Abstract
Local Binary Pattern (LBP) 
Introduction
Feature extraction plays an important role in pattern analysis. Many object surfaces show some texture characteristics, such as coarse, confusion, etc. At the time, the classification bases will be based on it rather than the uniformity of intensity, just like wood defects, the types and distribution of which vary. In the researches, it's impossible to extract all the texture characteristics of defects and the system suffers from the variations in the materials, causing selecting typical features for classification is difficult.
However, woods are the indispensable important resources and much in demand. However, the coverage of forests reduces substantially. In view of it, the rational uses of woods are the first measures be taken. Currently, how to improve the utilization rate of woods effectively is on a hot. In the production, the defects need to be removed. However, in most countries, this work is mainly finished by workers, costing many resources. Provided we can achieve accurate positioning and handling to the defects by the machines, we can improve the utilization rate of boards and avoid the wastes.
At present, some researchers have begun to study the solution measures. Finn Matti Niskanen [1] and their group adopt SOM to study color and texture based wood defect detection problems. The recognition mainly depends on color and texture characteristics. However, the system is vulnerable to thick dots on wood surfaces and the errors are relatively higher. In addition, German P. Meinlschmidt [2] and their group adopt infrared thermal imaging technology to study wood materials based defect detection problems. In their experiments, the materials under inspection are heated with radiators and the bases are the temperature above defects decreases slower than other regions. They complete defect detection online, however, the system costs more runtime and can only for special purposes, the practical value of which is relatively lower.
In fact, many researches indicate no one feature extraction technology is absolutely better than another, appropriate combination of them can obtain better recognition results. This paper adopts DTCWT and LBP to extract the features named the united statistical features by LBP and DTCWT (USF-LDT). Finally, we received a better identification effect and the system had better robust, raising the accuracy effectively.
And the diagram of SVM based classifier is presented in Figure 1 
Wood representation by USF-LDT
Kingsbury [5, 7, 8] proposed the DTCWT, which is composed by two biorthogonal filters and has better direction selective. The features based on en energy by DTCWT can be used to capture the wood texture characteristics, making it can effectively portray the global features, but the structure of it could not be described well.
However, LBP [3, 9] provides the spatial structure of the current image and LBP code was invariant to monotonous gray and was a texture descriptor which codified local primitives of the wood texture, such as curved edges, spots, etc. into LBP feature histograms [4] . Next, we will illuminate the different roles of LBP local and DTCWT global features.
Description of LBP texture features
LBP is put forward by Ojala [ 4 ] et al and LBP code determined by n sample points is used. For the fixed pixel centre coordinates ( , ) d d
x y , LBP is defined as a binary contrast between center pixel and the n surrounding pixels. TextureＷ is defined as the united distribution of the gray levels of n pixels: 2,…,P）corresponds the gray value of the n equally spaced pixels on a circle of radius R (R>0) that forms a circularly symmetric set. The coordinates of the n neighbors of the center pixel in the edge of the circle R can be calculated as following:
To achieve invariant with respect to any monotonic transformation, only the signs of the differences were considered: 
Here, we adopt uniform
LBP of values (P, R) equal to (8, 1), i.e. around a circle of radius R were eight adjacent pixels and the mapping type was uniform. In the experiments, the feature set selection is very important. After many cross experiments, we select all the feature sets with pixels 30*30.
In the feature extraction, we divide the wood images into three layers of R,G,B and divide each layer into many small blocks. Next, we extract the 59 dimension features denoted by LBP histograms from each block, marked by LBR, LBG and LBB separately.
Finally, the dimensions of the features are reduced to 1*177, represented by LBF. The LBP texture feature extraction process is shown in Figure3-1. Figure2-1.LBP feature extraction process.
Description of DTCWT features
Kingsbury [5, 6] proposed the DTCWT [7, 8] and it can maintain limited data redundancy. Provided the low-pass filters and highpass filters of wavelet decomposition are respectively defined as 0 ( ) H z , 1 ( ) H z , while the reconstruction filters can be defined as
For input signal ( )
X z , the low-pass and high-pass coefficients can be represented as following:
Hence, the signal ( ) X z can be decom -posed into:
Here, three-level wavelet transform is adopted. In DTCWT feature selection [10, 11, 12] process, the feature set is defined as Figure2-2.DTCWT feature extraction process.
Description of the typical features
Finally, we aggregately obtain 215 dimension eigenvectors. As above explanation, the two sets contain different features for recognition. Considering the typical features based classifier is generally superior to single and has enough error diversity, we combine LBF with DTW_F to improve the system performance. Hence, the SVM model trained has large diversity in error when facing the diversified interferential information. As above-mentioned, DTW_F described the global features, thus better for coarse representation, while LBF captured the details in local areas, thus better for finer representation.
Performance Standards
For the tests, the number of detection produced in the system is defined as N_sum. The number of incision on the real defects is defined as N_labeled. The number of incision to the detected and labeled defects in the identification is defined as N_dlabeled and the number of misjudging defects as normal and misjudging sound woods as defects is defined as DN_sum and SD_sum respectively.
The Missed rates（M1）indicate the proportion of the missed defects to real labeled defects and can be determined by:
The Misjudgment rates （M2） indicate the proportion of the misjudged defects to the total number of detections.
The Accuracy represents the recognition rate and can be expressed as following: Figure 3 -1 illustrates some but common cases, where, the rectangles denote the detections produced by the system. In this paper, we mainly evaluate the performance of the recognition system by M1 and M2. 
SVM classifier with typical features

