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OpenCV Essentials by Jesus Salido Tercero, Julio Alberto Patón Incertis, Ismael Serrano Gracia, Gloria Bueno García, Noelia Vállez Enano, Mª del Milagro Fernández Carrobles, Oscar Deniz Suarez

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Latent SVM

Latent SVM is a detector that uses HOG features and a star-structured, part-based model consisting of a root filter and a set of part filters to represent an object category. HOGs are feature descriptors that are obtained by counting the occurrences of gradient orientations in localized portions of an image. On the other hand, a variant of support vector machines (SVM) classifiers are used in this detector to train models using partially labeled data. The basic idea of an SVM is constructing a hyperplane or set of hyperplanes in high-dimensional space. These hyperplanes are obtained to have the largest distance to the nearest training data point (functional margin in order to achieve low generalization errors). Like cascade detectors, ...

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