DOCSLIB.ORG

Biologically Motivated Keypoint Detection for RGB-D Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Biologically Motivated Keypoint Detection for RGB-D Data UNIVERSIDADE DA BEIRA INTERIOR Engineering Biologically Motivated Keypoint Detection for RGB-D Data Sílvio Brás Filipe Thesis for obtaining the degree of Doctor of Philosophy in Computer Science and Engineering (3rd Cycle Studies) Covilhã, November 2016 Biologically Motivated Keypoint Detection for RGB-D Data ii Thesis prepared at IT - Instituto de Telecomunicações, within Pattern and Image Analysis - Covilhã, and submitted to University of Beira Interior for defense in a public examination session. Work is supported by `FCT - Fundação para a Ciência e Tecnologia' (Portugal) through the research grant `SFRH/BD/72575/2010', and the funding from `FEDER - QREN - Type 4.1 - Formação Avançada', co-founded by the European Social Fund and by national funds through Portuguese `MEC - Ministério da Educação e Ciência'. It is also supported by the IT - Instituto de Telecomunicações through `PEst-OE/EEI/LA0008/2013'. iii Biologically Motivated Keypoint Detection for RGB-D Data iv Dedicatory Dedicated to the persons who love me. v Biologically Motivated Keypoint Detection for RGB-D Data vi Acknowledgments This is an important part of this work, since I have the opportunity to thank the people who in one way or another were essential for its development. It would not have been possible to make this thesis without the help and support of many people. First and foremost, I would like to express my gratitude to the University of Beira Interior, specially to Dr. Luís A. Alexandre and Dr. Hugo Proença by the knowledge, support, advice, motivation, guidance, knowledge, and encouragement they gave me during the eight years that I worked with them. They have surely made my research a fulfilling and rewarding experience. I also appreciate the support, motivation and friendship given by Dr. Manuela Pereira, Dr. Simão de Sousa and Dr. Paulo Fiadeiro. I am equally grateful to Dr. Laurent Itti, professor of computer science, psychology and neuroscience at University of Southern California (Los Angeles, USA) for providing a stimulating environment and knowledge during my short visit as a PhD student. I am grateful to the `FCT - Fundação para a Ciência e Tecnologia' through `SFRH/BD/72575 /2010', Instituto de Telecomunicações (PEst-OE/EEI/LA0008/2013) and `FEDER - QREN - Type 4.1 - Formação Avançada', co-founded by the European Social Fund and by the Portuguese `MEC - Ministério da Educação e Ciência', for their financial support, without which it would not have been possible to complete the present document. I am also grateful to the anonymous reviewers who have read my work with professional care and attention and have offered valuable comments and suggestions. I would also like to thank my colleagues at the Soft Computing and Image Analysis Lab (SOCIA-LAB), with whom I shared the good and bad times of doing research. Finally, I would like to express my gratitude to my family, girlfriend and friends for their unconditional support and encouragement. Especially my parents support and help me through- out the stages of my life. Many thanks to all! vii Biologically Motivated Keypoint Detection for RGB-D Data viii "There is a real danger that computers will develop intelligence and take over. We urgently need to develop direct connections to the brain so that computers can add to human intelligence rather than be in opposition." Stephen Hawking ix Biologically Motivated Keypoint Detection for RGB-D Data x List of Publications Journal Papers 1. BIK-BUS: Biologically Motivated 3D Keypoint based on Bottom-Up Saliency Filipe, S., Itti, L., Alexandre, L.A., IEEE Transactions on Image Processing, vol. 24, pp. 163-175, January 2015. International Conference Papers 1. PFBIK-Tracking: Particle Filter with Bio-Inspired Keypoints Tracking Filipe, S., Alexandre, L.A., in IEEE Symposium on Computational