Choose the training set and test sets
There are many kinds of defects and knots can often be seen and affect the quality and grading of woods. Here, we emphasize particularly on knots and some varieties of knots are shown in Figure 4 
Description recognition algorithm recognition algorithm 1. (Input)
Training set are      , then the pre-labels produced in the process of detection will be equal to zero and the algorithm will denote it automatically. The detailed wood defect recognition process is represented in Figure 4 
Simulation and experiments
Some wood surfaces are relatively clean and the texture distribution is regular, however, in the actual production, most wood surfaces are not so smooth but blurry. Besides, large kinds of defects make the current methods fulfilled are unstable and vulnerable to the external interferences.
Before the experiments, we need obtain the best kernel parameters C and gamma by python and gnuplot. In the experiments, we mainly take WDIT as major experimental basis, of which the sample class distribution may be described in Table 5 -1. The results will be used to evaluate the performance of the algorithm. This chapter mainly contains three main proportions. In order to highlight the superiority of USF-LDT, the sketch diagrams of detection and the distribution curves are given. Experimental tool:OSU_SVM;Experimental environment: Intel(R) Pentium(R) D CPU 2.80 GHz 2.79 GHz 512 M
Comparative test one
Here, we actualize the wood defect recognition method based on LBP with SVM classifier. The detailed test results based on WDIT-1 are recorded and shown in Table 5 -2. The remaining test results of the other wood images were not individually given here. Through the test results, we summarized the problems encountered and the limitations itself to judge whether the proposed method has better robustness. 
Contrast
In this section, we compare the three methods from two aspects: on the one hand, we compare the test results based by the diagrams shown in Figure 5 -1, each column of which represents the results respectively; On the other hand, we will portray the distribution curves of the check items shown in Figure 5 The compared results showed the overall performance of our proposed technology is superior to others and has better robust to the disturbed information with good practical and promotional values.
Comparative test two
Here, we analyzed the performance of the three methods to give prominence to our proposed method. We don't think about the number of R_sum, Q_sum and N_dlabeled but turn to judge whether the methods can detect and carve the defects by the error that is the proportion of the number of the missed defects in detection to the total of the knots. From Table 5 -5, the accuracy of the proposed method reach more than 90%, suggesting the system can follow the manual classification even though there are some errors in the detection and recognition. the results in the log-form on WDIT, where the curve of our proposed method is lower than the others, which demonstrates that it has better detection ability to knots.
Conclusions and Outlooks

Conclusions.
The main purpose of this paper is to present a new wood defect recognition method. Moreover, the results of our proposed method presented here were good or considerably better than the results obtained by the other two methods. The experimental results show our proposed method has better robust to the interferences.
Though this technology has made a better recognition result, it is still in further study. At present, it has a certain distance from being applied to the actual production. However, it has perfect theoretical foundation and technology feasibility. Finally, we will receive a better recognition effect, meeting the strict requirements of the l production to a certain extent.
Outlooks.
Later, we will continue doing the following tasks well ：(1) Improve the recognition algorithm further and effectively heighten the accuracy. And we will study the "defects overlapping" problems；(2) Considering the varieties of knots, convert the original problem into multi-class identification problem. After the recognition algorithm was mature, we will improve the system further and the image scanning and capture technology will be introduced to implement real-time detection.

{'paper_id': '62157535', 'title': 'Locating theWood Defects with Typical Features and SVM', 's2_url': 'https://api.semanticscholar.org/CorpusID:62157535', 'source': 'S2ORC'}