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—When processing 3D point cloud data, features must be extracted from a small set of points, usually called keypoints or points of interest. This is done to avoid the computational complexity required to extract features from all points in a point cloud. There are many keypoint detectors, and this suggests the need for a comparative evaluation. In this(More)
With the emerging interest in active vision, computer vision researchers have been increasingly concerned with the mechanisms of attention. Therefore, a number of visual attention computational models, inspired by the Human Visual System (HVS), have been developed. These models aim to detect regions of interest in images. In recent decades, psychologists,(More)
This paper presents two methods for automatic segmentation of images of faces captured in long wavelength infrared, allowing a wide range of face rotations, expressions and artifacts (such as glasses and hats). We also present the validation of segmentation results using a recognition method to show the impact of the segmentation accuracy on the(More)
We present a new method for the detection of 3D keypoints on point clouds and we perform benchmarking between each pair of 3D keypoint detector and 3D descriptor to evaluate their performance on object and category recognition. Our keypoint detector is inspired by the behavior and neural architecture of the primate visual system. The 3D keypoints are(More)
Nowadays, face detection techniques assume growing relevance in a wide range of applications (e.g., bio-metrics and automatic surveillance)and constitute a prerequisite of many image processing stages. Among a large number of published approaches, one of the most relevant is the method proposed by Viola and Jones [18] to perform real-time face detection(More)
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