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vision-Inertial Tracking System for Robust Fiducials Registration in Augmented Reality

Vision-Inertial Tracking System for Robust Fiducials Registr...

引用

ieee symposium on computational intelligence for multimedia signal and vision processing

作者： Maidi, Madjid Ababsa, Fakhreddine Mallem, Malik Univ Evry Val Essonne Comp Sci & Complex Syst Lab IBISC F-91020 Evry France

ISBN: (纸本)9781424427710

This paper describes a multimodal tracking system to resolve occlusions in augmented reality applications. The first module of the proposed architecture is composed of a vision based system and allows identification and tracking of visible targets. When targets are partially occluded by scene elements, a second module relieves the vision based module and tracks feature points using a robust algorithm. Finally, a multi-sensors tracking approach is implemented to handle total occlusion of targets and maintains registration even if all markers are not visible. Experimental results and many evaluations have been performed to show the efficiency and robustness of the proposed multimodal approach of tracking and occlusion handling in augmented reality.

关键词： Augmented reality

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Unsupervised Learning Algorithm for signal Separation

Unsupervised Learning Algorithm for Signal Separation

引用

ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Jacob, Theju Snyder, Wesley North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781479945030

We present a neural network capable of separating inputs in an unsupervised manner. Oja's rule and Self-Organizing map principles are used to construct the network. The network is tested using 1) straight lines 2) MNIST database. The results demonstrate that the network can operate as a general clustering algorithm, with neighboring neurons responding to geometrically similar inputs.

关键词： pattern clustering self-organising feature maps source separation unsupervised learning Oja rule general clustering algorithm neural network neurons self-organizing map principles signal separation unsupervised learning algorithm Clustering algorithms Hebbian theory Lattices Neurons Training

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Target-surround feature attention model of visual tracking

Target-surround feature attention model of visual tracking

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Huang, Yu-Wei Lin, Wei-Song Lin, Ru-Je Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781424499151

This paper presents a target-surround feature attention (TSFA) model for constructing attention-based visual tracking algorithm. This model extracts attentive region by distinguishing the color contrast between the interested target and its surround. A preference generator provides online feature transformation to update the target/surround biasing masks that describes the color composition associated with the target and its surround. Output of the TSFA model is a saliency map representing occurrence possibility of the target. Tracker based on the mean shift algorithm is used to lock and locate the target on the saliency map. Experimental results show that visual tacking algorithm with the TSFA model may adapt to noisy images under changing illumination. © 2011 ieee.

关键词： Computer vision

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Dynamic vision sensor camera based bare hand gesture recognition

Dynamic vision sensor camera based bare hand gesture recogni...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Ahn, Eun Yeong Lee, Jun Haeng Mullen, Tracy Yen, John Information Sciences and Technology Pennsylvania State University University Park PA United States Advanced Samsung Institute of Technology Samsung Electronics Yongin-si Gyeonggi-do Korea Republic of

ISBN: (纸本)9781424499151

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition. © 2011 ieee.

关键词： Cameras

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cobICA: A Concentration-Based, Immune-Inspired Algorithm for ICA Over Galois Fields

cobICA: A Concentration-Based, Immune-Inspired Algorithm for...

引用

ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Silva, Daniel G. Montalvao, Jugurta Attux, Romis Univ Brasilia Dept Elect Engn Brasilia DF Brazil Univ Estadual Campinas Campinas SP Brazil Univ Fed Sergipe Sao Cristovao SE Brazil Univ Estadual Campinas DSPCom Lab Campinas SP Brazil

ISBN: (纸本)9781479945030

An interesting and recent application of population-based metaheuristics resides in an unsupervised signal processing task: independent component analysis (ICA) over finite fields. Based on a state-of-the-art immune-inspired method, this work proposes a new ICA algorithm for finite fields of arbitrary order that employs mutation and local search operators specifically customized to the problem domain. The results obtained with the new technique indicate that the proposal is effective in performing component separation, and the analysis includes a preliminary study on image separation.

关键词： Galois fields independent component analysis mathematical operators search problems signal processing arbitrary order cobICA component separation concentration-based algorithm finite fields image separation immune-inspired algorithm immune-inspired method local search operator mutation operator population-based metaheuristics unsupervised signal processing task Algorithm design and analysis Cloning Entropy Immune system Search problems Sociology Statistics

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Incremental Semi-Supervised Fuzzy Clustering for Shape Annotation

Incremental Semi-Supervised Fuzzy Clustering for Shape Annot...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Castellano, Giovanna Fanelli, Anna Maria Torsello, Maria Alessandra Univ Bari A Moro Dept Informat Bari Italy

ISBN: (纸本)9781479945030

In this paper, we present an incremental clustering approach for shape annotation, which is useful when new sets of images are available over time. A semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes belonging to that cluster. To capture the evolution of the image set over time, the previously discovered prototypes are added as pre-labeled objects to the current shape set and semi-supervised clustering is applied again. The proposed incremental approach is evaluated on two benchmark image datasets, which are divided into chunks of data to simulate the progressive availability of images during time.

