A Novel Multispectral Lab-depth based Edge Detector for Color Images with Occluded Objects

International audienceThis paper presents a new method for edge detection based on both Lab color and depth images. The principalchallenge of multispectral edge detection consists of integrating different information into one meaningfulresult, without requiring empirical parameters. Our method combines the Lab color channels and depth information in a well-posed way using the Jacobian matrix. Unlike classical multi-spectral edge detection methods using depth information, our method does not use empirical parameters. Thus, it is quite straightforward and efficient. Experiments have been carried out on Middlebury stereo dataset and several selected challenging images. Experimental results show that the proposed method outperforms recent relevant state-of-the-art methods

A novel multispectral corner detector and a new local descriptor: an application to human posture recognition

International audienceHuman posture recognition is an important task for intelligent systems specially those performing action recognition. In this paper, we propose a novel multispectral corner detector and a new HOG-based multispectral local descriptor. First, we select salient features which are extracted from an edge image obtained by picking the maximum eigenvalue of the jacobian matrix. Second, we extract for each feature point a local descriptor which combines both the Lab colour channels and depth information in a well-posed way using the Jacobian matrix. Last, we conduct a one-against-all learning strategy using both an incremental Covariance-guided One-Class Support Vector Machine (iCOSVM) and a Convolutional Neural Network (CNN). Experimental results show that we outperform the state-of-the-art methods whether our descriptor is combined with iCOSVM and with CNN