such that each model has a number N of nodes, assembling the model graphs into a gallery, and mapping the gallery of model graphs into an associated bunch graph by using average distance vectors Δij for the model graphs as edge vectors in the associated bunch graph, such that
where Δij is a distance vector between nodes i and j in model graph m. A number M of jets is associated with each node of the associated bunch graph, and at least one jet is labeled with an attribute characteristic of one of the number of images. An elastic graph matching procedure is performed, wherein the graph similarity function is replaced by a bunch-similarity function S(G, GI) such that
The model bunch graph may be manually prepared by associating a standard grid of points over an image, correcting node positions to fall over designated sites characteristic of the image, and extracting jets at the nodes. Object recognition may be performed by selecting a target image, extracting an image graph from the target image, comparing the target image graph to the gallery of model graphs to obtain a graph similarity S(GM, GI) such that
identifying a model graph having a greatest graph similarity with the image graph.
FIG. 1 shows a data format for image representation.
FIG. 2 shows graphs as placed over objects.
FIG. 3 shows graphs as placed over objects, emphasizing an irregular, object-type adapted graph structure.
FIG. 4 shows a bunch graph and image graph.
The process of the invention is based on a technique of representing images by wavelets and of comparing different images by elastic graph matching. An application of the present invention is the extraction of a fixed, sparse image representation (called "image graph") from each image in the gallery and of each new image to be recognized. An image graph sparsely summarizes an image in terms of those data that are important for recognition or analysis. Comparison of two image graphs is computationally very cheap, and recognition from large galleries thus becomes feasible. The extraction of the relevant data (that is, of the image graph) from an image is achieved with reference to a small collection (the "bunch graph") of sample images in which the relevant data have been identified manually. The different sample images in the bunch graph are selected to cover a large range of variants of the type of objects to be recognized or analyzed. During the matching, of the bunch graph to an image, data taken from different sample images are combined. The bunch graph thus covers a virtually infinite range of variants.
Graphs G with an underlying two-dimensional topography are used. Each node denotes a jet Jn and each edge denotes a distance vector Δxe. In the simplest case the graph has the form of a rectangular grid with constant vertical and constant horizontal spacing between nodes.
The jets are based on a wavelet transform, which is defined as a convolution with a family of complex Gabor kernels
providing at each location x (that is, pixel row and column) the coefficients
given the image gray level distribution /(x). In the sum, x′ runs over all pixels within the area where Ψ(x−x′) is substantially different from zero.
The kernel Ψ(x−x′) has the shape of a plane wave (controlled by a wave vector kj) which is restricted by a Gaussian envelope function of width σ/k with σ=2π. In addition, the kernels are corrected for their DC value, i.e., the sum ΣxΨj(x) vanishes. All kernels are similar in the sense that they can be generated from one kernel simply by dilation and rotation. The array of k-values is used such that
with kernel size index νε{0, . . . , 4}, and orientation index με{0, . . . , 7}, for simplicity labeled with one index j=μ+8ν.
The full wavelet transform provides 40 complex coefficients at each pixel (5 frequencies and 8 orientations). This array of coefficients at one pixel will be referred to as the jet J(x), see FIG. 1. In the system of the present invention, jets play the role of local feature vectors. Images are compared in terms of jets (and not in terms of pixel values), as described below.
The complex jet coefficients Jj can be written as Jj(x)=aj(x)exp(iφj(x)) with a smoothly changing magnitude aj(x), and a phase iφj(x) spatially varying with the characteristic spatial frequency of the respective Gabor kernel. The two jets J and J′ can be compared in terms of a "magnitude only" similarity function
This similarity function Sa has the advantage of varying smoothly with shifts of the image. However, the present invention also makes use of a "phase sensitive" similarity function Sφ:
Here, the kj are the wave vectors of the Gabor kernels, as above, and d is an estimated displacement vector which compensates for the rapid phase shifts. d is determined by maximizing Sφ in its Taylor expansion within a small square centered on d=0. The term d•kj with the estimated displacement vector d compensates for rapid phase shifts due to small variation in the positions x or x′ of the two jets being compared.
