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A machine vision system using a laser radar applied to robotic fruit harvesting

A machine vision system using a laser radar applied to robot...

引用

ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： A.R. Jimenez R. Ceres J.L. Pons Instituto de Automática Industrial (CSIC) Madrid Spain

This paper describes a new laser-based computer vision system used for automatic fruit recognition. Most relevant vision studies for fruit harvesting are reviewed. Our system is based on an infrared laser-range finder sensor generating range and reflectance images and it is designed to detect spherical objects in non-structured environments. A special image restoration technique is defined and applied to improve image quality. Image analysis algorithms integrate both range and reflectance information to generate four characteristic primitives evidencing the presence of spherical objects. This machine vision system, which generates the 3-D location, radio and surface reflectivity of each spherical object has been applied to the AGRIBOT orange fruit harvester robot. Test results indicate good correct detection rates, unlikely false alarms and a robust behavior.

关键词： Laser radar Machine vision Robot vision systems Reflectivity Infrared image sensors Infrared detectors Radar imaging Robot sensing systems Robotics and automation computer vision

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Passive night vision sensor comparison for unmanned ground vehicle stereo vision navigation

Passive night vision sensor comparison for unmanned ground v...

引用

ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： K. Owens L. Matthies Jet Propulsion Laboratory Pasadena CA

One goal of the "Demo III" unmanned ground vehicle program is to enable autonomous nighttime navigation at speeds of up to 10 m.p.h. To perform obstacle detection at night with stereo vision will require night vision cameras that produce adequate image quality for the driving speeds, vehicle dynamics, obstacle sizes, and scene conditions that will be encountered. This paper analyzes the suitability of four classes of night vision cameras (3-5 /spl mu/m cooled FLIR, 8-12 /spl mu/m cooled FLIR, 8-12 /spl mu/m uncooled FLIR, and image intensifiers) for night stereo vision, using criteria based on stereo matching quality, image signal to noise ratio, motion blur and synchronization capability. We find that only cooled FLIRs will enable stereo vision performance that meets the goals of the Demo III program for nighttime autonomous mobility.

关键词： Night vision Land vehicles Stereo vision Cameras Navigation Vehicle detection Image quality Vehicle dynamics Layout Image analysis

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Morphological shared-weight neural networks: a tool for automatic target recognition beyond the visible spectrum

Morphological shared-weight neural networks: a tool for auto...

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ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： M.A. Khabou P.D. Gader J.M. Keller Department of Computer Engineering and Computer Science University of Missouri Columbia CO USA

Morphological shared-weight neural networks (MSNN) combine the feature extraction capability of mathematical morphology with the function mapping capability of neural networks. This provides a trainable mechanism for translation invariant object detection using a variety of imaging sensors, including TV, forward-looking infrared (FLIR) and synthetic aperture radar (SAR). We provide an overview of previous results and new results with laser radar (LADAR). We present three sets of experiments. In the first set of experiments we use the MSNN to detect different types of targets simultaneously. In the second set we use the MSNN to detect only a particular type of target. In the third set we test a novel scenario: we train the MSNN to recognize a particular type of target using very few examples. A detection rate of 86% with a reasonable number of false alarms was achieved in the first set of experiments and a detection rate of close to 100% with very few false alarms was achieved in the second and third sets of experiments.

关键词： Neural networks Laser radar Infrared image sensors Radar detection Feature extraction Morphology Object detection Optical imaging TV Infrared detectors

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LADAR scene description using fuzzy morphology and rules

LADAR scene description using fuzzy morphology and rules

引用

ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： J.M. Keller P. Gader Xiaomei Wang Computer Engineering and Computer Science Department University of Missouri Columbia MO USA

This paper presents a method for automatically generating descriptions of scenes represented by digital images acquired using laser radar (LADAR). A method for matching the scenes to linguistic descriptions is also presented. Both methods rely on fuzzy spatial relations. Primitive spatial relations between objects are computed using fuzzy mathematical morphology and compared to a previous method based on training a neural network to learn human preferences. For each pair of objects in a scene, the primitive spatial relations are combined into complex spatial relations using a fuzzy rule base. A scene description is generated using the highest confidence rule outputs. Scene matching is performed using the outputs of the rules that correspond to the linguistic description. The results show that seemingly significant differences in spatial relationship definitions have little impact on system performance and that reasonable match scores and descriptions can be generated from the fuzzy system.

关键词： Laser radar Layout Morphology Digital images Radar imaging computer networks Fuzzy neural networks Neural networks Humans Impedance matching

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A ground truth tool for Synthetic Aperture Radar (SAR) imagery

A ground truth tool for Synthetic Aperture Radar (SAR) image...

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ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： I. Pavlidis D. Perrin N.P. Papanikolopoulos W. Au S. Sawtelle Honeywell Technology Center Minneapolis MN USA Department of Computer Science University of Minnesota Minneapolis MN USA WL AACR OH USA

The performance of computer vision algorithms has made great strides and it is good enough to be useful in a number of civilian and military applications. Algorithm advancement in Automatic Target Recognition (ATR) in particular; has reached a critical point. State-of-the-art ATRs are capable of delivering robust performance for certain operational scenarios. As computer vision technology matures and algorithms enter the civilian and military marketplace as products, the lack of a formal testing theory and tools become obvious. In this paper we present the design and implementation of a Ground Truth Tool (GTT) for Synthetic Aperture Radar (SAR) imagery. The tool serves as part of an evaluation system for SAR ATRs. It features a semi-automatic method for delineating image objects that draws upon the theory of deformable models. In comparison with other deformable model implementations, our version is stable and is supported by an extensive Graphical User Interface (GUI). Preliminary experimental tests show that the system can substantially increase the productivity and accuracy of the image analyst (IA).