Intelligence for Multime- dia, Signal and Vision Processing, 2014, December 9-12, Orlando, Florida, USA. 2. A Biological Motivated Multi-Scale Keypoint Detector for local 3D Descriptors Filipe, S., Alexandre, L.A., in 10th International Symposium on Visual Computing, 2014, pp. 218-227, December 8-10, Las Vegas, Nevada, USA. 3. A Comparative Evaluation of 3D Keypoint Detectors in a RGB-D Object Dataset Filipe, S., Alexandre, L.A., in 9th International Conference on Computer Vision Theory and Applications, 2014, pp. 476-483, January 5-8, Lisbon, Portugal. Portuguese Conference Papers 1. A 3D Keypoint Detector based on Biologically Motivated Bottom-Up Saliency Map Filipe, S., Alexandre, L.A., in 20th edition of the Portuguese Conference on Pattern Recog- nition, 2014, October 31, Covilhã, Portugal, 2014. 2. A Comparative Evaluation of 3D Keypoint Detectors Filipe, S., Alexandre, L.A., in 9th Conference on Telecommunications, 2013, pp. 145-148, May 8-10, Castelo Branco, Portugal, 2013. xi Biologically Motivated Keypoint Detection for RGB-D Data xii Resumo Com o interesse emergente na visão ativa, os investigadores de visão computacional têm estado cada vez mais preocupados com os mecanismos de atenção. Por isso, uma série de modelos computacionais de atenção visual, inspirado no sistema visual humano, têm sido de- senvolvidos. Esses modelos têm como objetivo detetar regiões de interesse nas imagens. Esta tese está focada na atenção visual seletiva, que fornece um mecanismo para que o cérebro concentre os recursos computacionais num objeto de cada vez, guiado pelas pro- priedades de baixo nível da imagem (atenção Bottom-Up). A tarefa de reconhecimento de objetos em diferentes locais é conseguida através da concentração em diferentes locais, um de cada vez. Dados os requisitos computacionais dos modelos propostos, a investigação nesta área tem sido principalmente de interesse teórico. Mais recentemente, psicólogos, neurobiólo- gos e engenheiros desenvolveram cooperações e isso resultou em benefícios consideráveis. No início deste trabalho, o objetivo é reunir os conceitos e ideias a partir dessas diferentes áreas de investigação. Desta forma, é fornecido o estudo sobre a investigação da biologia do sistema visual humano e uma discussão sobre o conhecimento interdisciplinar da matéria, bem como um estado de arte dos modelos computacionais de atenção visual (bottom-up). Normalmente, a atenção visual é denominada pelos engenheiros como saliência, se as pessoas fixam o olhar numa determinada região da imagem é porque esta região é saliente. Neste trabalho de inves- tigação, os métodos saliência são apresentados em função da sua classificação (biologicamente plausível, computacional ou híbrido) e numa ordem cronológica. Algumas estruturas salientes podem ser usadas, em vez do objeto todo, em aplicações tais como registo de objetos, recuperação ou simplificação de dados. É possível considerar estas poucas estruturas salientes como pontos-chave, com o objetivo de executar o reconheci- mento de objetos. De um modo geral, os algoritmos de reconhecimento de objetos utilizam um grande número de descritores extraídos num denso conjunto de pontos. Com isso, estes têm um custo computacional muito elevado, impedindo que o processamento seja realizado em tempo real. A fim de evitar o problema da complexidade computacional requerido, as características devem ser extraídas a partir de um pequeno conjunto de pontos, geralmente chamados pontos- chave. O uso de detetores de pontos-chave permite a redução do tempo de processamento e a quantidade de redundância dos dados. Os descritores locais extraídos a partir das imagens têm sido amplamente reportados na literatura de visão por computador. Uma vez que existe um grande conjunto de detetores de pontos-chave, sugere a necessidade de uma avaliação compar- ativa entre eles. Desta forma, propomos a fazer uma descrição dos detetores de pontos-chave 2D e 3D, dos descritores 3D e uma avaliação dos detetores de