Side information creation for efficient Wyner-Ziv video coding: Classifying and reviewing

Video coding technologies have played a major role in the explosion of large market digital video applications and services. In this context, the very popular MPEG-x and H-26x video coding standards adopted a predictive coding paradigm, where complex encoders exploit the data redundancy and irrelevancy to 'control' much simpler decoders. This codec paradigm fits well applications and services such as digital television and video storage where the decoder complexity is critical, but does not match well the requirements of emerging applications such as visual sensor networks where the encoder complexity is more critical. The Slepian Wolf and Wyner-Ziv theorems brought the possibility to develop the so-called Wyner-Ziv video codecs, following a different coding paradigm where it is the task of the decoder, and not anymore of the encoder, to (fully or partly) exploit the video redundancy. Theoretically, Wyner-Ziv video coding does not incur in any compression performance penalty regarding the more traditional predictive coding paradigm (at least for certain conditions). In the context of Wyner-Ziv video codecs, the so-called side information, which is a decoder estimate of the original frame to code, plays a critical role in the overall compression performance. For this reason, much research effort has been invested in the past decade to develop increasingly more efficient side information creation methods. This paper has the main objective to review and evaluate the available side information methods after proposing a classification taxonomy to guide this review, allowing to achieve more solid conclusions and better identify the next relevant research challenges. After classifying the side information creation methods into four classes, notably guess, try, hint and learn, the review of the most important techniques in each class and the evaluation of some of them leads to the important conclusion that the side information creation methods provide better rate-distortion (RD) performance depending on the amount of temporal correlation in each video sequence. It became also clear that the best available Wyner-Ziv video coding solutions are almost systematically based on the learn approach. The best solutions are already able to systematically outperform the H.264/AVC Intra, and also the H.264/AVC zero-motion standard solutions for specific types of content. (C) 2013 Elsevier B.V. All rights reserved.

Perceptually driven video error protection using a distributed source coding approach

In video communication systems, the video signals are typically compressed and sent to the decoder through an error-prone transmission channel that may corrupt the compressed signal, causing the degradation of the final decoded video quality. In this context, it is possible to enhance the error resilience of typical predictive video coding schemes using as inspiration principles and tools from an alternative video coding approach, the so-called Distributed Video Coding (DVC), based on the Distributed Source Coding (DSC) theory. Further improvements in the decoded video quality after error-prone transmission may also be obtained by considering the perceptual relevance of the video content, as distortions occurring in different regions of a picture have a different impact on the user's final experience. In this context, this paper proposes a Perceptually Driven Error Protection (PDEP) video coding solution that enhances the error resilience of a state-of-the-art H.264/AVC predictive video codec using DSC principles and perceptual considerations. To increase the H.264/AVC error resilience performance, the main technical novelties brought by the proposed video coding solution are: (i) design of an improved compressed domain perceptual classification mechanism; (ii) design of an improved transcoding tool for the DSC-based protection mechanism; and (iii) integration of a perceptual classification mechanism in an H.264/AVC compliant codec with a DSC-based error protection mechanism. The performance results obtained show that the proposed PDEP video codec provides a better performing alternative to traditional error protection video coding schemes, notably Forward Error Correction (FEC)-based schemes. (C) 2013 Elsevier B.V. All rights reserved.

TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services

he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Autobiographie et engagement : l’ambiguïté du genre et le discours politique de L’Amérique au jour le jour comme laboratoire scripturaire dans l’œuvre de Simone de Beauvoir

L’objet de ce mémoire est d’explorer le lien étroit qui existe entre la pratique de l’autobiographie et l’écriture comme forme d’engagement dans l’œuvre de Simone de Beauvoir, grâce à une analyse du genre ambigu de L’Amérique au jour le jour et des discours politiques qu’il renferme. Bien que L’Amérique au jour le jour constitue le corpus principal de ce mémoire, nous utiliserons aussi des textes contemporains à la rédaction du journal de voyage américain pour guider notre classification générique, dont les Lettres à Nelson Algren, les Lettres à Sartre et Les Mandarins, ainsi que les volumes de l’ensemble autobiographique beauvoirien qui portent sur l’après-guerre, même si ceux-ci sont postérieurs à la rédaction du journal. À l’aide de concepts issus de la poétique des genres, comme les questions de hiérarchie, de proportion, d’intention et de programme, et de l’éthique de l’engagement de l’écrivain telle que définie par la notion sartrienne de l’engagement, nous tenterons de démontrer que l’ambiguïté générique de L’Amérique au jour le jour relève d’une action délibérée de l’auteure visant à mettre en péril son capital symbolique pour assurer la crédibilité de son engagement intellectuel. Une fois les concepts précités définis, le deuxième chapitre de notre mémoire s’attardera à explorer toutes les facettes de l’ambiguïté générique du journal américain, alors que le troisième chapitre démontrera le lien entre les écritures intimes et l’engagement, tout en explorant les formes que prend l’engagement dans le livre. Pour ce faire, nous analyserons trois discours politiques tenus par Beauvoir dans son œuvre : la critique du consumérisme américain, la critique de la condition des Noirs et la critique de la femme américaine. Nous conclurons notre mémoire en démontrant que L’Amérique au jour le jour est devenu une sorte de matrice dans la pratique autobiographique et scripturaire de Simone de Beauvoir, ainsi que dans son engagement.

