introduction: we hypothesized that inappropriate attention during the period of abstinence in individuals with substance use disorder can result in an inadequate perception of emotion and unsuitable reaction to emotional scenes. the main aim of this research was to evaluate the attentional bias towards emotional images in former substance abusers and compare it to healthy adults. methods: paire...

This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences betw...

This paper presents a dataset that can be used for testing and evaluation of computer vision-based algorithms for 3D reconstruction and 3D person tracking. Additional possible uses include the building of 3D human head models and the production of stereoscopic sequences. A number of different scenes are included in the dataset. They are mostly single-subject scenes captured with two different l...

This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64...

In this paper we propose a method for the detection of dialogue scenes within movies. This task is of particular interest given the special semantic role played by dialogue based scenes in the most part of movies. The proposed approach firstly operates the segmentation of the video footage in shots, then each shot is classified as dialogue or not-dialogue by a Multi-Expert System (MES) and, fin...

This paper proposes Motion Structure Tracker (MST) to solve the problem of tracking in very crowded structured scenes. It combines visual tracking, motion pattern learning and multi-target tracking. Tracking in crowded scenes is very challenging due to hundreds of similar objects, cluttered background, small object size, and occlusions. However, structured crowded scenes exhibit clear motion pa...

In a visual driver-assistance system, road-sign detection and tracking is one of the major tasks. This study describes an approach to detecting and tracking road signs appearing in complex traffic scenes. In the detection phase, two neural networks are developed to extract color and shape features of traffic signs from the input scenes images. Traffic signs are then located in the images based ...

Understanding complex, dynamic scenes of real-world activities from low-level sensor data is of central importance for intelligent systems. The main difficulty lies in the fact that complex scenes are best described in high-level, logical formalisms, while sensor data usually consists of many low-level features. We first propose a method to obtain a logical representation of real-world, dynamic...

A scene boundary detection method is presented, which analyzes both aural and visual information sources and accounts for their inter-relations and coincidence to semantically identify video scenes. Audio analysis focuses on the segmentation of the audio source into three types of semantic primitives, i.e. silence, speech and music. Further processing on speech segments aims at locating speaker...