Robust Video Background Identification by Dominant Rigid Motion Estimation

Person Re-identification Based on Color Histogram and Spatial Configuration of Dominant Color Regions

There is a requirement to determine whether a given person of interest has already been observed over a network of cameras in video surveillance systems. A human appearance obtained in one camera is usually different from the ones obtained in another camera due to difference in illumination, pose and viewpoint, camera parameters. Being related to appearance-based approaches for person re-identification, we propose a novel method based on the dominant color histogram and spatial configuration of dominant color regions on human body parts. Dominant color histogram and spatial configuration of th…

re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification

Conventional approaches to object instance re-identification rely on matching appearances of the target objects among a set of frames. However, learning appearances of the objects alone might fail when there are multiple objects with similar appearance or multiple instances of same object class present in the scene. This paper proposes that partial observations of the background can be utilized to aid in the object re-identification task for a rigid scene, especially a rigid environment with a lot of reoccurring identical models of objects. Using an extension to the Mask R-CNN architecture, we…

Multiregion Bilinear Convolutional Neural Networks for Person Re-Identification

In this work we propose a new architecture for person re-identification. As the task of re-identification is inherently associated with embedding learning and non-rigid appearance description, our architecture is based on the deep bilinear convolutional network (Bilinear-CNN) that has been proposed recently for fine-grained classification of highly non-rigid objects. While the last stages of the original Bilinear-CNN architecture completely removes the geometric information from consideration by performing orderless pooling, we observe that a better embedding can be learned by performing bilin…

Deep Constrained Dominant Sets for Person Re-identification

In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome …

基于生物力学的非刚性医学图像配准及其在线性与非线性弹性下的逆向材料属性估计

Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity

本文利用PINNs实现受生物力学约束的非刚性医学图像配准及软组织材料属性逆向估计，将线性弹性推广至非线性，通过前列腺MR图像实验验证了方法，还可通过鞍点求解联合优化的逆问题。

Constrained Dominant sets and Its applications in computer vision

In this thesis, we present new schemes which leverage a constrained clustering method to solve several computer vision tasks ranging from image retrieval, image segmentation and co-segmentation, to person re-identification. In the last decades clustering methods have played a vital role in computer vision applications; herein, we focus on the extension, reformulation, and integration of a well-known graph and game theoretic clustering method known as Dominant Sets. Thus, we have demonstrated the validity of the proposed methods with extensive experiments which are conducted on several benchmar…