pontos-chave 3D existentes numa biblioteca de pública disponível e com objetos 3D reais. A invariância dos detetores de pontos- chave 3D foi avaliada de acordo com variações nas rotações, mudanças de escala e translações. Essa avaliação retrata a robustez de um determinado detetor no que diz respeito às mudanças de ponto-de-vista e os critérios utilizados são as taxas de repetibilidade absoluta e relativa. Nas experiências realizadas, o método que apresentou melhor taxa de repetibilidade foi o método ISS3D. Com a análise do sistema visual humano e dos detetores de mapas de saliência com in- spiração biológica, surgiu a ideia de se fazer uma extensão para um detetor de ponto-chave com base na informação de cor na retina. A proposta produziu um detetor de ponto-chave 2D inspirado pelo comportamento do sistema visual. O nosso método é uma extensão com base xiii Biologically Motivated Keypoint Detection for RGB-D Data na cor do detetor de ponto-chave BIMP, onde se incluem os canais de cor e de intensidade de uma imagem. A informação de cor é incluída de forma biológica plausível e as característi- cas multi-escala da imagem são combinadas num único mapas de pontos-chave. Este detetor é comparado com os detetores de estado-da-arte e é particularmente adequado para tarefas como o reconhecimento de categorias e de objetos. O processo de reconhecimento é realizado comparando os descritores 3D extraídos nos locais indicados pelos pontos-chave. Para isso, as localizações do pontos-chave 2D têm de ser convertido para o espaço 3D. Isto foi possível porque o conjunto de dados usado contém a localização de cada ponto de no espaço 2D e 3D. A avaliação permitiu-nos obter o melhor par detetor de ponto-chave/descritor num RGB-D object dataset. Usando o nosso detetor de ponto-chave e o descritor SHOTCOLOR, obtemos uma noa taxa de reconhecimento de categorias e para o reconhecimento de objetos é com o descritor PFHRGB que obtemos os melhores resultados. Um sistema de reconhecimento 3D envolve a escolha de detetor de ponto-chave e de- scritor, por isso é apresentado um novo método para a deteção de pontos-chave em nuvens de pontos 3D e uma análise comparativa é realizada entre cada par de detetor de ponto-chave 3D e descritor 3D para avaliar o desempenho no reconhecimento de categorias e de objetos.
Load more

Recommended publications
  • The Photographerls Guide to The
    THE PHOTOGRAPHER’S GUIDE TO THE EYE EYES—NOSTRILS—LIPS—SHINY THINGS. My eyes scanned the photo of two women almost the way a monkey’s would, going first to the faces, specifically features that WHAT SCIENCE IS would indicate friend, foe, or possible mate, then to the brightest objects in the image: a blue bottle, a pendant. Surprisingly, it took LEARNING ABOUT HOW forever (almost three seconds) for me to look at the famous—and famously gorgeous—face in the photo, Angelina Jolie. WE SEE CAN HELP YOU In this impulsive type of seeing, called “bottom-up,” my eyes went first to the face of the other woman in the picture until I consciously ordered them to spend time on Jolie—“top-down TAKE MORE COMPELLING attentional deployment” in scientific lingo. That’s because, researchers have found, we recognize culture-defined beauty only PICTURES after taking the reflexive glances all primate eyes perform within the first few milliseconds. In other words, we see first with our BY NEAL MATTHEWS animal selves and then with our acculturated minds. We process visual information very quickly, as the brain electronically parcels parts of images to different cortical areas concerned with faces, colors, shapes, motion, and many other aspects of a scene, where they are broken down even further. Then the brain puts all that information back together into a coherent composite before directing the eyes to move. My eye tracks were being recorded by vision researcher Laurent Itti, an associate professor of computer science, psychology, and neuroscience at the University of Southern California’s iLab (ilab.usc.edu).
