DOCSLIB.ORG

Gist: a Mobile Robotics Application of Context-Based Vision in Outdoor Environment

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gist: a Mobile Robotics Application of Context-Based Vision in Outdoor Environment Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment Christian Siagian Laurent Itti Department of Computer Science Department of Computer Science University of Southern California University of Southern California Los Angeles, CA 90089 Los Angeles, CA 90089 Abstract Another way to obtain navigational input is by using the primary sensory modality in human: vision. Within the We present context-based scene recognition for mobile Computer Vision field, a large portion of approach towards robotics applications. Our classifier is able to differenti- scene recognition is object-based [1, 24, 9]. That is, a phys- ate outdoor scenes without temporal filtering relatively well ical location is recognized by identifying surrounding ob- from a variety of locations at a college campus using a set jects (and their configuration) as landmarks. This approach of features that together capture the “gist” of the scene. We involves intermediate steps such as segmentation, feature compare the classification accuracy of a set of scenes from grouping, and object detection. The last step can be com- 1551 frames filmed outdoors along a path and dividing them plex because the necessity of object matching from multi- to four and twelve different legs while obtaining a classifi- ple views [11, 10]. It should be pointed out that this ap- cation rate of 67.96 percent and 48.61 percent, respectively. proach is mostly used indoors for the simplicity of selecting We also tested the scalability of the features by comparing a small set of anchor objects that keep re-occuring in the en- the classification results from the previous scenes with four vironment. In the generally spacious outdoors, objects tend legs with a longer path with eleven legs while obtaining a to be farther away from each other. Because of inherently classification rate of 55.08 percent. In the end, some ideas noisy camera data - objects are more obscured and smaller are put forth to improve the theoretical strength of the gist in comparison to image size - such layered approach pro- features. duces errors that are carried over and amplified along the stream of processing. 1. Introduction A different set of approaches within the Vision domain looks for regions and their relationship in an image as a sig- The field of mobile robotics, which hinges on solving tasks nature of a location. Katsura [6] utilize top-down knowl- such as localization, mapping, and navigation, can be sum- edge for recognizing the regions such as the sky being at marized into one central question: Where are we? This the top part of an image and buildings at the east and west fundamental problem can be approached from several an- of the frame. A scalability problem arises from the inability gles. A significant number of mobile robotics implementa- to chracterize the region more than simply its centroid. Mat- tions use sonar, laser, or other range sensors [3, 7, 23]. In sumoto [10] uses template-matching to recognize a set of re- the outdoors these sensors become less effective in solving gions and, to a degree, encapsulates a richer set of features. data association problem because, unlike the indoor envi- However, the need for pixel-wise comparison may be too ronment, the existence of spatial simplifications such as flat specific to allow flexibility of different views of the same walls and narrow corridors cannot be assumed. Contrasting location. Murrieta-Cid [11] goes a step further in that it uses with the regularity of indoor scenes, the surface shape of the regions, particularly isolated-blob region, as an interme- the outdoors varies tremendously, especially when consid- diate step to locate predetermined set of anchor landmarks. ering environments with different terrains. It is very hard Here, there is a need for robust segmentation phase as well to predict the sensor input given all the protrusions and sur- as identification of landmarks from multiple views. face irregularities[8]. A slight pose change can result in a The context-based approach, on the other hand, bypasses large jump in range reading because of tree trunks, moving the traditional processing steps. Instead, it analyzes the branches and leaves. In addition, a flat surface for a robot scene as a whole and extract a low-dimensional signature to travel is an absolute must, otherwise we introduce a third for it. This signature in the form of a scene-level feature (and mostly unaccountable) dimension. vector embodies the cognitive psychologists’ idea of the 1 “gist” of a scene [17]. The hypothesis at the foundation of ponency features. We incorporate information from the ori- this technique is that it should produce a more robust solu- entation channel, which uses Gabor filters, at four