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

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Autonomous flight and vision-based target tracking for a flapping-wing MAV

Autonomous flight and vision-based target tracking for a fla...

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2016 IEEE/RSJ International conference on intelligent robots and Systems, IROS 2016

作者： Ryu, Seungwan Kwon, Ukjin Jin Kim, H. Department of Mechanical and Aerospace Engineering Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781509037629

In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also present autonomous vision-based target tracking for a FWMAV equipped with a low-cost and light-weight first person view (FPV) camera. We construct a ground station, integrated with control and vision algorithms, which performs the image processing and the computation of control inputs based on the acquired state variables from motion capture system. In addition, we employ a vision algorithm for a low-quality camera to detect a static target with the discussions on the techniques to improve the reliability of visual detection. Experimental results show satisfactory flight performance, achieving the height regulation and directional control, and autonomous vision-based target tracking. © 2016 IEEE.

关键词： Cameras

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Video switching and sensor fusion for multi-camera sensing systems

Video switching and sensor fusion for multi-camera sensing s...

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

作者： Saxena, M Selvaraj, V Dhareshwar, R Hall, EL Univ Cincinnati Ctr Robot RES Cincinnati OH 45221 USA

ISBN: (纸本)0819438626

With the low cost of solid-state camera systems, it is now possible to include many cameras on a mobile robot or other machine. However, video processing is still relatively expensive. Therefore it is often desirable to share several cameras with a single processor. The purpose of this paper is to describe the design of a video switching system that permits eight cameras to be multiplexed with a single chip. Multiples of eight could also easily be accomplished. The heart of the system is a Maxim video switch. The user simply selects using a three-bit control signal, which camera signal is selected. The output of the video switch is then the desired camera image. One application of this video switch is a four camera input system to a mobile robot being constructed at the University of Cincinnati. Other applications include surveillance and other mobile systems. The decision as to which camera to observe can be made automatically from a computer providing a great versatility. For example, supplemental motion detectors could be used to activate the camera selection for a surveillance system. Higher-level logic has been used on our mobile robot application. Still higher-level logic could be used to fuse the video information in various ways before processing. The significance of this device is that it provides a wealth of video information to be used at the discretion of either a human viewer or automatic system.

关键词： computer vision video switching multiplexing mobile robot

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Scene segmentation from vector-valued images using anisotropic diffusion

Scene segmentation from vector-valued images using anisotrop...

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

作者： Burgiss, SG Lester, ED Whitaker, RT Abidi, MA Univ Tennessee Dept Elect Engn Imaging Robot & Intelligent Syst Lab Knoxville TN 37996 USA

ISBN: (纸本)081942983X

Scene segmentation is a pre-processing step for many vision systems. We are concerned with segmentation as a precursor to 3-D scene modeling. Segmentation of a scene for this purpose usually involves dividing an image into areas that are relatively uniform in some value (e.g. intensity, range, or curvature). This single segmented image represents the analogous segmented scene. This paper presents a segmentation method that uses features to indicate boundaries or edges between regions. We incorporate features from multiple image types to obtain an more accurate segmentation of objects or object parts in the scene. Multiple features are not only combined directly to improve segmentation results, but they are also used to guide a smoothing operation. This smoothing technique preserves features representing edges while smoothing noise in the images. The segmentation method is based on applying a watershed algorithm to a fuzzy feature map. A fuzzy feature map being any image containing fuzzy values representing degree of membership in a particular feature class. The first step in obtaining the fuzzy feature map involves smoothing noise from the image pair. We apply an anisotropic diffusion algorithm to both images. This algorithm smooths noise in the images while preserving changes in range, intensity, and surface normal. We create three fuzzy feature maps from the smoothed range and intensity image pair: gradient of the range image, gradient of the intensity image, sind gradient of the surface normal of the range image. We fuse these fuzzy feature maps to create a fuzzy feature map of edges. This map includes both step and crease edges. Where step edges are defined as discontinuities in range and crease edges are discontinuities in surface normal of range. We derive a segmentation from this fuzzy edge map by application of a morphological watershed algorithm. We present results from analysis of real images.

关键词： anisotropic diffusion image segmentation range image scale space multi-scale analysis

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Using Short-wave Infrared Imaging for Fruit Quality Evaluation

Using Short-wave Infrared Imaging for Fruit Quality Evaluati...