    [Show full text]
  • Neural Encoding with Visual Attention
    Neural encoding with visual attention Meenakshi Khosla1;∗, Gia H. Ngo1, Keith Jamison3, Amy Kuceyeski3;4 and Mert R. Sabuncu1;2;3 1 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853 2 Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853 3 Radiology, Weill Cornell Medicine, New York, NY 10065 4 Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065 ∗ Correspondence: [email protected] Abstract Visual perception is critically influenced by the focus of attention. Due to limited re- sources, it is well known that neural representations are biased in favor of attended locations. Using concurrent eye-tracking and functional Magnetic Resonance Imag- ing (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model. Next, we propose a novel approach to neural encoding by includ- ing a trainable soft-attention module. Using our new approach, we demonstrate that it is possible to learn visual attention policies by end-to-end learning merely on fMRI response data, and without relying on any eye-tracking. Interestingly, we find that attention locations estimated by the model on independent data agree well with the corresponding eye fixation patterns, despite no explicit supervision to do so. Together, these findings suggest that attention modules can be instrumental in neural encoding models of visual stimuli. 1 1 Introduction Developing accurate population-wide neural encoding models that predict the evoked brain response directly from sensory stimuli has been an important goal in computational neuroscience.
    [Show full text]
  • Feature-Based Attention Is Independent of Object Appearance
    Journal of Vision (2014) 14(1):3, 1–11 http://www.journalofvision.org/content/14/1/3 1 Feature-based attention is independent of object appearance Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Guobei Xiao Tongji University School of Medicine, Shanghai, China Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Guotong Xu Tongji University School of Medicine, Shanghai, China Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Xiaoqing Liu Tongji University School of Medicine, Shanghai, China Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Jingying Xu Tongji University School of Medicine, Shanghai, China Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Fang Wang Tongji University School of Medicine, Shanghai, China Department of Ophthalmology of Shanghai Tenth People’s Hospital and Tongji Eye Institute, Li Li Tongji University School of Medicine, Shanghai, China Computer Science Department and Neuroscience Graduate Laurent Itti Program, University of Southern California $ Department of Ophthalmology of Shanghai Tenth People’s Hospital, The Advanced Institute of Translational Medicine and School of Software Engineering, Jianwei Lu Tongji University, Shanghai, China $ How attention interacts with low-level visual luminance discrimination task on one side, and ignored representations to give rise to perception remains a the other side, where we probed for enhanced activity central yet controversial question in neuroscience. While when either it was perceived as belonging to a same several previous studies suggest that the units of object, or shared features with the task side.
    [Show full text]
  • The Seabee II: a Beosub Class
    AUVSI TECHNICAL REPORT, JUNE 2007 1 The SeaBee II: A BeoSub Class AUV Randolph Voorhies, Christopher Roth, Michael Montalbo, Andre Rosa, Andrew Chambers, Kevin Roth, Neil Bernardo, Christian Siagian, Laurent Itti Abstract—The SeaBee II is an autonomous underwater year’s design is the most promising AUV to come vehicle (AUV) designed and built by students at the out of USCR. University of Southern California to compete in the 10th annual International Autonomous Underwater Vehicle Competition hosted by AUVSI and ONR in July 2007. This years entry is USCR’s most ambitious project to date, featuring a powerful, actively water-cooled BeoWulf Class I computing cluster composed of two Intel Core 2 Duo main computers. The design also boasts a brand new lightweight hull, a highly configurable external rack, and a 5 thruster propulsion system allowing easy set up of mission-oriented payloads without the need for constant re-balancing. Index Terms—Autonomous Underwater Vehicle AUV Saliency Gist Vision Robotics XTX I. INTRODUCTION N August of 2006, the University of Southern I California Competition Robotics Team (USCR) - www.ilab.usc.edu/uscr - began the design pro- cess for a brand new autonomous underwater ve- hicle for the 10th AUVSI competition. This year’s competition offers a number of challenges beyond the scope of those of previous years. These new challenges include most notably the addition of a ”treasure” to be recovered from the bottom of the pool. We approached this year’s competition with II. MECHANICAL DESIGN the platform concept centered around the compu- tational workhorse of a Beowulf Class 1 cluster.