关键词： fuzzy set theory image classification learning (artificial intelligence) pattern clustering incremental approach incremental semisupervised fuzzy clustering progressive availability simulation shape annotation Clustering algorithms Context History Marine animals Prototypes Shape Transform coding

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Biomimetic target detection: modeling 2nd order correlation of OFF and ON channels

Biomimetic target detection: modeling 2nd order correlation ...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Wiederman, Steven D. O'Carroll, David C. Univ Adelaide Adelaide Ctr Neurosci Res Adelaide SA 5005 Australia

ISBN: (纸本)9781467359177

Flying insects have proven to be valuable animal models in elucidating the computational processes underlying visual motion detection. For example, wide-field motion processing (optic flow) in the insect visual system has been investigated from both behavioral and physiological perspectives [1] resulting in computational models with diverse applications [2,3]. However, insects must also extract the movement of prey or conspecifics from their environment, therefore have the capability of detecting and interacting with small moving targets, even amidst swarms [4,5]. Using electrophysiological techniques, we record from small target motion detector (STMD) neurons in the insect brain, likely to underlie these behaviors. We previously proposed an 'elementary' small target motion detector (ESTMD) model of STMDs that accounts for their size and velocity tuning, as well as their ability to discriminate targets against cluttered surrounds [6-8]. However, other properties such as directionality [9], facilitated responses [10] and high contrast sensitivity [11] are not accounted for in this model. Here we propose two model variants that cascade the ESTMD with a traditional motion detection model, the Hassenstein Reichardt 'elementary motion detector' (EMD) [12]. We show that these elaborations maintain core STMD properties as well as model directionality and highly expansive contrast sensitivity. By encapsulating the properties of STMD neurons we aim to develop computational models that can simulate these insects remarkable capability of target discrimination and pursuit for application in robotics and artificial vision systems.

关键词： insect motion vision small target motion detector ON OFF channels motion detection second order motion

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Multivariate PDF Matching via Kernel Density Estimation

Multivariate PDF Matching via Kernel Density Estimation

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Fantinato, Denis G. Boccato, Levy Attux, Romis Neves, Aline Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Sao Andre SP Brazil

ISBN: (纸本)9781479945030

In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.

关键词： Gaussian processes evolutionary computation image processing learning (artificial intelligence) probability Gaussian kernel ITL framework Parzen window method blind equalization scenario computational intelligence paradigm correntropy approach differential evolution extreme learning machines information theoretic learning framework kernel density estimation multivariate PDF matching probability density function quadratic distance statistical dependence computational efficiency Estimation Joints Kernel Measurement Probability density function Vectors

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Investigation of local and global features for face detection

Investigation of local and global features for face detectio...

引用

symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Pereira, Eanes Gomes, Herman Moura, Eduardo Carvalho, João Zhang, Tong Center for Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Paraíiba Brazil Multimedia Interaction and Understanding Lab Hewlett-Packard Laboratories Palo Alto CA United States

ISBN: (纸本)9781424499151

This work is concerned with the empirical evaluation of a set of local and global features under the context of frontal (including semi-profile) and full profile face classification. Integral LBP, Integral Histograms, PCA and Optimized Face Ratios features have been evaluated using SVM classifiers. A data set of about 14,000 face and 300,000 non face images has been used in the experiments. Face images were obtained from well known public face research databases, such as BioID, Color Feret, CMU PIE, among others. The PCA-SVM classifier presented best overall results for both frontal and full profile faces whereas the classifier based on Face Ratios presented the lowest classification rates. A weighted combination of all classifiers yielded high True Positive (TPR) and True Negative (TNR) rates: 91.7% and 100%, respectively, for the frontal face experiments;99.59% and 99.62%, respectively, for the profile face experiments. These results indicate that the evaluated features are very suitable to the problem of face detection and that a simple classifier combination improves individual classifiers performance. © 2011 ieee.

关键词： Face recognition

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Stereoscopic Video Description for Human Action Recognition

Stereoscopic Video Description for Human Action Recognition

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Mademlis, Ioannis Iosifidis, Alexandros Tefas, Anastasios Nikolaidis, Nikos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9781479945030

In this paper, a stereoscopic video description method is proposed that indirectly incorporates scene geometry information derived from stereo disparity, through the manipulation of video interest points. This approach is flexible and able to cooperate with any monocular low-level feature descriptor. The method is evaluated on the problem of recognizing complex human actions in natural settings, using a publicly available action recognition database of unconstrained stereoscopic 3D videos, coming from Hollywood movies. It is compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Experimental results denote that the proposed approach outperforms them and achieves state-of-theart performance.

关键词： feature extraction geometry object recognition stereo image processing video signal processing visual databases Hollywood movies action recognition database depth-aware approach human action recognition monocular algorithm monocular low-level feature descriptor scene geometry information stereo disparity stereoscopic video description unconstrained stereoscopic 3D videos video interest point manipulation Cameras Erbium Image color analysis Pattern recognition Stereo image processing Three-dimensional displays Vectors Stereo image processing three-dimensional displays image colour analysis visual databases Video signal processing Erbium Cameras object recognition Pattern recognition Feature extraction Stereoscopic Videotapes geometry

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