Model Graphs
In order to store a part of an image that contains an object (like the face in the original image 1 in FIG. 1), a set "grid" of pixel positions ("nodes", see 23 in FIG. 2) is selected in the image. The grid can be exactly rectangular (as in 21 a, FIG. 2) or can be deformed (as in 21 b, FIG. 2) or of a different arrangement, such as in FIG. 3. Nodes can be selected by hand, or they can be selected by the automatic procedure described below. Pairs of neighboring nodes can be connected by "edges," see FIG. 2 or 3. For each node, after it is positioned within the image, the jet computed at that pixel location is recorded. In addition, for each edge, a distance vector is recorded (the distance vector is a pair of pixel distances in the x and y directions). This data structure (jets and distance vectors) is called an "image graph". It can be incorporated in a "model domain", by storing it in an appropriate file. In this case, the graph is called a "model graph". Herein, two types of model domains will be described, called "gallery" and "bunch graph", as described below.
Elastic Graph Matching
Elastic Graph Matching is the process by which a stored model graph GM is matched to a new image. Let the model graph have N nodes, labeled with jets Jn, and E edges, labeled with distance vectors Δxe. In the matching process, an image graph GI is selected from the new image. This image graph has the same structure as GM (same number of nodes, same pairs of nodes connected by edges). During the matching process, node points are selected in the image, jets are extracted from the image, distance vectors are computed, and the resulting structure, a "tentative image graph", is compared to the model graph GM. This comparison results in a "graph similarity"
The first term on the right-hand-side is called the "node similarity" and is computed as the average similarity between jets at corresponding nodes, computed with either the magnitude similarity S=Sa, equation 4, or the phase similarity S=Sφ, equation 5. The second term on the right-hand-side is the "edge similarity," computed as the average squared difference between pairs of corresponding edges. (The second term is also called the "distortion term"). The system parameter λ controls the relative importance of node similarity and edge similarity.
In the matching process, the graph similarity (6) between the image graph and the model graph is optimized by varying the node positions of the tentative image graph appropriately, with the jet for each node being determined anew after the position of the node is varied. After each modification, the new graph similarity is computed. If it is higher, the new tentative image graph is selected; if it is lower, the old tentative image graph is retained.
A simple procedure for optimizing the tentative image graph is as follows.
Global move (coarse): The first tentative image graph is formed as an exact replica of the relative node positions of the model graph, placed in the middle of the image. Then, the position of the tentative image graph is modified (without distortion) in random steps of plus or minus 5 pixels in x or y direction, until a (local) optimum is reached, using the magnitude similarity, equation 4.
Global move (fine): the procedure is repeated, with a step size of one pixel instead of 5, again using the magnitude similarity, equation 4.
Local move (magnitude): Randomly chosen nodes are shifted in random steps of plus or minus one pixel in the x or y direction, again using the magnitude similarity, equation 4. The procedure is repeated until N visits to nodes have not led to an improvement of the graph similarity.
Local move (phase): Randomly chosen nodes are visited and displacements d are chosen (up to a maximum of 1 pixel distance) to optimize the phase similarity, equation 5. Each node is visited just once.
The optimal graph resulting at the end of this elastic matching procedure is called the "image graph." For an example see 21 b in FIG. 2 or 31-34 in FIG. 3.
Although the optimization procedure just described is the preferred one, many variations are possible: using phase similarity early on; using only low-frequency components of the jets early in the procedure; varying the parameter λ with time; deforming the grid several nodes at a time; rotating the grid (with or without adjustment of the jet components for rotation); scaling the grid (with or without adjustment of the jet components for scale); and disregarding certain nodes at certain stages of the process or on the basis of their node similarity.
Bunch Graphs and Labeled Bunch Graphs
From a number M of images of different objects of a given class (and/or different images of the same object), M model graphs are assembled into a gallery. These M model graphs are all of the same qualitative structure, that is, they have the same number N of nodes (which are numbered 1 to N), and each pair of nodes (identified by a pair of numbers) is either connected in all of the M model graphs or not connected in all model graphs. The model graphs in the gallery differ in the jets that are affixed to the nodes and in the distance vectors affixed to the edges. One may imagine the model graphs as stacked up on each other, see FIG. 4, with nodes bearing the same number aligned with each other.
This gallery of model graphs is then mapped into a different graph structure, called a "bunch graph", constructed in the following way. Let the distance vector between nodes i and j in model graph m be designated as Δi m j. Then the average Δij of that distance vector over all model graphs is computed,
These average distance vectors are then used as the edge vectors in the new graph structure called the associated bunch graph. The associated bunch graph has N nodes, but differs from the image graphs and model graphs in that there are now M jets for each node of the bunch graph. Each jet in the bunch graph is designated by the number of the model graph in the associated gallery which contains that specific jet.