    [Show full text]
  • Laboratory Notes
    LABORATORY The iLab Neuromorphic Vision NOTES C++ Toolkit: Free tools for the next generation of vision algorithms Because of its truly interdisciplinary nature— stage relies on a neural saliency map, which toolkit for the implementation of the next benefiting from the latest advances in experi- gives a graded measure of ‘attractiveness’ to processing stage—concerned with identify- mental and computational neuroscience, elec- every location in the scene, and is modeled ing the object that has drawn the attention trical engineering, control theory, and signal on the neural architecture of the posterior and the eyes—and most of these models are and image processing—neuromorphic engi- parietal cortex in the monkey brain. At any inspired by the visual-response properties of neering is a very complex field. This has been neurons in the infero-temporal cortex. Finally, one of the leading motivations for the devel- additional modules are available for short- opment of a Neuromorphic Vision Toolkit term and long-term memory, cognitive at the iLab of the University of Southern knowledge representation, and modulatory California to provide a set of basic tools that feedback from a high-level task definition can assist newcomers in the field with the de- (e.g., look for the stop sign) to the low-level velopment of new models and systems. More visual processing (e.g., emphasize the con- generally, the iLab Neuromorphic Vision tribution of red to the saliency map, prime C++ Toolkit project aims at developing the the object recognition module for the ‘traffic next generation of vision algorithms, the ar- sign’ object class).
    [Show full text]
  • Collection Neuroscience 1995 - 2009
    Elsevier Sciverse Sciencedirect - Evidence-based Selection für die Universität Osnabrück Collection Neuroscience 1995 - 2009 Titel Autor Jahr URL Animal Models in Eye Research Panagiotis Antonios Tsonis 2008 http://www.sciencedirect.com/science/book/9780123741691 Boxing Friedrich Unterharnscheidt / Julia Taylor Unterharnscheidt 2003 http://www.sciencedirect.com/science/book/9780127091303 Brain Mapping: The Disorders John Mazziotta/Arthur Toga/Richard Frackowiak 2000 http://www.sciencedirect.com/science/book/9780124814608 Brain Mapping: The Methods Arthur Toga/John Mazziotta 2002 http://www.sciencedirect.com/science/book/9780126930191 Brain Mapping: The Systems Arthur Toga/John Mazziotta 2000 http://www.sciencedirect.com/science/book/9780126925456 Brain Warping Arthur Toga 1999 http://www.sciencedirect.com/science/book/9780126925357 Cognitive Electrophysiology of Mind and Brain, The Alberto Zani/Alice Proverbio 2003 http://www.sciencedirect.com/science/book/9780127754215 Cognitive Systems - Information Processing Meets Brain Science Richard Morris/Lionel Tarassenko/Michael Kenward 2006 http://www.sciencedirect.com/science/book/9780120885664 Consciousness and Cognition Henri Cohen, Brigitte Stemmer 2007 http://www.sciencedirect.com/science/book/9780123737342 Consciousness Transitions Hans Liljenström, Peter Århem 2007 http://www.sciencedirect.com/science/book/9780444529770 Eye Movement Research J.M. Findlay, R. Walker, R.W. Kentridge 1995 http://www.sciencedirect.com/science/publication?issn=0926907X&volume=6 From Molecules to Networks
    [Show full text]
  • Mining Videos for Features That Drive Attention
    Chapter 14 Mining Videos for Features that Drive Attention Farhan Baluch and Laurent Itti Abstract Certain features of a video capture human attention and this can be measured by recording eye movements of a viewer. Using this technique combined with extraction of various types of features from video frames, one can begin to understand what features of a video may drive attention. In this chapter we define and assess different types of feature channels that can be computed from video frames, and compare the output of these channels to human eye movements. This provides us with a measure of how well a particular feature of a video can drive attention. We then examine several types of channel combinations and learn a set of weightings of features that can best explain human eye movements. A linear combination of features with high weighting on motion and color channels was most predictive of eye movements on a public dataset. 14.1 Background Videos are made up of a stream of running frames each of which has a unique set of spatial and textural features that evolve over time. Each video therefore presents a viewer with a large amount of information to process. The human visual system has limited capacity and evolution has incorporated several mechanisms into the visual processing systems of animals and humans to allow only the most important and behaviorally relevant information to be passed on for further processing. The first stage is the limited amount of high resolution area in the eye, i.e., the fovea. When we want to focus on a different spatial region of a video we make an eye movement, also F.