关键词： Synthetic aperture radar computer vision Military computing Deformable models Graphical user interfaces Application software Target recognition Robustness System testing Productivity

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Detecting moving objects in airborne forward looking infra-red sequences

Detecting moving objects in airborne forward looking infra-r...

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ieee workshop on computer vision beyond the visible spectrum: methods and applications

作者： A. Strehl J.K. Aggarwal Computer and Vision Research Center Department of Electrical and Computer Engineering University of Technology Austin TX USA

In this paper we propose a system that detects independently moving objects (IMOs) in forward looking infra-red (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Consequently, areas with residual motion indicate object activity. These areas are detected, refined and selected using a Bayes' classifier. The remaining regions are clustered into pairs. Each pair represents an object's front and rear end. Using motion and scene knowledge we estimate object pose and establish a region-of-interest (ROI) for each pair. Edge elements within each ROI are used to segment the convex cover containing the IMO. We show detailed results on real, complex, cluttered and noisy sequences. Moreover, we outline the integration of our robust system into a comprehensive automatic target recognition (ATR) and action classification system.

关键词： Object detection Infrared detectors Layout Motion estimation Target recognition Motion detection Forward contracts Image sequences Motion analysis Image segmentation

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A multiscale analysis model applied to natural surfaces

A multiscale analysis model applied to natural surfaces

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ieee workshop on applications of computer vision (WACV)

作者： F. Falzon G. Giraudon M. Berthod INRIA Sophia-Antipolis France

Multiscale Analysis of surfaces allows a hierarchical representation of their composing features. To represent a surface at a given scale, structures that are insignificant at that scale have to be eliminated. A typical example for this approach is cartography. However, the aims of cartographers reach beyond simply gradually eliminating the structures; in the majority of cases, the nature of geomorphological structures which compose the surface have to be preserved across all scales. Thus a global smoothing of the surface is not suitable to solve the present problem, since that would cause inevitably morphological modifications of certain important structures. In fact, the points to be preserved across scale variations are to be chosen interactively by the user. The authors present a surface model which allows them to perform a Multiscale Analysis which takes the importance of local structures into consideration, i.e. structures which are inherent to the relief morphology. From that discrete model we extract a context-dependent Multiscale Analysis Operator which can be isotropic or anisotropic and can be expressed in different forms.< >

关键词： Surface morphology Surface reconstruction Sampling methods Interpolation Smoothing methods Performance analysis Context modeling Anisotropic magnetoresistance Digital elevation models computer vision

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Volume Graphics 2001 1

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丛书名： Eurographics

1000年

作者： Klaus Mueller Arie E. Kaufman

ISBN: (数字)9783709167564

ISBN: (纸本)9783211837375

This book contains the proceedings of the International workshop on Volume Graphics 200 1 (VG'O I) which took place on June 21 and June 22 at Stony Brook, New York. This year's event was the second in the series, following a successful premiere in Swansea, Wales, in March 1999, and was co-sponsored by the ieee Technical Committee on Visualization and Graphics (TC-VG) as well as EUROGRAPHICS. The Volume Graphics workshop is held bi-annually and has been created to pro vide a forum for the exploration and advancement of volume-based techniques, beyond the scope of just volume visualization. It brings together researchers and practitioners both from academia and industry, from many parts of the world. Volume graphics is in the process of evolving into a general graphics technology, and the papers included in these proceedings are testimonial to the wide spectrum of unique applications and solu tions that volumetric representations are able to offer.

关键词： computer Graphics computer-Aided Engineering (CAD, CAE) and Design Image Processing and computer vision Special Purpose and Application-Based Systems Multimedia Information Systems

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Variational, Geometric, and Level Set methods in computer vision 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Nikos Paragios Olivier Faugeras Tony Chan Christoph Schnörr

ISBN: (数字)9783540321095

ISBN: (纸本)9783540293484

Mathematical methods has been a dominant research path in computational vision leading to a number of areas like ?ltering, segmentation, motion analysis and stereo reconstruction. Within such a branch visual perception tasks can either be addressed through the introduction of application-driven geometric ?ows or through the minimization of problem-driven cost functions where their lowest potential corresponds to image understanding. The 3rd ieee workshop on Variational, Geometric and Level Set methods focused on these novel mathematical techniques and their applications to c- puter vision problems. To this end, from a substantial number of submissions, 30 high-quality papers were selected after a fully blind review process covering a large spectrum of computer-aided visual understanding of the environment. The papers are organized into four thematic areas: (i) Image Filtering and Reconstruction, (ii) Segmentation and Grouping, (iii) Registration and Motion Analysis and (iiii) 3D and Reconstruction. In the ?rst area solutions to image enhancement, inpainting and compression are presented, while more advanced applications like model-free and model-based segmentation are presented in the segmentation area. Registration of curves and images as well as multi-frame segmentation and tracking are part of the motion understanding track, while - troducing computationalprocessesinmanifolds,shapefromshading,calibration and stereo reconstruction are part of the 3D track. We hope that the material presented in the proceedings exceeds your exp- tations and will in?uence your research directions in the future. We would like to acknowledge the support of the Imaging and Visualization Department of Siemens Corporate Research for sponsoring the Best Student Paper Award.

关键词： Image Processing and computer vision computer Imaging, vision, Pattern Recognition and Graphics Pattern Recognition Artificial Intelligence Algorithm Analysis and Problem Complexity computer Graphics

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