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Pedestrian detection, as an important task in video surveillance and forensics applications, has been widely studied. However, its performance is unsatisfactory especially in the low resolution conditions. In realistic scenarios, the size of pedestrians in the images is often small, and detection can be challenging. To solve this problem, this paper proposes a novel resolution-score discriminative...
This paper presents a novel extended multi-structure local binary pattern (EMSLBP) approach for high-resolution image classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed EMSLBP approach, three-coupled descriptors with multi-structure sampling are proposed to extract complementary features (pixel value and radial difference) from local image patches. The...
Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture...
This paper, for the first time, introduces a multiple-class boosting scheme (MBS) to combine depth motion maps (DMMs) and completed local binary patterns (CLBP) for action recognition. DMMs derive from projecting depth frames onto three orthogonal Cartesian planes (front, side and top) and characterize the motion energy of an action, on which the CLBP features are further extracted. And then a new...
This paper presents a fusion approach for improving human action recognition based on two differing modality sensors consisting of a depth camera and an inertial body sensor. Computationally efficient action features are extracted from depth images provided by the depth camera and from accelerometer signals provided by the inertial body sensor. These features consist of depth motion maps and statistical...
This paper presents a computationally efficient method for action recognition from depth video sequences. It employs the so called depth motion maps (DMMs) from three projection views (front, side and top) to capture motion cues and uses local binary patterns (LBPs) to gain a compact feature representation. Two types of fusion consisting of feature-level fusion and decision-level fusion are considered...
Given a training set of images and a binary classifier, we introduce the notion of an exaggerated image stereotype for some image class of interest, which emphasizes/exaggerates the characteristic patterns in an image and visualizes which visual information the classification relies on. This is useful for gaining insight into the classification mechanism. The exaggerated image stereotypes results...
In the paper, the mapping relation between static characteristics of electro-hydraulic servo valve and fault pattern is analyzed. The method of multi-parameter fault pattern recognition of electro-hydraulic servo valve based on BP neural networks is introduced, which is researched based on tests. The result shows that the accuracy rate of fault pattern recognition is higher by adopting this method...
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