    [Show full text]
  • NSF Workshop on Hybrid Neuro- Computer Vision Systems
    NSF Workshop on Hybrid Neuro- Computer Vision Systems April 19-20, 2010 New York City Organizers: Shih-Fu Chang and Paul Sajda 1 NSF HNCV10 Workshop Objectives • Facilitate interaction among experts from neuroscience, computer vision, and brain machine interface • Explore new applications and requirements • Hybrid vision system designs that exploit synergistic combinations of neural and computer vision • Recommend future directions and programs NSF HNCV10 Speakers and Sponsors Neuroscience and Neural Computing Government Representatives • Garrett Stanley, Biomedical Engineering, Georgia Tech. • Qiang Ji, NSF (Workshop Sponsor) • Chris Rozell, Electrical Computer Engineering, Georgia Tech • Liyi Dai, Army • Charles Cadieu, Neural Science, UC Berkeley • Tom Nugent III , DARPA • Paul Sajda, Biomedical Engineering, Columbia U. • Dan Purdy , DARPA • Jack Gallant, Psychology, UC Berkeley • Grace Rigdon , DARPA • Frank Tong, Psychology, Vanderbilt U. • Wendell G Sykes , DARPA • Philippe Schyns, Psychology, U. of Glasgow Neural-Inspired Computer Vision Industry Participants: • Thomas Serre, Cognitive and Linguistic Science, Brown U. • Sarnoff • Yann LeCun, Computer Science and Neural Science, NYU • Google • Aude Oliva, Brain and Cognitive Science, MIT • IBM • Li Fei-Fei, Computer Science, Stanford U. • AT&T • Antonio Torralba, Computer Science, MIT • Neuromatters • Laurent Itti, Computer Science, Psychology and Neuroscience, USC • Total Immersion Software Hybrid Vision Systems and Applications • Others • Amy Kruse, Neural Science, Total Immersion
    [Show full text]
  • Visual Attention and Target Detection in Cluttered Natural Scenes
    Visual attention and target detection in cluttered natural scenes Laurent Itti Abstract. Rather than attempting to fully interpret visual scenes in a University of Southern California parallel fashion, biological systems appear to employ a serial strategy by Computer Science Department which an attentional spotlight rapidly selects circumscribed regions in the Hedco Neuroscience Building scene for further analysis. The spatiotemporal deployment of attention 3641 Watt Way, HNB-30A has been shown to be controlled by both bottom-up (image-based) and Los Angeles, California 90089-2520 top-down (volitional) cues. We describe a detailed neuromimetic com- E-mail: [email protected] puter implementation of a bottom-up scheme for the control of visual attention, focusing on the problem of combining information across mo- Carl Gold dalities (orientation, intensity, and color information) in a purely stimulus- Christof Koch driven manner. We have applied this model to a wide range of target California Institute of Technology detection tasks, using synthetic and natural stimuli. Performance has, Computation and Neural Systems however, remained difficult to objectively evaluate on natural scenes, Program because no objective reference was available for comparison. We Mail-Code 139-74 present predicted search times for our model on the Search–2 database Pasadena, California 91125 of rural scenes containing a military vehicle. Overall, we found a poor correlation between human and model search times. Further analysis, however, revealed that in 75% of the images, the model appeared to detect the target faster than humans (for comparison, we calibrated the model’s arbitrary internal time frame such that 2 to 4 image locations were visited per second).