different tion, because random noise that locally may be catastrophic angles and at four spatial scales for a subtotal of sixteen tends to average out globally. By identifying scenes, and sub-channels. The color channel (in two color opponency: not objects, we do not have to deal with noise in isolated re- red-green and blue-yellow) is composed of twelve differ- gions. Our approach, which is biologically inspired, mim- ent center-surround scale combination while the intensity ics the ability of human vision to collect coarse yet concise channel (dark-bright opponency) is composed of six differ- contextual information about an image in a short amount of ent center-surround scale combinations. That is a total of time [16, 27, 22]. This gist information includes a rough thirty-four sub-channels altogether. The following image 1 spatial layout [12], but often lacks in fine-scale detail about illustrates the features used by the gist model. specific objects [13]. Gist can be identified in as little as 45 - 135 ms [19], faster than a single saccadic eye movement. Input image Such quick turnaround time is remarkable considering that it extracts quintessential characteristics of an image which Linear filtering at 8 spatial scales can be useful for tasks such as semantic scene categoriza- tion (e.g., indoors vs. outdoors; beach vs. city), scale selec- colors intensity orientations tion, region priming and layout recognition [25]. R, G, B, Y ON, OFF 0, 45, 90, 135Ê The challenge to discover a compact and holistic repre- sentation has been a research of various works. Renniger Center-surround differences and normalization [18] and Malik use a set of texture information and keep track of them using a histogram to create an overall pro- 12 (RG and BY) 6 On-Off 16 Gabor Feature Feature Filter Vectors Vectors Feature file of an image. Ulrich and Nourbakhsh [28] build color Vectors histogram and perform matching using a voting procedure. Gist Feature Vectors Although an approach by Takeuchi [21] is meant to look for red buildings, the actual implementation uses a histogram of red texture pixels. In contrast to the previous approaches, PCA/ICA Dimension Reduction which does not encode spatial information, Oliva [15] and Torralba performs Fourier Transform analysis to individ- ual sub-region divided using a regularly-spaced grid, which Place Classifier then is correlated to several scene categories. Later on Tor- ralba [26] uses steerable wavelet pyramid in the same man- Most Likely Location ner. The core of our present research focuses on a process of extracting the gist features of an image from several do- Figure 1: Visual Feature Channel Used in the Gist Model mains that do not focus on specific locations of the image but still take into account a coarse spatial information. For each of the thirty-four sub-channels, we extract the corresponding gist vector of 21 features from its filter out- 2. Design and Implementation put/feature map. We hypothesize that, because of the speed in which gist is calculated, they come from a series of sim- Part of our contribution is that we present a more biolog- ple feedforward operations. We thought that accumulation ically plausible model which utilizes the rich image fea- or averaging operations (as oppose to competition) are more tures from the visual cortex. The gist model is built us- plausible in finding general information of a set of values. ing the Vision toolkit developed by Itti at al. [4] which Our 21 gist features can be visualized by a pyramid of sub- feature the Saliency model and can be freely downloaded section average-outputs (figure 2). The first, the tip of the on the web. In the Saliency model, the image is processed pyramid, represents the mean of the entire feature map. The through a number of low-level visual “channels” at mul- next four values are means of each of the quarter sub-region tiple spatial scales. Within each channel, the model per- of the map - delineated by an evenly spaced two-by-two forms a center-surround operations between different scales grid. The last sixteen values are the means of each of the to detect conspicuous regions for that channel. These re- sub-region delineated by a four-by-four grid. This approach sults then is linearly combined to yield a saliency map. is similar to Torralba [26] with the use of wavelet pyramid. One of the goals here is to re-use the same intermediate The number of features could be increased by adding other maps, so that gist is computed almost for free once we domains such as motion and stereo, or using even smaller have all the maps for attention. Our gist model make use sub-regions (the next level would be an eight-by-eight grid: of the already available orientation, color and intensity op- an additional 64 features) for more localized gist informa- 2 tion. (SAL).” The path takes place in an outdoor environment that expands several city blocks.
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]