Texture Analysis And Its Applications For Outdoor Scene Image

Texture analysis is always an important issue in pattern recognition and computer vision, and also has found wide application in scientific research and industry fields. Since the 1970's, texture analysis has been a major research area in this field. Every year, a large number of relevant research papers were published in important journals and conferences. Numbers of renowned research institutions are also engaged in this area.However, due to the complexity of human cognitive mechanisms and the richness of the actual image texture, it is still a great challenge for the existing approach. Although researchers have made lots of valuable research results, there still existing many puzzles to be resolved.This dissertation focuses on the key point in the field of texture analysis and its applications. Widely used texture analysis methods and its experiment results are reviewed, compared and analyzed. Its applications in nature scene segmentation and pavement crack detection were also explored in this dissertation. The main research can be summarized as follows.1. In order to eliminate shadows in natural scenes, an approach based on transition region detection and Retinex illumination model was proposed. The range of gray value of shadows was first extracted by Gaussian distribution. By using a multi-criterion method, shadows were effectively detected. Then, the existing transition regions detection methods were compared and analyzed; and an improved local complexity algorithm was proposed for accurate transition region extraction. Based on the improved algorithm, a Retinex based shadow elimination approach was proposed. Natural scene image was segmented to three regions, the shadows, the transition region and the sunshine region. The Retinex method was adopted in shadow and sunshine regions separately. After bilinear interpolation was used for fill up the transition region, a shadow-free image was finally obtained. The proposed method achieved good performance in shadow removal, and halo artifacts generated by Retinex algorithm in high dynamic range area was avoided. Experimental results on real scenes demonstrate the effectiveness of this algorithm.2. To solve the scale selection problem in the texture feature extraction, features' characters at different scales and its effect in classification accuracy were first analyzed. After digging out the reasons of the uncertainty in multi-class texture classification, an unsupervised texture classification approach which based on scale-invariant features was proposed. By extracting a variety of different scales of texture features, the trends were obtained and were adopted as distinctive features for texture classification. Experimental results on standard texture database show that the proposed method achieved higher accuracy rate, and most important is the selection of the best scale was avoided.3. For the selection of the similarity measure in unsupervised texture classification, a variety of distance measurement were compared and analyzed. A novel distance measurement is proposed and then applied to nature scene segmentation. Quantitative assessment method was introduced in the experimental process. Experiment results show that the proposed distance measurement has good stability and always achieved good classification results in different nature scenes. For the real-time segmentation, a fast approach based on HSV color space was proposed. The proposed approach was implemented with high efficiency and good performance.4. In dealing with the complex texture and the uneven illumination background, a pavement crack detection approach based on orientation-selection texture analysis method and SVM was proposed. In the proposed approach, texture analysis based on GLCM and rotation-, translation-invariant shape descriptors were used against strong texture and uneven illumination background. By training SVM with kernel functions, the crack detection approach achieved high accurate rate in different pavement surface. Experiments on real pavement images confirmed the adaptability and accuracy of the proposed approach, even in strong texture and uneven illumination background.For real-time pavement crack detection and classification, a novel pre-detection algorithm based on LBP operator is proposed. Distinctive characteristics of LBP, such as fast and insensitive to illumination, were used in our approach. Complex texture and noisy background were also considered. Patterns of LBP were regrouped in order to capture different level edge response (strong edges and weak edges). Experiments on real road surface images confirmed that our approach achieved higher accuracy rate, even in strong texture background. It is more important that the approach we proposed is fast enough to meet the demand of real time detection.