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

共 375 条记录，以下是221-230 订阅

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Panoramic Stereo Sphere vision

Panoramic Stereo Sphere Vision

引用

conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Feng, Weijia Zhang, Baofeng Roning, Juha Zong, Xiaoning Yi, Tian Tianjin Normal Univ Coll Comp & Informat Engn Tianjin Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Appl Complied In Tianjin Peoples R China Univ Oulu Dept Comp Sci & Engn SF-90100 Oulu Finland

ISBN: (纸本)9780819494351

Conventional stereo vision systems have a small field of view (FOV) which limits their usefulness for certain applications. While panorama vision is able to "see" in all directions of the observation space, scene depth information is missed because of the mapping from 3D reference coordinates to 2D panoramic image. In this paper, we present an innovative vision system which builds by a special combined fish-eye lenses module, and is capable of producing 3D coordinate information from the whole global observation space and acquiring no blind area 360 degrees x360 degrees panoramic image simultaneously just using single vision equipment with one time static shooting. It is called Panoramic Stereo Sphere vision (PSSV). We proposed the geometric model, mathematic model and parameters calibration method in this paper. Specifically, video surveillance, robotic autonomous navigation, virtual reality, driving assistance, multiple maneuvering target tracking, automatic mapping of environments and attitude estimation are some of the applications which will benefit from PSSV.

关键词： Panoramic Stereo Sphere vision fish-eye lens parameters calibration

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Embodying a cognitive model in a mobile robot

Embodying a cognitive model in a mobile robot

引用

conference on intelligent robots and computer vision XXIV - algorithms, techniques, and Active vision

作者： Benjamin, D. Paul Lyons, Damian Lonsdale, Deryle Pace Univ Dept Comp Sci 1 Pace Plaza New York NY 10038 USA Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Brigham Young Univ Dept Linguist & English Language Provo UT 84602 USA

ISBN: (纸本)0819464821

The ADAPT project is a collaboration of researchers in robotics, linguistics and artificial intelligence at three universities to create a cognitive architecture specifically designed to be embodied in a mobile robot. There are major respects in which existing cognitive architectures are inadequate for robot cognition. In particular, they lack support for true concurrency and for active perception. ADAPT addresses these deficiencies by modeling the world as a network of concurrent schemas, and modeling perception as problem solving. Schemas are represented using the RS (Robot Schemas) language, and are activated by spreading activation. RS provides a powerful language for distributed control of concurrent processes. Also, The formal semantics of RS provides the basis for the semantics of ADAPT's use of natural language. We have implemented the RS language in Soar, a mature cognitive architecture originally developed at CMU and used at a number of universities and companies. Soar's subgoaling and learning capabilities enable ADAPT to manage the complexity of its environment and to learn new schemas from experience. We describe the issues faced in developing an embodied cognitive architecture, and our implementation choices.

关键词： mobile robotics cognitive architecture predictive vision learning robot schemas ADAPT

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Knowledge & vision engines: A new generation of image understanding systems, combining computational intelligence methods and model-based knowledge representation and reasoning

Knowledge & vision engines: A new generation of image unders...

引用

conference on intelligent robots and computer vision XIX: algorithms, techniques, and Active vision

作者： Kuvychko, I

ISBN: (纸本)0819438626

vision is a part of a larger informational system that converts visual information into knowledge structures. These structures drive vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, that is an interpretation of visual information in terms of such knowledge models. The solution to Image Understanding problems is suggested in form of active multilevel hierarchical networks represented dually as discrete and continuous structures. Computational intelligence methods transform images into model-based knowledge representation. Certainty Dimension converts attractors in neural networks into fuzzy sets, preserving input-output relationships. Symbols naturally emerge in such networks. Symbolic Space is a dual structure that combines closed distributed space split by the set of fuzzy regions, and discrete set of symbols equivalent to the cores of regions represented as points in the Certainty dimension. Model Space carries knowledge in form of links and relations between the symbols, and supports graph, diagrammatic and topological operations. Composition of spaces works similar to M. Minsky frames and agents, Gerard Edelman's "maps of maps", etc., combining machine learning, classification and analogy together with induction, deduction and other methods of higher level model-based reasoning. Based on such principles, an Image Understanding system can convert images into knowledge models, effectively resolving uncertainty and ambiguity via feedback projections and does not require supercomputers.