Each jet of this collection of models in the associated gallery and thus each corresponding jet in the bunch graph may be labeled with one or several attributes characterizing the object that is represented by the model. For example, if the models represent human faces, an attribute could be the gender of the person, or the presence or absence of attributes like eyeglasses or beard or a particular type of hair style.
The collection of jets of this kind plus the attribute labels attached to each jet, plus the average edge vectors, may be called a "model stacks" or "generalized object knowledge" or "labeled bunch graph," which latter term is the name adopted herein. The name derives from the view that the labeled bunch graph consists of a single graph with N nodes, each of which has M different jets, which are labeled by attributes in addition to being designated by a sequence number.
The bunch graph idea was developed for applications involving objects which are qualitatively of the same structure. Examplary object types are different individuals of a given animal species (man, dog, horse, . . . ), or different types of the same kind of technical implement (automobiles, motorcycles, airplanes, computer terminals . . . ), or corresponding parts of a kind of animal or implement (human face, human hand, front of automobile, tail of airplane . . . ). The general structure of the model grid (for instance, the aspect ratio length/width of the graphs, or the local density of nodes) is adapted to the general type of object or part to be handled, see FIG. 3 for examples. Further, the idea is that jets attached to the same node of the bunch graph encode the same qualitative region of the object (for instance, the left eye of all stored faces, if the application is concerned with faces).
Labeled Bunch Graph Matching
When comparing an image to the bunch graph G instead of to a single model graph GM, the elastic graph matching procedure previously described is to be modified simply by replacing the graph-similarity function S(GM, GI), equation 6, by a "bunch-similarity function"
In this, each node n of the tentative image graph GI, when compared to node n of the bunch graph, selects the jet with maximal similarity. The bunch-similarity function contains no term sensitive to deformations.
As when matching a single model graph to an image, the tentative image graph is modified to optimize its similarity to the bunch graph, this time using the similarity of equation 8, however. Optimization is done according to the same schedule as the elastic graph matching procedure described previously. After each move of a node of the tentative image graph, the node is free to pick the best-matching jet attached to the corresponding node of the bunch graph, see FIG. 4. Again, variation in size and orientation of the object in the image can be accommodated by allowing for changes, in size and orientation of the tentative image graph (especially in the early global move stage of the match), along with proper transformation of the jet components.
Practical Operations with the Labeled Bunch Graph
1. Manual Preparation of Bunch Graph
To deal with images of a specified type of object from a uniform viewpoint (for instance, frontal images of human faces), a bunch graph can be prepared by a "manual" method. A collection of representative images of that class of objects is acquired (typically of the order of 50 to 100). A suitable graph structure (that is, an arrangement of node points plus a set of edges connecting them) is selected. For examples see FIGS. 2 and 3. The graph structure can be regular, as in FIG. 2, or it can be adapted to the object type, see FIG. 3.
The qualitative graph is then placed on individual images, such that node points occupy appropriately fixed positions. This is done by first placing a standard grid of points (depending on the object) over the object in the image, and by then correcting individual node positions such that they fall over appropriately designated sites characteristic of the object (e.g., in facial images, the centers of the eyes, the comers of the mouth, etc.). For this process it is convenient to use a graphical interface which allows the user to move node points around with the help of a computer mouse.
When node points are selected in all images of the collection, jets are extracted at those node points and are assembled into the bunch graph structure described previously.
Individual jets or groups of jets in the bunch graph can be manually labeled with attribute information (such as gender of a person, breed of animal, make of car, etc.).
2. Automatic Image Graph Generation
After a bunch graph for a particular type of object has been prepared, new images can be analyzed automatically by bunch graph matching, as described previously. If the gallery associated with the bunch graph covers enough object variants, bunch graph matching will result in reliable and precise placement of nodes on their intended object parts. Image graph generation can take place in two steps. In the first step, a bunch graph is used that is optimized for reliably finding instances of the object type in images and to estimate the size and the orientation of the object. With this information in hand, an appropriate region of the image can be cut out and normalized as to size. In this region, the final image graph can be identified by matching to a second bunch graph, one that is designed to extract the data important for the final purpose (such as object recognition). For example of grids adapted to these two stages see FIG. 3. Image graphs thus created are stored in a gallery for later use.
