Real-time filtering and detection of dynamics for compression of HDTV

The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals

Automated solar radio burst detection on radio spectrum: a review of techniques in image processing

The information of solar atmosphere was obtained after investigating the recording radiation of the space mission. With technology growing recently, a lot of solar radio receiver was introduced to monitor the solar radio activity on the ground with high efficiency. It is recorded in every second for 24 hours per day. A massive of solar radio spectra data produced every day that makes it impossible to identify, whether the data contain burst or not. By doing manual detection, human effort and error become the issues when the solar astronomer needs the fast and accurate result. Recently, the success of various techniques in image processing to identify solar radio burst automatically was presented. This paper reviews previous technique in image processing. This discussion will help the solar astronomer to find the best technique in pre-processing before moving into the next stage for detection of solar radio burst.Keywords: monitoring solar activity; automated solar radio burst detection; image processing; techniqu

Implémentation temps réel d'algorithme de détection de mouvement par champs de markov sur RISC et DSP C6x

- Cet article décrit une implémentation temps réel d'un algorithme de détection de mouvement basée sur les champs de Markov. Il décrit aussi les optimisations architecturales appliquées aux RISC et au DSP C6x qui permettent d'atteindre le temps réel

Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation

The extraction of moving objects from their background is a challenging task in visual surveillance. As a single threshold often fails to resolve ambiguities and correctly segment the object, in this paper, we propose a new method that uses three thresholds to accurately classify pixels as foreground or background. These thresholds are adaptively determined by considering the distributions of differences between the input and background images and are used to generate three boundary sets. These boundary sets are then merged to produce a final boundary set that represents the boundaries of the moving objects. The merging step proceeds by first identifying boundary segment pairs that are significantly inconsistent. Then, for each inconsistent boundary segment pair, its associated curvature, edge response, and shadow index are used as criteria to evaluate the probable location of the true boundary. The resulting boundary is finally refined by estimating the width of the halo-like boundary and referring to the foreground edge map. Experimental results show that the proposed method consistently performs well under different illumination conditions, including indoor, outdoor, moderate, sunny, rainy, and dim cases. By comparing with a ground truth in each case, both the classification error rate and the displacement error indicate an accurate detection, which show substantial improvement in comparison with other existing methods. © 2010 IEEE.published_or_final_versio

Détection multirésolution des changements par analyse spectrale locale de Walsh-Hadamard

Cet article traite du problème de la détection automatique des changements sur un couple d'images multitemporelles en imagerie dite de surveillance. Les limites des approches classiques nous ont conduit à développer un nouvel opérateur, fondé sur l'analyse en composantes principales d'attributs spatio-fréquentiels extraits de spectres bidimensionnels locaux de Walsh-Hadamard à échelles dyadiques décroissantes. L'opérateur est utilisé dans le cadre d'une stratégie d'analyse multirésolution, ce qui permet de détecter des changements d'ordre textural tout en réduisant de façon significative le problème d'occlusion régionale sans perte de résolution spatiale. La méthode proposée fournit une description hiérarchique des changements de la scène et peut en outre bénéficier d'une optimisation calculatoire par décomposition du couple d'images en pyramides passe-bas

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance