How do we distinguish between Image Recognition,Detection and Segmentation?
It can be a little confusing at times, and the terms are not completely independent.

Detection:

In detection, you are simply detection the presence of something. For example, you might design an algorithm to detect beach-balls from pictures of a beach. You would feed an image into your algorithm, and it will spit out an answer 'yes', if there was a beach ball, and 'no' if there was no beach ball. This is very simply what it means to 'detect' something. 

Segmentation:

Segmentation is a method - a technique - an algorithm. So for example, if someone says "How did you design your beach-ball detection algorithm?", you might say "I used a segmentation algorithm". Or you might say, "I used a template-matching algorithm". Or you might say "I used a scale and shift-invariant matching score algorithm", etc. Those are all methods of doing something, and segmentation is just a method. 

In particular, segmentation simply means a thresholding of your image into various areas. In a simple case, say you want to separate background from foreground. Then, you segment the image into two regions: A region that corresponds to the background, and a region that corresponds to the foreground. 

Image Recognition:

Lastly, there is image recognition, which is simply, a very broad goal.  For example, you might say, "I achieved image recognition for beach scenes, by detecting beach balls using the method of segmentation, detecting water using the method of k-means, and detecting bikinis using the method of template matching. 



Of course, those are just examples, but broadly speaking, this is how you should parse the different words and terminologies you come across in this field. Hope that helped!