A new feature-based wavelet completed local ternary pattern (FEAT-WCLTP) for texture and medical image classification

Nowadays, texture image descriptors are used in many important real-life applications. The use of texture analysis in texture and medical image classification has attracted considerable attention. Local Binary Patterns (LBP) is one of the simplest yet eff ective texture descriptors. But it has some limitations that may affect its accuracy. Hence, different variants of LBP were proposed to overcome LBP’s drawbacks and enhance its classification accuracy. Completed local ternary pattern (CLTP) is one of the significant LBP variants. However, CLTP suffers from two main limitations: the selection of the threshold value is manually based and the high dimensionality which is negatively affected the descriptor performance and leads to high computations. This research aims to improve the classification accuracy of CLTP and overcome the computational limitation by proposing new descriptors inspired by CLTP. Therefore, this research introduces two contributions: The first one is a proposed new descriptor that integrates redundant discrete wavelet transform (RDWT) with the original CLTP, namely, wavelet completed local ternary pattern (WCLTP). Extracting CLTP in wavelet transform will help increase the classification accuracy due to the shift invariant property of RDWT. Firstly, the image is decomposed into four sub-bands (LL, LH, HL, HH) by using RDWT. Then, CLTP is extracted based on the LL wavelet coefficients. The latter one is the reduction in the dimensionality of WCLTP by reducing its size and a proposed new texture descriptor, namely, feature-based wavelet completed local ternary pattern (FeatWCLTP). The proposed Feat-WCLTP can enhance CLTP’s performance and reduce high dimensionality. The mean and variance of the values of the selected texture pattern are used instead of the normal magnitude texture descriptor of CLTP. The performance of the proposed WCLTP and Feat-WCLTP was evaluated using four textures (i.e. OuTex, CUReT, UIUC and Kylberg) and two medical (i.e. 2D HeLa and Breast Cancer) datasets then compared with several well-known LBP variants. The proposed WCLTP outperformed the previous descriptors and achieved the highest classification accuracy in all experiments. The results for the texture dataset are 99.35% in OuTex, 96.57% in CUReT, 94.80% in UIUC and 99.88% in the Kylberg dataset. The results for the medical dataset are 84.19% in the 2D HeLa dataset and 92.14% in the Breast Cancer dataset. The proposed Feat-WCLTP not only overcomes the dimensionality problem but also considerably improves the classification accuracy. The results for Feat-WCLTP for texture dataset are 99.66% in OuTex, 96.89% in CUReT, 95.23% in UIUC and 99.92% in the Kylberg dataset. The results for the medical dataset are 84.42% in the 2D HeLa dataset and 89.12% in the Breast Cancer dataset. Moreover, the proposed Feat-WCLTP reduces the size of the feature vector for texture pattern (1,8) to 160 bins instead of 400 bins in WCLTP. The proposed WCLTP and Feat-WCLTP have better classification accuracy and dimensionality than the original CLTP

3D Face Tracking Using Stereo Cameras with Whole Body View

All visual tracking tasks associated with people tracking are in a great demand for modern applications dedicated to make human life easier and safer. In this thesis, a special case of people tracking - 3D face tracking in whole body view video is explored. Whole body view video means that the tracked face typically occupies not more than 5-10% of the frame area. Currently there is no reliable tracker that can track a face in long-term whole body view videos with luminance cameras in the 3D space. I followed a non-classical approach to designing a 3D tracker: first a 2D face tracking algorithm was developed in one view and then extended into stereo tracking. I recorded and annotated my own extensive dataset specifically for 2D face tracking in whole body view video and evaluated 17 state of the art 2D tracking algorithms. Based on the TLD tracker, I developed a face adapted median flow tracker that shows superior results compared to state of the art generic trackers. I explored different ways of extending 2D tracking into 3D and developed a method of using the epipolar constraint to check consistency of 3D tracking results. This method allows to detect tracking failures early and improves overall 3D tracking accuracy. I demonstrated how a Kinect based method can be compared to visual tracking methods and compared four different visual tracking methods running on low resolution fisheye stereo video and the Kinect face tracking application. My main contributions are: - I developed a face adaptation of generic trackers that improves tracking performance in long-term whole body view videos. - I designed a method of using the epipolar constraint to check consistency of 3D tracking results