Alireza Rahimpour

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Classical dictionary learning algorithms that rely on a single source of information have been successfully used for classification tasks. Additionally, the exploitation of multiple sources has shown to be advantageous in challenging real-world situations. We propose a new framework to exploit robust modality fusion in classification in order to achieve(More)
Distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wireless distributed object recognition, the bandwidth is limited and it is desirable to avoid transmitting redundant visual features from(More)
ii This dissertation is dedicated to my loving wife Ruoning and my cute son Isaac Li (李溪根). iii Acknowledgements I would like to thank all the individuals who have inspired, encouraged, and advised me in the preparation of this dissertation. First and foremost, I would like to thank my advisor, Dr. Hairong Qi. Her willingness to support my work and her(More)
Heating, Ventilating and Air Conditioning units (HVAC) are a major electrical energy consumer in buildings. Monitoring of the operation and energy consumption of HVAC would increase the awareness of building owners and maintenance service providers of the condition and quality of performance of these units, enabling conditioned-based maintenance which would(More)
Learning binary representation is essential to large-scale computer vision tasks. Most existing algorithms require a separate quantiza-tion constraint to learn effective hashing functions. In this work, we present Direct Binary Embedding (DBE), a simple yet very effective algorithm to learn binary representation in an end-to-end fashion. By appending an(More)
Distributed surveillance systems have become popular in recent years due to security concerns. However, transmitting high dimensional data in bandwidth-limited distributed systems becomes a major challenge. In this paper, we address this issue by proposing a novel probabilistic algorithm based on the divergence between the probability distributions of the(More)
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