关键词： image understanding spatial turing machine computational intelligence knowledge & vision engines

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LABRADOR: A Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval

LABRADOR: A Learning Autonomous Behavior-based Robot for Ada...

引用

conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Yamauchi, Brian Moseley, Mark Brookshire, Jonathan iRobot Corp 8 Crosby Dr Bedford MA 01730 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9780819494351

As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

关键词： CANINE mobile robotics computer vision machine learning navigation human-robot interaction

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Decentralized tracking of humans using a camera network

Decentralized tracking of humans using a camera network

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conference on intelligent robots and computer vision XXIX - algorithms and techniques

作者： Gruenwedel, Sebastian Jelaca, Vedran Nino-Castaneda, Jorge Oswaldo Van Hese, Peter Van Cauwelaert, Dimitri Veelaert, Peter Philips, Wilfried Ghent Univ TELIN IPI IBBT B-9000 Ghent Belgium

ISBN: (纸本)9780819489487

Real-time tracking of people has many applications in computer vision and typically requires multiple cameras;for instance for surveillance, domotics, elderly-care and video conferencing. However, this problem is very challenging because of the need to deal with frequent occlusions and environmental changes. Another challenge is to develop solutions which scale well with the size of the camera network. Such solutions need to carefully restrict overall communication in the network and often involve distributed processing. In this paper we present a distributed person tracker, addressing the aforementioned issues. Real-time processing is achieved by distributing tasks between the cameras and a fusion node. The latter fuses only high level data based on low-bandwidth input streams from the cameras. This is achieved by performing tracking first on the image plane of each camera followed by sending only metadata to a local fusion node. We designed the proposed system with respect to a low communication load and towards robustness of the system. We evaluate the performance of the tracker in meeting scenarios where persons are often occluded by other persons and/or furniture. We present experimental results which show that our tracking approach is accurate even in cases of severe occlusions in some of the views.

关键词： computer vision decentralized architecture sensor fusion target tracking multi-camera tracking occupancy mapping

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Training industrial robots with gesture recognition techniques

Training industrial robots with gesture recognition techniqu...

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conference on intelligent robots and computer vision XXX - algorithms and techniques

作者： Piane, Jennifer Raicu, Daniela Furst, Jacob Yaskawa Amer Inc Waukegan IL 60085 USA Depaul Univ Chicago IL 60604 USA

ISBN: (纸本)9780819494351

In this paper we propose to use gesture recognition approaches to track a human hand in 3D space and, without the use of special clothing or markers, be able to accurately generate code for training an industrial robot to perform the same motion. The proposed hand tracking component includes three methods: a color-thresholding model, naive Bayes analysis and Support Vector Machine (SVM) to detect the human hand. Next, it performs stereo matching on the region where the hand was detected to find relative 3D coordinates. The list of coordinates returned is expectedly noisy due to the way the human hand can alter its apparent shape while moving, the inconsistencies in human motion and detection failures in the cluttered environment. Therefore, the system analyzes the list of coordinates to determine a path for the robot to move, by smoothing the data to reduce noise and looking for significant points used to determine the path the robot will ultimately take. The proposed system was applied to pairs of videos recording the motion of a human hand in a, real. environment to move the end-affector of a SCARA robot along the same path as the hand of the person in the video. The correctness of the robot motion was determined by observers indicating that motion of the robot appeared to match the motion of the video.

关键词： Naive Bayes Support Vector Machine Stereo matching Gesture recognition Industrial Robot

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Learning for intelligent mobile robots

Learning for intelligent mobile robots

引用

conference on intelligent robots and computer vision XXI

作者： Hall, EL Liao, X Ali, SMA Univ Cincinnati Ctr Robot Res Cincinnati OH 45221 USA

ISBN: (纸本)081945155X

Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot's motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is "beyond the adaptive critic." A

关键词： intelligent robots adaptive control robust control reinforcement learning adaptive critic

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Genetic feature selection for highly-accurate stereo reconstruction of natural surfaces

Genetic feature selection for highly-accurate stereo reconst...

