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Object detection and tracking phd thesis


object detection and tracking phd thesis

image domain, a multi-scale approach is therefore necessary. In (Lindeberg 2013b, 2015) 2 3 it is shown that the determinant of the Hessian operator performs significantly better than the Laplacian operator or its difference-of-Gaussians approximation, as well as better than the Harris or Harris-Laplace operators, for image-based matching using local sift-like or surf-like. Permanent archiving in digital archives. Chang International Conference on Machine Learning (icml 2012. Chang International Conference on Machine Learning (icml 2014. International Journal of Computer Vision. Eklundh, "On the computation of a scale-space primal sketch Journal of Visual Communication and Image Representation, vol.

Object detection and tracking phd thesis
object detection and tracking phd thesis

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Thus, simultaneous selection of interest points (x,y)displaystyle (hat x,hat y) and scales tdisplaystyle hat t is performed according to (hat x,hat y;hat t)operatorname argmaxminlocal x,y;t nabla _norm2L x,y;t). Laplacian of the, gaussian (LoG). Using a mixture of the above: Combining several good approaches normally yields an even better result. Here is a list of the most common techniques in face detection: (you really should read to the end, else you will miss the most important developments! Beyond local contrast and extent, these scale-space blobs also measured how stable image structures are in scale-space, by measuring their scale-space lifetime.

Blob detection - Wikipedia



object detection and tracking phd thesis


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