Kechen Song

Research and Perspective on Local Binary Pattern

In view of the theoretical and practical value of local binary pattern (LBP), the various LBP methods in texture analysis and classification, face analysis and recognition, and other detection applications are reviewed. Firstly, the principle of LBP method is briefly discussed, which mainly analyses the threshold operation, the uniform pattern and rotation invariant pattern in LBP method. Secondly, the texture analysis and classification of the LBP method, face analysis and recognition of the LBP method and other detection applications of the LBP method are particular combed and commented. Finally, the existing important problems of the LBP method are analyzed and the future for the LBP method is pointed out.

Adjacent evaluation of local binary pattern for texture classification

An adjacent evaluation window is constructed to modify threshold scheme of LBP.The adjacent evaluation derives two new descriptors: AECLBP and AELTP.The adjacent evaluation plays an important role in solving sensitivity to noise.The proposed approaches are experimented on five texture databases. This paper presents a novel, simple, yet robust texture descriptor against noise named the adjacent evaluation local binary patterns (AELBP) for texture classification. In the proposed approach, an adjacent evaluation window is constructed to modify the threshold scheme of LBP. The neighbors of the neighborhood center gc are set as the evaluation center ap. Surrounding the evaluation center, we set up an evaluation window and calculate the value of ap, and then extract the local binary codes by comparing the value of ap with the value of the neighborhood center gc. Moreover, this adjacent evaluation method is generalized and can be integrated with the existing LBP variants such as completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features against noise for texture classification. The proposed approaches are compared with the state-of-the-art approaches on Outex and CUReT databases, and evaluated on three challenging databases (i.e. UIUC, UMD and ALOT databases) for texture classification. Experimental results demonstrate that the proposed approaches present a solid power of texture classification under illumination and rotation variations, significant viewpoint changes, and significant large-scale challenging conditions. Furthermore, the proposed approaches are more robust against noise and consistently outperform all the basic approaches in comparison.

Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model

Accurate detection of surface defect is an indispensable section in steel surface inspection system. In order to detect the micro surface defect of silicon steel strip, a new detection method based on saliency convex active contour model is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background for the purpose of highlighting the potential objects. The extracted saliency map is then exploited as a feature, which is fused into a convex energy minimization function of local-based active contour. Meanwhile, a numerical minimization algorithm is introduced to separate the micro surface defects from cluttered background. Experimental results demonstrate that the proposed method presents good performance for detecting micro surface defects including spot-defect and steel-pit-defect. Even in the cluttered background, the proposed method detects almost all of the microdefects without any false objects.

Noise robust image matching using adjacent evaluation census transform and wavelet edge joint bilateral filter in stereo vision

The adjacent evaluation census is proposed to improve the robustness of census.Two different and complementary metrics are extracted to reduce ambiguity.The random walk is integrated into aggregation and optimization.WEJBF is employed to eliminate error regions of the original disparity map. Automation application systems based on stereo vision require robust image matching methods to achieve available depth image information. This paper presents a novel noise robust stereo matching using adjacent evaluation census transform and wavelet edge joint bilateral filter. The adjacent evaluation census is firstly proposed to improve the robustness against noise of the census transform. Meanwhile, two different and complementary types of metrics are extracted (the adjacent evaluation census mean and the adjacent evaluation census weighted difference). Moreover, the weighted template is composed of four different directions. Then, to improve the robustness of cost aggregation and disparity optimization, the random walk is integrated into the proposed stereo matching method. Additionally, a disparity map post-processing method named wavelet edge joint bilateral filter is employed to eliminate error regions. An obtained wavelet-based edge image is considered as an important weighted coefficient to guide the post-processing. Experimental results demonstrate that the proposed method presents the best performance of the robustness against noise on the Middlebury dataset. Even in the toughest situation with additive Gaussian noise, our method can still achieve the moderate disparity map. In addition, the wider applicability of the proposed method is demonstrated on the KITTI (i.e., Karlsruhe Institute of Technology (KIT) and Toyota Technological Institute at Chicago (TTI-C)) dataset and some typical real-world sequences.

Intelligent assessment of subsurface cracks in optical glass generated in mechanical grinding process

Abstract Grinding process of optical glass has been reported to be related with the creation of subsurface cracks. However, for the time being, most measurement methods have been depended on human operations. In this paper, an intelligent assessment method based on image processing technique is proposed. Grinding trials proved that, the proposed method can accurately (with the biggest relative error of 3.53%) and quickly (nearly 1.6 seconds per micrographs) recognize and measure the subsurface crack depths. More importantly, the proposed method has good robustness to different-sized images. Besides, the method does not require any input parameters or any adjustment of thresholds, therefore the method does not require any prior knowledge of either mechanical grinding process or brittle material behaviors relating with subsurface cracks. Based on above, the proposed method is expected to be meaningful to both metrology equipment companies and optical glass manufacturers.

Neighborhood Estimated Local Binary Patterns for Texture Classification

A novel texture classification approach based on neighborhood estimated local binary patterns (NELBP) is proposed. In the proposed approach, the local surrounding values of neighborhood estimated are introduced to operate binary patterns. Moreover, two different and complementary descriptors (average-based descriptor and differences-based descriptor) are extracted from local patches. Contrast experiments on Outex database and CUReT database demonstrate that the proposed NELBP is more robust to Gaussian noise than the conventional LBP for texture classification. In addition, the results also show that the combined complementary descriptor playes an important role in texture classification.

Optical challenging feature inline measurement system based on photometric stereo and HON feature extractor

Inline, also known as on-line, measurement is one of the most important measurement techniques in automotive manufacturing industry, especially for body shop. Nowadays, optical measurement systems, which is most commonly used for inline measurement, are developing rapidly. However, some of dimensional features in body shop such as spot welding points, stud welding, high reflective surfaces and black painted materials, are still challenging for traditional optical inline systems to measure. Aiming at optical challenging dimensional features, a measuring system with cameras and fixed illuminations is developed to achieve robust and accurate measurement. Photometric stereo technique is used to obtain the surface normal map of the feature, so the algorithm can adapt to various reflections of the material. A Histogram of Oriented Normals (HON) extractor is proposed to extract the feature vector, which is small enough to fit a neural network, from normal map. After building a database with 1000 normal map and corresponding feature position, an artificial neural network is trained for localization the feature from 2D image. Combining 2D information from two different cameras, the 3D coordinate will be available with triangulation. Comparing with traditional HOG bench mark extractor, proposed system showed significant advantage on optical challenging materials in the experiment with welded stud sample. Comparing with off-line measurement systems, proposed system takes much less time, which gives the potential for optical challenging features inline measurement.

3D inspection technology combining passive stereo matching and active structured light for steel plate surface sample

Three-dimensional (3D) inspection technology is a new important research hotspot in the field of steel surface defect inspection. However, current 3D information acquisition methods (i.e., the passive stereo vision method and active structured light method) still suffer from several issues. In order to solve these issues, a three-dimensional inspection system is designed in this paper. In this system, the passive stereo vision method and active structured light method are combined to obtain the surface sample height information of high temperature steel plate. Furthermore, the Hexagonal grid Census (Hg_Census) transform is proposed to improve the robustness of census transform stereo matching. Moreover, the passive stereo disparity map is adopted as a constraint condition to realise phase matching using wrapped phase. In addition, the local phase matching and sub-pixel disparity refinement are proposed to obtain high measuring accuracy. Three high temperature steel plate surface samples are picked to verify the effectiveness of the proposed method. The actual experimental results present the average error is less than 1 mm.

Texture Segmentation of Natural Images Based on Active Contour Model

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.