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This paper considers the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or human body images is about the same person, even if the person is not seen before. Traditional methods usually look for a distance (or similarity) measure between images (e.g., by metric learning(More)
In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification applications. First, we model the feature vectors, from the whole corpus, from each image and at each individual patch, in a Bayesian hierarchical framework using mixtures of Gaussians. After such a hierarchical(More)
Wyner-Ziv video coding (WZVC) has gained considerable interests in the research community. In this paper, we present a model to examine the WZVC performance and compare it with conventional motion-compensated prediction (MCP) based video coding. Theoretical results show that although WZVC can achieve as much as 6-dB gain over conventional video coding(More)
The increasing complexity, heterogeneity, and dynamism of emerging pervasive Grid environments and applications has necessitated the development of autonomic self-managing solutions, that are inspired by biological systems and deal with similar challenges of complexity, heterogeneity, and uncertainty. This paper introduces Project AutoMate and describes its(More)
Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by i) developing a novel Long Short-Term Memorized Context Fusion (LSTMCF) Model that captures and(More)
Side estimation plays an important role in Wyner-Ziv video coding. In this paper, we examine the use of two conventional motion search methods to improve side estimation. Unlike motion estimators used in conventional video encoders, side estimators do not have access to the original frame. This difference leads to some interesting observations concerning(More)
This work details the authors' efforts to push the baseline of expression recognition performance on a realistic database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this work. These two happen frequently in real life settings. The approach towards solving this problem involves face detection, followed by(More)
The scale, heterogeneity, and dynamism of emerging distributed and decentralized environments make coordination a significant and challenging problem. In this paper we present Comet, a scalable peer-to-peer content-based coordination space. Comet provides a global virtual shared-space that can be associatively accessed by all peer nodes in the system, and(More)