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Andrea Cavallaro
Researcher at Queen Mary University of London
Publications - 366
Citations - 10738
Andrea Cavallaro is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Video tracking & Object detection. The author has an hindex of 46, co-authored 345 publications receiving 8945 citations. Previous affiliations of Andrea Cavallaro include Tel Aviv University & Dalhousie University.
Papers
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Journal ArticleDOI
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
TL;DR: This paper provides a comprehensive analysis of facial representations by uncovering their advantages and limitations, and elaborate on the type of information they encode and how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias.
Journal ArticleDOI
Sensor capability and atmospheric correction in ocean colour remote sensing
TL;DR: An overview of the state of the art in atmospheric correction algorithms is provided, recent advances are highlighted and the possible potential for hyperspectral data to address the current challenges is discussed.
Journal ArticleDOI
Cast shadow segmentation using invariant color features
TL;DR: A new cast shadow segmentation algorithm is proposed that exploits spectral and geometrical properties of shadows in a scene to perform this task and is robust and efficient in detecting shadows for a large class of scenes.
Proceedings ArticleDOI
Omni-Scale Feature Learning for Person Re-Identification
TL;DR: Zhou et al. as mentioned in this paper designed a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale, and a novel unified aggregation gate was introduced to dynamically fuse multi-scale features with input-dependent channel-wise weights.
Posted Content
Omni-Scale Feature Learning for Person Re-Identification
TL;DR: A novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning by designing a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale.