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检索条件"任意字段=Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques"

共 664 条记录，以下是231-240 订阅

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Visual terrain mapping for traversable path planning of mobile robots

Visual terrain mapping for traversable path planning of mobi...

引用

Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Shirkhodaie, A Amrani, R Tunstel, E Tennessee State Univ Dept Mech & Mfg Engn Intelligent Tact Mobil Robot Lab Nashville TN 37209 USA

ISBN: (纸本)081945561X

In this paper, we have primarily discussed technical challenges and navigational skill requirements of mobile robots for traversability path planning in natural terrain environments similar to Mars surface terrains. We have described different methods for detection of salient terrain features based on imaging texture analysis techniques. We have also presented three competing techniques for terrain traversability assessment of mobile robots navigating in unstructured natural terrain environments. These three techniques include: a rule-based terrain classifier, a neural network-based terrain classifier, and a fuzzy-logic terrain classifier. Each proposed terrain classifier divides a region of natural terrain into finite sub-terrain regions and classifies terrain condition exclusively within each sub-terrain region based on terrain visual clues. The Kalman Filtering technique is applied for aggregative fusion of sub-terrain assessment results. The last two terrain classifiers are shown to have remarkable capability for terrain traversability assessment of natural terrains. We have conducted a comparative performance evaluation of all three terrain classifiers and presented the results in this paper.

关键词： terrain traversability assessment modeling visual terrain mapping image texture analysis

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Integration of image/video understanding engine into 4D/RCS architecture for intelligent perception-based behavior of robots in real-world environments

Integration of image/video understanding engine into 4D/RCS ...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Kuvich, G Smart Computer Vision Systems (United States)

ISBN: (纸本)081945561X

To be completely successful, robots need to have reliable perceptual systems that are similar to human vision. It is hard to use geometric operations for processing of natural images. Instead, the brain builds a relational network-symbolic structure of visual scene, using different clues to set up the relational order of surfaces and objects with respect to the observer and to each other. Feature, symbol, and predicate are equivalent in the biologically inspired Network-Symbolic systems. A linking mechanism binds these features/symbols into coherent structures, and image converts from a "raster" into a "vector" representation. View-based object recognition is a hard problem for traditional algorithms that directly match a primary view of an object to a model. In Network-Symbolic Models, the derived structure, not the primary view, is a subject for recognition. Such recognition is not affected by local changes and appearances of the object as seen from a set of similar views. Once built, the model of visual scene changes slower then local information in the visual buffer. It allows for disambiguating visual information and effective control of actions and navigation via incremental relational changes in visual buffer. Network-Symbolic models can be seamlessly integrated into the NIST 4D/RCS architecture and better interpret images/video for situation awareness, target recognition, navigation and actions.

关键词： image video understanding network symbolic systems active vision UGV 4D/RCS

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Self-calibration algorithms for cameras and laser range finders

Self-calibration algorithms for cameras and laser range find...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Zhang, QL Pless, R Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

ISBN: (纸本)081945561X

Autonomous systems that navigate through unknown and unstructured environments must solve the egomotion estimation problem. Fusing the information from many different sensors makes this motion estimation more stable, but requires that the relative position and orientation of these sensors be known. Self-calibration algorithms are the most useful for this calibration problem because the do not require any known feature in the environment and can be used during system operation. Here we give geometric constraints, the coherent motion constraints, that allow a framework for the development of self-calibration algorithms for a heterogeneous sensor system (such as cameras, laser range finders, and odometry). If, for all sensors, a conditional probability density function can be defined to relate sensor measurements to the sensor motion, then the coherent motion constraints allows a maximum likelihood formulation of the sensor calibration problem. We present complete algorithms here for the case of a camera and laser range finder, in the case of both discrete and differential motions.

关键词： Sensors Cameras Calibration Motion estimation Laser range finders Motion measurement Imaging systems Algorithm development robots Sensor calibration

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Autonomous robot vision software design using Matlab toolboxes

Autonomous robot vision software design using Matlab toolbox...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Tedder, M Chung, CJ Lawrence Technol Univ Southfield MI 48075 USA

ISBN: (纸本)081945561X

The purpose of this paper is to introduce a cost-effective way to design robot vision and control software using Matlab for an autonomous robot designed to compete in the 2004 intelligent Ground Vehicle Competition (IGVC). The goal of the autonomous challenge event is for the robot to autonomously navigate an outdoor obstacle course bounded by solid and dashed lines on the ground. Visual input data is provided by a DV camcorder at 160 x 120 pixel resolution. The design of this system involved writing an image-processing algorithm using hue, satuaration, and brightness (HSB) color filtering and Matlab image processing functions to extract the centroid, area, and orientation of the connected regions from the scene. These feature vectors are then mapped to linguistic variables that describe the objects in the world environment model. The linguistic variables act as inputs to a fuzzy logic controller designed using the Matlab fuzzy logic toolbox, which provides the knowledge and intelligence component necessary to achieve the desired goal. Java provides the central interface to the robot motion control and image acquisition components. Field test results indicate that the Matlab based solution allows for rapid software design, development and modification of our robot system.

关键词： computer vision algorithms autonomous mobile robots obstacle avoidance fuzzy logic neural networks

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Bayesian stereo: 3D vision designed for sensor fusion

Bayesian stereo: 3D vision designed for sensor fusion

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Larson, J Pless, R Washington Univ St Louis MO 63130 USA

ISBN: (纸本)081945561X

Classical stereo algorithms attempt to reconstruct 3D models of a scene by matching points between two images. Finding points that match is an important part of this process, and point matches are most commonly chosen as the minimum of an error function based on color or local texture. Here we motivate a probabilistic approach to this point matching problem, and provide an experimental design for the empirical measurement of the color matching error for corresponding points. We use this prior in a Bayesian scene reconstruction example, and show that we get better 3D reconstruction by not committing to a specific pixel match early in the visual processing. This allows a calibrated stereo camera to be considered as a probabilistic volume sensor --- which allows it to be more easily integrated with scene structure measurements from other kinds of sensors.

关键词： stereo Bayesian probabilistic sensor fusion

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The 12^TH annual intelligent ground vehicle competition:: Team approaches to intelligent vehicles

The 12<SUP>TH</SUP> annual intelligent ground vehicle compet...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Theisen, BL Maslach, D USA Tank Automat Res Dev & Engn Ctr Warren MI 48397 USA

ISBN: (纸本)081945561X

The intelligent Ground Vehicle Competition (IGVC) is one of three, unmanned systems, student competitions that were founded by the Association for Unmanned Vehicle Systems International (AUVSI) in the 1990s. The IGVC is a multidisciplinary exercise in product realization that challenges college engineering student teams to integrate advanced control theory, machine vision, vehicular electronics, and mobile platform fundamentals to design and build an unmanned system. Both U.S. and international teams focus on developing a suite of dual-use technologies to equip ground vehicles of the future with intelligent driving capabilities. Over the past 12 years, the competition has challenged undergraduate, graduate and Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing automation. To date, teams from over 43 universities and colleges have participated. This paper describes some of the applications of the technologies required by this competition and discusses the educational benefits. The primary goal of the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and professional networking opportunities created for students and industrial sponsors through a series of technical events over the three-day competition are highlighted. Finally, an assessment of the competition based on participant feedback is presented.

关键词： intelligent robots autonomous systems ground vehicles engineering education IGVC

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computer vision inspection of rice seeds quality with discriminant analysis

Computer vision inspection of rice seeds quality with discri...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Cheng, F Ying, YB Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Peoples R China

ISBN: (纸本)081945561X

This study was undertaken to develop computer vision-based rice seeds inspection technology for quality control. Color image classification using a discriminant analysis algorithm identifying germinated rice seed was successfully implemented. The hybrid rice seed cultivars involved were Jinyou402, Shanyou10, Zhongyou207 and Jiayou99. Sixteen morphological features and six color features were extracted from sample images belong to training sets. The color feature of 'Huebmean' shows the strongest classification ability among all the features. Computed as the area of seed region divided by area of the smallest convex polygon that can contain the seed region, the feature of 'Solidity' is prior to the other morphological features in germinated seeds recognition. Combined with the two features of 'Huebmean' and 'Solidity', discriminant analysis was used to classify normal rice seeds and seeds germinated on panicle. Results show that the algorithm achieved an overall average accuracy of 98.4% for both of normal seeds and germinated seeds in all cultivars. The combination of 'Huebmean' and 'Solidity' was proved to be a good indicator for germinated seeds. The simple discriminant algorithm using just two features shows high accuracy and good adaptability.

关键词： computer vision rice seeds germinated discriminant analysis

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Camera localization methods for intelligent room systems using RF techniques

Camera localization methods for intelligent room systems usi...

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Intellignt robots and computer vision XXII: algorithms, techniques, and Active vision

作者： Natarajan, Pradeep Mundhenk, T. Nathan Bellman, K. Itti, L. Arbib, M. Computer Science Department Henri Salvatori Comp. Science Center University of Southern California Los Angeles CA 90089-0781 Neuroscience Program Hedco Neuroscience Building University of Southern California Los Angeles CA 90089-2520 Aerospace Integration Science Center Aerospace Corporation 2350 E. El Segundo Blvd. El Segundo CA 90245-4691

One of the important components of a multi sensor "intelligent" room, which can observe, track and react to its occupants, is a multi camera system. This system involves the development of algorithms that enable a set of cameras to communicate and cooperate with each other effectively so that they can monitor the events happening in the room. To achieve this, the cameras typically must first build a map of their relative locations. In this paper, we discuss a novel RF based technique for estimating distances between cameras. The algorithm proposed for RF can estimate distances with relatively good accuracy even in the presence of random noise.

关键词： Artificial intelligence

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Citrus fruit recognition using color image analysis

Citrus fruit recognition using color image analysis

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Xu, HR Ying, YB Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310029 Peoples R China

ISBN: (纸本)081945561X

An algorithm for the automatic recognition of citrus fruit on the tree was developed. Citrus fruits have different color with leaves and branches portions. Fifty-three color images with natural citrus-grove scenes were digitized and analyzed for red, green, and blue (RGB) color content. The color characteristics of target surfaces (fruits, leaves, or branches) were extracted using the range of interest (ROI) tool. Several types of contrast color indices were designed and tested. In this study, the fruit image was enhanced using the (R-B) contrast color index because results show that the fruit have the highest color difference among the objects in the image. A dynamic threshold function was derived from this color model and used to distinguish citrus fruit from background. The results show that the algorithm worked well under frontlighting or backlighting condition. However, there are misclassifications when the fruit or the background is under a brighter sunlight.

关键词： machine vision citrus detection color indices dynamic thresholdng image processing

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Surface roughness estimation of shot blasted steel bars using machine vision

Surface roughness estimation of shot blasted steel bars usin...

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Conference on intelligent robots and computer vision XXII - algorithms, techniques and Active vision

作者： Lydén, S Kälviäinen, H Nykänen, J Lappeenranta Univ Technol Dept Informat Proc FIN-53851 Lappeenranta Finland Imatra Steel Imatra FIN-55100 Imatra Finland

ISBN: (纸本)081945561X

During the manufacturing process steel bars are cleaned of roll scale by shot blasting, before further processing the bars by drawing. The main goal of this project is to increase the automation of the shot blasting process by machine vision. For this purpose a method is needed for estimating the surface roughness and other anomalies from the steel bars from digital images after the shot blasting. The goal of this method is to estimate if the quality of shot blasting is sufficient considering the quality of the final products after the drawing. In this project a method for normalising the images is considered and several methods for estimating the actual roughness level are experimented. During the experiments a best method was one where the roughness levels are calculated directly from the images as if the images were similar to other measuring sources and the grey-level values in the images represent the deviation on the bar surface. This at least separates the different samples.

关键词： shot blasting steel bars surface roughness image processing machine vision differential evolution

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