Phylogénie moléculaire du genre Salix L. (Salicaceae) en Amérique du Nord

La culture de saules (Salix sp.) est une pratique courante en Europe et en Amérique du Nord pour produire de la biomasse végétale. Cependant, le développement d’outils moléculaires est très récent. De plus, la phylogénie des saules est incomplète. Il y a un manque d’information pour les programmes de sélection d'espèces indigènes et pour la compréhension de l’évolution du genre. Le genre Salix inclut 500 espèces réparties principalement dans les régions tempérées et boréo-arctique de l’hémisphère nord. Nous avons obtenu l’ensemble des espèces retrouvées naturellement en Amérique (121 indigènes et introduites). Dans un premier temps, nous avons développé de nouveaux outils moléculaires et méthodes : extraction d’ADN, marqueurs microsatellites et gènes nucléaires. Puis, nous avons séquencé deux gènes chloroplastiques (matK et rbcL) et la région ITS. Les analyses phylogénétiques ont été réalisées selon trois approches : parcimonie, maximum de vraisemblance et Bayésienne. L’arbre d’espèces obtenu a un fort support et divise le genre Salix en deux sous-genres, Salix et Vetrix. Seize espèces ont une position ambiguë. La diversité génétique du sous-genre Vetrix est plus faible. Une phylogénie moléculaire complète a été établie pour les espèces américaines. D’autres analyses et marqueurs sont nécessaires pour déterminer les relations phylogénétiques entre certaines espèces. Nous affirmons que le genre Salix est divisé en deux clades.

Phylogénie et biogéographie du genre Bauhinia s.l. (Leguminosae)

Bauhinia s.l. est le plus vaste genre de la tribu des Cercideae (Ceasalpinioideae, Leguminoseae), avec plus de 300 espèces. Il présente une distribution pantropicale et une grande variabilité morphologique. Ces deux caractéristiques ont limité les études taxonomiques sur le genre complet, résultant en plusieurs études taxonomiques de certains groupes seulement. En 1987, Wunderlin et al. proposent une vaste révision taxonomique de la tribu des Cercideae, basée sur des données morphologiques, et divisent le genre Bauhinia en quatre sous-genres. En 2005, Lewis et Forest publient une nouvelle classification préliminaire basée sur des données moléculaires, mais sur un échantillonnage taxonomique restreint. Leurs conclusions remettent en question le monophylétisme du genre Bauhinia et suggèrent plutôt la reconnaissance de huit genres au sein du grade Bauhinia s.l. Afin de vérifier les hypothèses de Lewis et Forest, et obtenir une vision plus claire de l’histroire de Bauhinia s.l., nous avons séquencé deux régions chloroplastiques (trnL-trnF et matK-trnK) et deux régions nucléaires (Leafy et Legcyc) pour un vaste échantillonnage représentatif des Cercideae. Une première phylogénie de la tribu a tout d’abord été réalisée à partir des séquences de trnL-trnF seulement et a confirmé le non-monoplylétisme de Bauhinia s.l., avec l’inclusion du genre Brenierea, traditionnellement reconnu comme genre frère de Bauhinia s.l. Afin de ne pas limiter notre vision de l’histoire évolutive des Cercideae à un seul type de données moléculaires et à une seule région, une nouvelle série d’analyse a été effectuée, incluant toutes les séquences chloroplastiques et nucléaires. Une phylogénie individuelle a été reconstruite pour chacune des régions du génome, et un arbre d’espèce ainsi qu’un arbre de supermatrice ont été reconstruits. Bien que certaines contradictions apparaissent entre les phylogénies, les grandes lignes de l’histoire des Cercideae ont été résolues. Bauhinia s.l. est divisée en deux lignées : les groupes Phanera et Bauhinia. Le groupe Bauhinia est constitué des genres Bauhinia s.s., Piliostigma et Brenierea. Le groupe Phanera est constitué des genres Gigasiphon, Tylosema, Lysiphyllum, Barklya, Phanera et Schnella. Les genres Cercis, Adenolobus et Griffonia sont les groupes-frères du clade Bauhinia s.l. Au minimum un événement de duplication de Legcyc a été mis en évidence pour la totalité de la tribu des Cercideae, excepté Cercis, mais plusieurs évènements sont suggérés à la fois par Legcyc et Leafy. Finalement, la datation et la reconstruction des aires ancestrales de la tribu ont été effectuées. La tribu est datée de 49,7 Ma et est originaire des régions tempérées de l’hémisphère nord, probablement autour de la mer de Thétys. La tribu s’est ensuite dispersée vers les régions tropicales sèches de l’Afrique, où la séparation des groupes Bauhinia et Phanera a eu lieu. Ces deux groupes se sont ensuite dispersés en parallèle vers l’Asie du sud-est au début du Miocène. À la même période, une dispersion depuis l’Afrique de Bauhinia s.s. a permis la diversification des espèces américaines de ce genre, alors que le genre Schnella (seul genre américain du groupe Phanera) est passé par l’Australie afin de rejoindre le continent américain. Cette dispersion vers l’Australie sera également à l’origine des genres Lysiphyllum et Barklya

Gait Analysis for Classification

This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.

Validating Co-Training Models for Web Image Classification

Co-training is a semi-supervised learning method that is designed to take advantage of the redundancy that is present when the object to be identified has multiple descriptions. Co-training is known to work well when the multiple descriptions are conditional independent given the class of the object. The presence of multiple descriptions of objects in the form of text, images, audio and video in multimedia applications appears to provide redundancy in the form that may be suitable for co-training. In this paper, we investigate the suitability of utilizing text and image data from the Web for co-training. We perform measurements to find indications of conditional independence in the texts and images obtained from the Web. Our measurements suggest that conditional independence is likely to be present in the data. Our experiments, within a relevance feedback framework to test whether a method that exploits the conditional independence outperforms methods that do not, also indicate that better performance can indeed be obtained by designing algorithms that exploit this form of the redundancy when it is present.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Laser classification (NPL)

Short video on laser classification produced by the National Physical Laboratory

An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.