3. Object Recognition by Comparison to Galleries of Model Graphs
With the help of a bunch graph (or several bunch graphs to support several stages as described and/or to cover different poses or types), an image graph is extracted from an image (or video frame) that is presented to the system. This image graph can then be compared efficiently to a gallery of stored image graphs of the same pose or type. This image graph comparison is done in terms of a simple graph similarity as in equation 6, with or without the distortion term (the relative importance of which is proportional to the user-controlled parameter λ in that equation). The computation is efficient since no graph adaptation steps are necessary. This graph comparison gives a similarity value for each model graph stored in the gallery. The graph with the best similarity value can be taken as identifying the object or person recognized. A confidence measure can be derived from the distribution of similarity values, by using, for example, a threshold on the distance between the best match and the second best or on the mean of all other matches.
Additional Implementations (Embodiments) and Operations
1. Variants of Gabor-Based Wavelets
Instead of wavelets of the form of equation 1, other types of wavelets (for instance, Mallat-wavelets) can be used. The wavelet transform can be implemented directly as a convolution, as in equation 2, or with the help of a Fast Fourier Transform. The wavelet transform can be speeded by using a pyramid-type scheme (according to which the lower frequency levels are computed by first low-pass filtering and then down-sampling the image).
2. Determination of Localization, Orientation and Size of Object
After proper identification of the image graph, its localization, orientation, and size determine those quantities for the object found in the image.
3. Determination of Pose of Object
Several bunch graphs that are constructed for different poses of the object can be used side-by-side. During the matching process, each bunch graph is matched individually to the image, and the one with the best similarity is selected along with the image graph it created. The identity of the best-matching bunch graph determines the pose. For graph structures adapted to different head poses, see FIG. 3.
4. Automatic Extraction of Standardized Portraits from Images
If images are presented which each contain one or several instances of the object class(es) represented in bunch graphs, although in varying orientation, size, and pose, bunch graph matching can be used to determine those instances and parameters. Consequently, windows of appropriate location, size, and orientation can be cut out from the image and can be resized to a normal format. Instances of different pose can be labeled and sorted according to pose. It is thus possible to automatically create large collections of standardized portraits (for instance, of persons taken from (group-) photographs, of animals of a particular species or kind, of vehicles of the same type but different make or individuality, etc.)
5. Automatic Incorporation of New Image Graphs into a Bunch Graph
Starting from an already existing bunch graph, automatically created image graphs can be incorporated in the bunch graph to enlarge its domain of applicability.
6. Statistical Optimization of Bunch Graphs
Statistical methods can be used to select entries in the bunch graph. An important criterion could be recognition success and (small) number of entries in the bunch graph. Within the bunch of jets attached at a bunch graph node, clustering methods can be used to find subsets of similar jets. These can then be replaced by just one representative (a statistical mean or a jet near the center of a cluster).
7. Preparation of a Matrix of Pair-Wise Similarities between Objects
Given a gallery of image graphs, a matrix of pair-wise similarities can be prepared, using the graph similarity of equation 6, with or without the distortion term. This matrix can be used and evaluated for several purposes. Clusters of graphs with large mutual similarity can be determined. These clusters can be used to identify sub-types of the object class that is being examined. The clusters can be used to create new bunch graphs to cover the sub-types or distinctions thus identified.
8. Determination of Attributes
During bunch graph matching, as described previously, each node of the final image graph is identified with the best-matching jet in the corresponding node of the bunch graph. If an attribute label (for example, gender of a person, or the presence of a beard or glasses or type of hair-style, or a particular breed of a given animal species, or a particular make of a vehicle type) is attached to the identified jet, that attribute can be attached to the node of the image graph. The resulting distribution of attribute labels on the nodes of the image graph can be evaluated in various ways. A classification of the object in the image in terms of type (e.g., gender, age or facial expression of person, lineage of animal, make of car) can be achieved by letting the attribute labels vote, a clear majority determining the type, or with the help of Bayesian estimation methods. By a similar procedure, the type of an object part (hair-style or beard of person, expression of mouth or eyes, type of attachment on vehicle or machine etc.) can be determined with the help of attributes that are attached to individual nodes or to groups of nodes of models in the bunch graph.
9. Use of Attributes for More Efficient Gallery Search
The bunch graph method can be used to automatically classify and label all image graphs in a gallery according to a number of attributes (e.g., age, gender, subspecies, peculiar features like scar or birth mark). During a recognition task, these attributes can be exploited to make the search more efficient (e.g., by concentrating the search on only those gallery graphs with the right attributes) or more reliable (by using the attributes as additional support for a particular identification).
10. Improving the Efficiency of Object Recognition
Using a pre-compiled matrix of similarities within the gallery, search methods can be devised that are more efficient than exhaustive search of all gallery image graphs.
