Stereo correspondence from motion correspondence

Abstract This paper introduces a new framework for stereo c orrespondence r ecovery using one motion of a stereo rig. Both the stereo correspondence and the motion of the stereo rig are unknown. By combining the stereo g e ometry and the motion correspondence w e a r e able to infer the stereo c orrespondence f r om motion correspondence without having to systematically use the intensity-based stereo matching algorithms. The stereo c orrespondence recovery consists of two consecutive steps: the rst step uses metric data associated with the stereo rig while the second step uses feature c orrespondences only. Experiments involving real stereo p airs indicate the feasibility and robustness of the approach

Consensus graph and spectral representation for one-step multi-view kernel based clustering

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multiview Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches

Analyse et gestion de l’occupation de places de stationnement par vision artificielle

Cet article présente un système de surveillance basé sur la vision pour le développement de services de gestion de places de parking. Le système présenté est un système adaptable pour l'analyse de places de stationnement dans des parkings de différentes configurations. Dans ce but, des expérimentations ont été menées sous différentes prises de vue en utilisant une caméra connectée à une station de travail mobile. Les résultats obtenus montrent la faisabilité du système dans l'analyse et dans la gestion des emplacements de parking avec des véhicules

The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions

The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions