We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face la...

Most state-of-the-art approaches to action recognition rely on global representations either by concatenating local information in a long descriptor vector or by computing a single location independent histogram. This limits their performance in presence of occlusions and when running on multiple viewpoints. We propose a novel approach to providing robustness to both occlusions and viewpoint ch...

Small bowel motility dysfunctions are a widespread functional disorder characterized by abdominal pain and altered bowel habits in the absence of specific and unique organic pathology. Current methods of diagnosis are complex and can only be conducted at some highly specialized referral centers. Wireless Video Capsule Endoscopy (WCE) could be an interesting diagnostic alternative that presents ...

The paper describes a novel segmentation system based on the combination of Histogram of Oriented Gradients (HOG) descriptors and linear Support Vector Machine (SVM) classification for football video. Recently, HOG methods were widely used for pedestrian detection. However, presented experimental results show that combination of HOG and SVM is very promising for locating and segmenting players....

This paper presents a real-time Human detection algorithm based on HOG (Histograms of Oriented Gradients) features and SVM (Support Vector Machine) architecture. Motion detection is used to extract moving regions, which can be scanned by sliding windows; detecting moving region can subtract unnecessary sliding windows of static background regions under the surveillance conditions, then detectio...

This paper introduces a solution for detecting humans in smart spaces through computer vision. The approach is valid both for images in visible and infrared spectra. Histogram of oriented gradients (HOG) is used for feature extraction in the human detection process, whilst linear support vector machines (SVM) are used for human classification. A set of tests is conducted to find the classifiers...

This paper addresses the problem of fast Human Activity Recognition (HAR) in visual lifelogging. We identify the importance of visual features related to HAR and we specifically evaluate the HAR discrimination potential of Colour Histograms and Histogram of Oriented Gradients. In our evaluation we show that colour can be a low-cost and e↵ective means of low-cost HAR when performing single-user ...

This paper provides a comparison between two of the most used visual descriptors (image features) nowadays in the field of object detection. The investigated image features involved the Haar filters and the Histogram of Oriented Gradients (HoG) applied for the on road vehicle detection. Tests are very encouraging with a average detection of 96% on realistic on-road vehicle images.

In this paper we propose a human detection framework based on an enhanced version of Histogram of Oriented Gradients (HOG) features. These feature descriptors are computed with the help of a precalculated histogram of square-blocks. This novel method outperforms the integral of oriented histograms allowing the calculation of a single feature four times faster. Using Adaboost for HOG feature sel...

In the first part of this contribution we prove the global existence and uniqueness of a trajectory that globally converges to the minimizer of the Gross-Pitaevskii energy functional for a large class of external potentials. Using the method of Sobolev gradients we can provide an explicit construction of this minimizing sequence. In the second part we numerically apply these results to a specif...