11. Automatic Preparation of a Composite Image
After a bunch graph is matched to an image, the information as to which jet of the bunch graph was most similar to the image jet is available for each node of the image graph. This information can be used to construct a "composite image" that is based entirely on data derived from the sample images that went into the bunch graph (or from the jets in the bunch graph). A simple way to construct the composite is as follows. Assume all sample images of the bunch graph have the same format (same width and height, measured in pixels). Subdivide this region into tiles such that each tile contains exactly one node of the bunch graph. Assume that for node n the jet of sample image m has been selected during the bunch graph match. Then, cut tile n out of image m. Repeat this for all nodes of the bunch graph and form a single image out of all these tiles. A small amount of smoothing will suppress discontinuities at the tile borders. The resulting image is the composite image. (An alternative method could reconstruct an image out of the jet data in the bunch graph.) Experiments have shown that for reasonably rich bunch graphs, composite images come very close to the original images.
12. Identification of Unusual Object Features
By comparing a composite image with the original image (for instance, by subtracting corresponding pixel values from each other) unusual features can be detected, that is, features that were not contained in the sample images used to prepare the bunch graph (for instance, abnormalities of body shape for different individuals of an animal species).
Referring now to the drawings, FIG. 1 shows a data format for image representation. An image 1 is selected to be analyzed. The image 1 is in the form of a pixel array (typically 512�512 or 128�128 pixels). Each pixel is a fixed point represented as a number, typically in the range 0-256. Pixels are indexed by their horizontal (x-) and vertical (y-) position. A general image may contain one or several pictured objects (for instance, human faces).
One of the kernels 2 a described in equation 1 is also shown. The kernel is centered on the middle of the square. The value zero is represented by the average gray, whereas positive values are lighter and negative values are darker. This kernel 2 a is of low frequency-(short k-vector), and the k-vector points at 2 o'clock. Another kernel 2 b has a higher frequency and a k-vector pointing to 4:30 o'clock.
The image 1 convolved with the kernel 2 a is shown as 3 a. The imaginary component (that is, the sine component) is shown, the oscillatory term in equation 1 being exp(ikj•x)=cos(kj•x)+i sin(kj•x), as well as the imaginary part of the convolution 3 b of the image 1 with the kernel 2 b. The magnitude 4 a, aj(x) of the convolution of the image 1 with the kernel 2 a is used in the similarity functions equations 4 and 5. The magnitude 4 b of the convolution of the image 1 with the kernel 2 b is also shown. The jet J(x) at the pixel position x is the vector of the convolutions of the image 1 with all the different kernels, taken at position (x), that is, it is the set J(x)={Jj(x),j=1, . . . 40}. The jet is shown in FIG. 1 in schematic representation 5. The resulting image or model graph 6 is composed of a grid and of labels. The grid is a set of nodes and a set of edges (pairs of neighboring nodes). There are two kinds of labels. Edge labels are distance vectors between node positions (measured in pixels in the x- and y-directions). Node labels are jets that are extracted from an image by positioning the grid over the image and extracting the jets computed at the node positions.
FIG. 2 shows graphs as placed over objects. An image 21 a is selected here to derive a model graph. Another image 21 b is shown in which an image graph has been formed by graph matching with the graph of the model graph derived from the image 21 a. The model graph 22 is formed by placing a regular grid over the object and extracting jets at the node points of the grid. The node points 23 of the grid are shown, as are the edges 24 of the grid. An image graph 25 is formed in image 21 b. It is the graph that best matches the model graph shown for image 21 a, formed by the matching procedure described previously.
FIG. 3 shows graphs as placed over objects for an irregular, object type-adapted graph structure. A frontal image with image graph 31 is selected. The qualitative image graph structure is adapted to the task of finding a frontal face in an image, a large proportion of the number of nodes lying on the outline and relatively few nodes in the face region. The actual image graph (that is, the precise node positions) has been created by matching to a bunch graph as described previously. Another frontal image with image graph 32 is shown. The qualitative image graph structure is adapted to the task of face recognition, a large proportion of the number of nodes lying over facial features (eyes, nose, mouth) that are characteristic for a person and are not likely to change (as does the outline with changing hairstyle). An image of a person in half-profile 33 is shown. The graph structure is adapted for face finding. An image of a person in half-profile 34 is also shown. The graph structure is adapted for recognition. Image graph nodes 35 and image graph edges 36 are shown for all the graph structures.