引用

conference on intelligent robots and computer vision XVII - algorithms, techniques, and Active vision

作者： Paar, G Sidla, O Polzleitner, W Joanneum Res Graz Austria

ISBN: (纸本)081942983X

One approach to stereo matching is to use different local features to find correspondences. The selection of an optimum feature set is the content of this paper. An operational software tool based on the principle of comparing feature vectors is used for stereo matching. A relatively large set of different local features is sought for optimum combinations of 6-10 of them. This is done by a genetic process that uses an intrinsic quality criterion that evaluates the correctness of each individual match. The convergence of the genetic feature selection process is demonstrated on a real stereo pair of a tunnel surface. Four areas were used for individual optimization. After several hundred generations for each of the areas, it is shown that the identified feature sets result in a considerably better stereo matching result than the currently used features, which were the result of an initial manual choice. The experiments described in this paper use a "super-set" of 145 features for every pixel, which are created by filtering the image with convolution kernels (averaging, Gaussian filters, bandpass, highpass), median filters and Gabor kernels. From these 145 filters, the genetic feature selection process selects an optimal set of operators. Using the selected filters results in a 15% improvement of the matching accuracy and robustness.

关键词： stereo matching 3D reconstruction computer vision feature selection genetic algorithm

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computer simulations for comparison of pattern recognition based on different variants of distortion invariant correlation filters

Computer simulations for comparison of pattern recognition b...

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conference on intelligent robots and computer vision XXI

作者： Evtikhiev, NN Ivanov, PA Lyapin, AS Shevchuk, AV Sirotkin, SI Starikov, RS Zaharcev, AV Moscow Engn Phys Inst Moscow 115409 Russia

ISBN: (纸本)081945155X

One of the widespread methods for distorted patterns is to use a distortion invariant correlation filters. Invariant filters have different properties that are quite good for different pattern recognition problems. This paper presents the results of computer simulations of pattern recognition using different modem approaches on distortion invariant correlation filters. The different types of correlation filters (MACE, GMACE, LPCCF, WBKF and others) are compared for input test sets of different examples of patterns. There are presented results of pattern recognition for different types of distortions. The output correlation peaks are compared by its characteristics. The obtained results of comparison provide that in some cases there are correspondences between the choused correlation filter, the variant of pattern and type of the distortion for optimal output peak characteristics.

关键词： correlation distortion pattern recognition invariant filters

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Vector quantization of multiresolution morphological pyramids for efficient coding of images

Vector quantization of multiresolution morphological pyramid...

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conference on intelligent robots and computer vision XIV - algorithms, techniques, Active vision, and Materials Handling

作者： Zhang, ZY Mitra, S TEXAS TECH UNIV DEPT ELECT ENGNCOMP VIS & IMAGE ANAL LABLUBBOCKTX 79409

ISBN: (纸本)0819419524

Morphological pyramid has been proven to be a useful tool in image compression due to low computational complexity, simple implementation and good compression performance based on minimization of entropy. Several morphology based pyramid decomposition techniques already exist. These techniques use morphological filters prior to the down sampling of images. The coding schemes developed commonly omit the first error image of the error pyramid to achieve high compression ratios. However, fine image details may be lost in this process. In order to get high quality lossy image, an estimator involving connectivity preserving filters for the first error image has been used. By using this estimator, the bits per pixel required to code the first error image can be reduced by 30 to 40 percent to obtain `near lossless' compression. In this paper, we apply variable Vector Quantization (VQ) to pyramid coding. We compare the performance of the above estimator to that of VQ scheme. For multi- level pyramid, we discuss accumulative errors and use a modified pyramid generation structure to reduce the accumulative errors. We perform our comparison on two standard images and use Peak Signal to Noise Ratio to judge the compression efficiency and visual quality.

关键词： Image compression Quantization Error analysis Image processing Image quality Nonlinear filtering computer programming Visualization Image filtering Image analysis

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