We propose a new iterative greedy algorithm to reconstruct sparse signals in Compressed Sensing. The algorithm, called Conjugate Gradient Hard Thresholding Pursuit (CGHTP), is a simple combination of Hard Thresholding Pursuit (HTP) and Conjugate Gradient Iterative Hard Thresholding (CGIHT). The conjugate gradient method with a fast asymptotic convergence rate is integrated into the HTP scheme that only uses simple line search, which accelerates the convergence of the iterative process. Moreover, an adaptive step size selection strategy, which constantly shrinks the step size until a convergence criterion is met, ensures that the algorithm has a stable and fast convergence rate without choosing step size. Finally, experiments on both Gaussian-signal and real-world images demonstrate the advantages of the proposed algorithm in convergence rate and reconstruction performance.

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

Color histogram-based trackers have obtained excellent performance against many challenging situations. However, since the appearance of color is sensitive to illumination, they tend to achieve lower accuracy when illumination is severely variant throughout a sequence. To overcome this limitation, we propose a novel hyperline clustering based discriminant model, an illumination invariant model that is able to distinguish the object from its surrounding background. Furthermore, we exploit this model and propose an anchor based scale estimation to cope with shape deformation and scale variation. Numerous experiments on recent online tracking benchmark datasets demonstrate that our approach achieve favorable performance compared with several state-of-the-art tracking algorithms. In particular, our approach achieves higher accuracy than comparative methods in the illumination variant and shape deformation challenging situations.

Person reidentification (re-id) has gained significant progress and aroused great interest in computer vision. However, due to the effect of weak illumination and poor alignment, person re-id is still a challenging task. Many previous works focus on either illumination enhancement methods or pose estimation. However, those methods are difficult to apply in real-world scenarios, which usually contain various interference factors. To improve the performance of re-id, we propose an Illumination-Invariant and Pose-Aligned Network (IIPA-Net). The illumination change is handled by a retinex decompose network, and the pose variation problem is solved by a local feature matching method. Based on the multimodal nature of a person, we propose a part attention module to optimize the global feature. Finally, a data-driven training strategy is proposed to train the proposed architecture effectively. Experiments show that the proposed framework outperforms other state-of-the-art approaches on both normal- and low-light datasets.