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With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, Convolutional DLSTM (ConvDLSTM), for crowd scene understanding...
Although Query-by-Example techniques based on Euclidean distance in a multidimensional feature space have proved to be effective for image databases, this approach cannot be effectively applied to video since the number of dimensions would be massive due to the richness and complexity of video data. The above issue has been addressed in two recent solutions, namely Deterministic Quantization (DQ)...
The detection of texts in video images is an important task towards automatic content-based information indexing and retrieval system. In this paper, we propose a texture-based method for text detection in complex video images. Taking advantage of the desirable characteristic of gray-scale invariance of local binary patterns (LBP), we apply a modified LBP operator to extract feature of texts. A polynomial...
In this paper, a novel liver lesion diagnosis approach based on multi-phase enhanced CT images is proposed. Regions of Interest (ROIs) which are drawn by an experienced radiologist are categorized into 4 classes: normal, cyst, haemangioma and hepatic cellular carcinoma. The diagnosis scheme includes 3 steps: feature extraction, feature selection and classification. For each ROI, 3 distinct kinds of...
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