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We propose a shape-based, hierarchical part-template matching approach to simultaneous human detection and segmentation combining local part-based and global shape-template-based schemes. The approach relies on the key idea of matching a part-template tree to images hierarchically to detect humans and estimate their poses. For learning a generic human detector, a pose-adaptive feature computation...
This paper proposes the video object segmentation using block-matching-based self-organization maps and a kernel function. A window covering a target object in a frame is split into units with some pixels. A vector with two elements is provided a unit to quantify its color attributes, and is presented to train a map. It is also presented to classify the units. The trained map then judges the class...
We use spectral analysis to facilitate Gaussian processes (GP) classification. Our solution provides two improvements: scaling of the data to achieve a more isotropic nature, as well as a method to choose the kernel to match certain data characteristics. Given the dataset, from the Fourier transform of the training data we compare the frequency domain features of each dimension to estimate a rescaling...
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