3D Object Comparison Based on Shape Descriptors

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Topology dictionary with Markov model for 3D video content-based skimming and description

This paper presents a novel approach to skim and de-scribe 3D videos. 3D video is an imaging technology which consists in a stream of 3D models in motion captured by a synchronized set of video cameras. Each frame is composed of one or several 3D models, and therefore the acquisition of long sequences at video rate requires massive storage de-vices. In order to reduce the storage cost while keeping rele-vant information, we propose to encode 3D video sequences using a topology-based shape descriptor dictionary. This dictionary is either generated from a set of extracted pat-terns or learned from training input sequences with seman-tic annotations. It relies on an unsupervised 3D shape-based clustering of the dataset by Reeb graphs, and features a Markov network to characterize topological changes. The approach allows content-based compression and skimming with accurate recovery of sequences and can handle com-plex topological changes. Redundancies are detected and skipped based on a probabilistic discrimination process. Semantic description of video sequences is then automat-ically performed. In addition, forthcoming frame encoding is achieved using a multiresolution matching scheme and allows action recognition in 3D. Our experiments were per-formed on complex 3D video sequences. We demonstrate the robustness and accuracy of the 3D video skimming with dramatic low bitrate coding and high compression ratio. 1

Picture recognition in animals and in humans : a review

The question of object–picture recognition has received relatively little attention in both human and comparative psychology; a paradoxical situation given the important use of image technology (e.g. slides, digitised pictures) made by neuroscientists in their experimental investigation of visual cognition. The present review examines the relevant literature pertaining to the question of the correspondence between and:or equivalence of real objects and their pictorial representations in animals and humans. Two classes of reactions towards pictures will be considered in turn: acquired responses in picture recognition experiments and spontaneous responses to pictures of biologically relevant objects (e.g. prey or conspecifics). Our survey will lead to the conclusion that humans show evidence of picture recognition from an early age; this recognition is, however, facilitated by prior exposure to pictures. This same exposure or training effect appears also to be necessary in nonhuman primates as well as in other mammals and in birds. Other factors are also identified as playing a role in the acquired responses to pictures: familiarity with and nature of the stimulus objects, presence of motion in the image, etc. Spontaneous and adapted reactions to pictures are a wide phenomenon present in different phyla including invertebrates but in most instances, this phenomenon is more likely to express confusion between objects and pictures than discrimination and active correspondence between the two. Finally, given the nature of a picture (e.g. bi-dimensionality, reduction of cues related to depth), it is suggested that object–picture recognition be envisioned in various levels, with true equivalence being a limited case, rarely observed in the behaviour of animals and even humans

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field