This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. David G. Lowe, "Distinctive Image Features from Scale-Invar…

The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. Navneet D…

We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descrip…

After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. Navneet Dalal and Bill…

We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. Navneet Dalal and Bill Triggs, "Histograms of Oriented Gradients for Human Detection" https://lear.inrial…

We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets. Matthew D Zeiler, Rob Fergus, "Vis…