    [Show full text]
  • Computational Models: Bottom-Up and Top-Down Aspects
    Computational models: Bottom-up and top-down aspects Laurent Itti and Ali Borji, University of Southern California 1 Preliminaries and definitions Computational models of visual attention have become popular over the past decade, we believe primarily for two reasons: First, models make testable predictions that can be explored by experimentalists as well as theoreticians; second, models have practical and technological applications of interest to the applied science and engineering communities. In this chapter, we take a critical look at recent attention modeling efforts. We focus on computational models of attention as defined by Tsotsos & Rothenstein (2011): Models which can process any visual stimulus (typically, an image or video clip), which can possibly also be given some task definition, and which make predictions that can be compared to human or animal behavioral or physiological responses elicited by the same stimulus and task. Thus, we here place less emphasis on abstract models, phenomenological models, purely data-driven fitting or extrapolation models, or models specifically designed for a single task or for a restricted class of stimuli. For theoretical models, we refer the reader to a number of previous reviews that address attention theories and models more generally (Itti & Koch, 2001a; Paletta et al., 2005; Frintrop et al., 2010; Rothenstein & Tsotsos, 2008; Gottlieb & Balan, 2010; Toet, 2011; Borji & Itti, 2012b). To frame our narrative, we embrace a number of notions that have been popularized in the field, even though many of them are known to only represent coarse approximations to biophysical or psychological phenomena. These include the attention spotlight metaphor (Crick, 1984), the role of focal attention in binding features into coherent representations (Treisman & Gelade, 1980a), and the notions of an attention bottleneck, a nexus, and an attention hand as embodiments of the attentional selection process (Rensink, 2000; Navalpakkam & Itti, 2005).
    [Show full text]
  • Data Sharing for Computational Neuroscience
    Neuroinform DOI 10.1007/s12021-008-9009-y Data Sharing for Computational Neuroscience Jeffrey L. Teeters & Kenneth D. Harris & K. Jarrod Millman & Bruno A. Olshausen & Friedrich T. Sommer Received: 9 January 2008 /Accepted: 10 January 2008 # Humana Press Inc. 2008 Abstract Computational neuroscience is a subfield of which will be publicly available in 2008. These include neuroscience that develops models to integrate complex electrophysiology and behavioral (eye movement) data experimental data in order to understand brain function. To described towards the end of this article. constrain and test computational models, researchers need access to a wide variety of experimental data. Much of Keywords Computational neuroscience . Data sharing . those data are not readily accessible because neuroscientists Electrophysiology . Online data repositories . Computational fall into separate communities that study the brain at models . Hippocampus . Sensory systems . Eye movements . different levels and have not been motivated to provide Visual cortex data to researchers outside their community. To foster sharing of neuroscience data, a workshop was held in 2007, bringing together experimental and theoretical neuroscient- Introduction ists, computer scientists, legal experts and governmental observers. Computational neuroscience was recommended Many aspects of brain function that are currently unex- as an ideal field for focusing data sharing, and specific plained could potentially be understood if a culture of data methods, strategies and policies were suggested for achiev- sharing were established in neuroscience. The fields of ing it. A new funding area in the NSF/NIH Collaborative computational neuroscience and systems neuroscience seek Research in Computational Neuroscience (CRCNS) pro- to elucidate the information processing strategies employed gram has been established to support data sharing, guided by neural circuits in the brain.
    [Show full text]
  • Biologically-Inspired Robotics Vision Monte-Carlo Localization in the Outdoor Environment
    Biologically-Inspired Robotics Vision Monte-Carlo Localization in the Outdoor Environment Christian Siagian and Laurent Itti Abstract— We present a robot localization system using simultaneous localization and mapping (SLAM) [12], [13], biologically-inspired vision. Our system models two extensively [14] is an active branch of robotics research. studied human visual capabilities: (1) extracting the “gist” of One additional categorization to consider here comes from a scene to produce a coarse localization hypothesis, and (2) refining it by locating salient landmark regions in the scene. the vision perspective, which classifies systems according to Gist is computed here as a holistic statistical signature of the the type of visual features used: local features and global image, yielding abstract scene classification and layout. Saliency features. Local features are computed over a limited area of is computed as a measure of interest at every image location, the image, as opposed to global features which may pool efficiently directing the time-consuming landmark identification information over the entire image into, e.g., histograms. Be- process towards the most likely candidate locations in the image. The gist and salient landmark features are then further fore analyzing the variety of approaches, which by no means processed using a Monte-Carlo localization algorithm to allow is exhaustive, it should be pointed out that, like other vision the robot to generate its position. We test the system in three problems, any localization and landmark recognition system different outdoor environments - building complex (126x180ft. faces the general issues of occlusion, dynamic background, area, 3794 testing images), vegetation-filled park (270x360ft. lighting, and viewpoint changes.
    [Show full text]