FIG. 4 shows a bunch graph and an image graph. Part of an image 41 containing a face is selected. An image graph is placed over the image, utilizing bunch graph matching (only the nodes 42 are shown, not the edges). The image shown has been cut out of a larger image, the position of the frame being centered on the image graph. The qualitative structure of the image graph is a rectangular grid in this case. A schematic depiction of the image graph 43 is shown in its relation to the bunch graph 44. The bunch graph 44 is a collection of model graphs (typically 50-100) derived from images of the type of object and pose of concern (frontal faces in this example). Individual model graphs 45 are shown in the bunch graph which are derived from sample images. All model graphs have the same qualitative graph structure and identically-numbered nodes. The nodes carrying the same number in different model graphs refer to the same qualitative part of the sample objects (for instance, left eye of a face). For each node in a model graph a jet is stored that was derived from the node position in the corresponding sample image. The stored distance vectors for a given pair of nodes are identical for all model graphs, and are computed as an average, equation 7. Pointers 46 are directed from the nodes of the image graph to the best-matching nodes of the bunch graph. When an image node is repositioned during the bunch graph matching process, it extracts the jet from the image that is centered at the new position, compares it to all the jets attached to the corresponding nodes in the bunch graph, and selects the one with the highest similarity. In this way, the bunch graph can optimize its total similarity, equation 8, making use of all possible combinations of stored jets (restricted only by the requirement that bunch graph jets have to be selected from the correct node). Attributes 47 are attached to the model graphs (for instance, gender, age and happiness/sadness of the person represented by the model graph).
While the invention has been described by way of example and in terms of preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. To the contrary, it is intended to cover various modifications and similar arrangements. Therefore, the claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.
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text:
The Business Woman blog is about what I'm learning day to day about growing my business. It allows me to share the growing pains of road blocks and open doors I come across while trying to go to the next level with my event Planning Business.
Wednesday, November 2, 2011
Is there a Difference in South & East Coast Business? What are you afraid of?
You all know how I love to visit my home town Washington DC. Each time I go, I grow (lol). It's always a pleasure to visit old friends, families as well as try to do business by creating a new business contact.
I had the opportunity to return home this time for my class reunion.. They are tearing our high school down to rebuild it. It's getting an extreme makeover sort to speak. While there I was able to have a meeting with a friend who has her own business.
She held an empower event that I was able to attend to network with other business-like mind people. The cost for this event was $50. The room was packed with suited as well jeaned Men & Women. Everyone exchanging business cards talking and getting to know what each other does. There were a couple of speakers a few vendors tables set up for visual services and products the networkers were able to explore. There was finger food and drinks offer [cash bar] and everyone was able to stand up and present a 3 minute elevator pitch (lol). There were about 250 people; per my friend and this was a low count compared to last month. This event is held once a month but on this night it was a special event because of the speakers.
Some of the conversation was business; politics; football and even fashion. Some greeted each other like old friends and other like they have just met for the first time.
After the event was over, I decided to stick around to ask a few questions about the event; the purpose and how long has she been doing this type of networking event.
We sat down and had a wonderful conversation. It was powerful funny and very enlighten. The questions rolled off my tongue like a new student and she answer each one with honesty and joy of her accomplishments. Then I realized from her that there is a difference in doing business in the south verse the east coast. But why since we all are trying to achieved the same thing? It appeared to me we all are trying to become successful; Aren't we all seeking independence in what we do; Are we not trying to remove ourselves from corporate America to start our own business? Is there a difference in our passions? Hmmm? Is there?
What is holding business people back from growing their business? Is it lack of Education; funding; support? Are we expecting something for nothing? Should we expect our friends to cheers us on or do we feel like every man/woman for themselves?
I learned a lot from my friend and details will be shared later. But I believe I'm on the right track and I'm doing my best in moving forward. I know I still have a lot to learn and some growing to do when it comes to business, but I have decided I want to be like those who are growing their business and not sitting around waiting on something to happen. I know we all have our circle of friends that we do business with, but what would honestly happen if we step away from the circle for a few moments to add create a new circle of business contacts outside the rim of what we have. Will we learn something new? Will we gather new information? Will our business increase? When it all boils down, what do we have to loose if we decide to try something new outside the rim of what we normally do with the people we normally do business with? What are we afraid of?
Let me hear from you, have you done business with someone else other then friends outside your own back yard? What was the results..
Until next time- The Business Women!
Posted by Teri Lewis at 7:34 AM
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