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Kernel descriptors have been proven to outperform existing histogram based local descriptors as such descriptors are extracted from the match kernels which measure similarities between image patches using different pixel attributes (gradient, colour or LBP pattern). The extraction of kernel descriptors does not require coarse quantization of pixel attributes. Instead, each pixel equally participates...
Shape features are one of the most popular low-level image representations for computer vision (CV) tasks such as template matching, image collaboration and object recognition. In this paper, an application-originated research has been introduced for extracting representative shape characteristics from challenging real-world scenes based on the image “textures”. The proposed new approach starts from...
In a typical computer vision application, such as video event detection, the “meaningful” information is fundamentally represented by pre-defined features, which determine the appropriate analytical methodologies in the following processing phases. Based on the uncompressed low-level image characteristics, such as colour, intensity and spatial positions, the features used for event detection in this...
To exert color display advantage of wide gamut displays, there needs gamut expansion from the current standard gamut to the gamut of these devices. A novel gamut boundary descriptor and a three dimensional gamut expansion method using a fixed anchor point in CIELUV color space are proposed. Gamut boundary is represented by a closed surface composed of a series of triangles and the line gamut boundary...
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