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

作者： Zhang, Dong Lee, Dah-Jye Desai, Alok Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA

ISBN: (纸本)9780819499424

Quality evaluation of agricultural and food products is important for processing, inventory control, and marketing. Fruit size and surface quality are two important quality factors for high-quality fruit such as Medjool dates. Fruit size is usually measured by length that can be done easily by simple image processing techniques. Surface quality evaluation on the other hand requires more complicated design, both in image acquisition and image processing. Skin delamination is considered a major factor that affects fruit quality and its value. This paper presents an efficient histogram analysis and image processing technique that is designed specifically for real-time surface quality evaluation of Medjool dates. This approach, based on short-wave infrared imaging, provides excellent image contrast between the fruit surface and delaminated skin, which allows significant simplification of image processing algorithm and reduction of computational power requirements. The proposed quality grading method requires very simple training procedure to obtain a gray scale image histogram for each quality level. Using histogram comparison, each date is assigned to one of the four quality levels and an optimal threshold is calculated for segmenting skin delamination areas from the fruit surface. The percentage of the fruit surface that has skin delamination can then be calculated for quality evaluation. This method has been implemented and used for commercial production and proven to be efficient and accurate.

关键词： Machine vision histogram analysis fruit grading Medjool date non-contact visual inspection short-wave infrared imaging

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Color Back Projection for Fruit Maturity Evaluation

Color Back Projection for Fruit Maturity Evaluation

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

作者： Zhang, Dong Lee, Dah-Jye Desai, Alok Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA

ISBN: (纸本)9780819499424

In general, fruits and vegetables such as tomatoes and dates are harvested before they fully ripen. After harvesting, they continue to ripen and their color changes. Color is a good indicator of fruit maturity. For example, tomatoes change color from dark green to light green and then pink, light red, and dark red. Assessing tomato maturity helps maximize its shelf life. Color is used to determine the length of time the tomatoes can be transported. Medjool dates change color from green to yellow, and the orange, light red and dark red. Assessing date maturity helps determine the length of drying process to help ripen the dates. Color evaluation is an important step in the processing and inventory control of fruits and vegetables that directly affects profitability. This paper presents an efficient color back projection and image processing technique that is designed specifically for real-time maturity evaluation of fruits. This color processing method requires very simple training procedure to obtain the frequencies of colors that appear in each maturity stage. This color statistics is used to back project colors to predefined color indexes. Fruit maturity is then evaluated by analyzing the reprojected color indexes. This method has been implemented and used for commercial production.

关键词： Machine vision color back projection color grading tomatoes non-contact visual inspection

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Handling of Split-and-Merge Effects and Occlusions using Feature-Based Probabilistic Data Association

Handling of Split-and-Merge Effects and Occlusions using Fea...

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27th conference on intelligent robots and computer vision: algorithms and techniques

作者： Grinberg, Michael Ohr, Florian Fraunhofer Inst Informat & Data Proc IITB D-761313 Karlsruhe Germany

ISBN: (纸本)9780819479327

One of the big challenges in multi-target tracking is the track management and correct data association between measurements and tracks. Major reason for tracking errors are detection failures such as merged, split, incomplete or missed detections as well as clutter-based detections ( phantom objects). Those effects combined with uncertainties in existence and number of objects in the scene as well as uncertainties in their observability and dynamic object state lead to gross tracking errors. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets which is capable of coping with occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize information about the measurements' composition gained through tracking dedicated feature points in the image and in 3D space, which allows us to reconstruct the desired object characteristics from the data even in the case of detection errors due to above-mentioned reasons. The proposed Feature-Based Probabilistic Data Association approach resolves data association ambiguities in a soft threshold-free decision based not only on target state prediction but also on the existence and observability estimation modeled as two additional Markov chains. A novel measurement reconstruction scheme allows for a correct innovation in case of split, merged and incomplete measurements realizing thus a detection-by-tracking approach. This process is assisted by a grid based object representation which offers a lower abstraction level of targets extent and is used for detailed occlusion analysis.

关键词： multitarget tracking 9MTT) feature-based tracking track-before-detect detection-by-tracking probabilistic data association point tracking 6D-vision split and merge handling occlusion handling stereo video environment perception

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification 6

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

引用

6th IEEE International conference on Power, intelligent Computing and Systems, ICPICS 2024

作者： Zhong, Herun School of Arts and Design Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9798350374315

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. This article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. The discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks. © 2024 IEEE.

关键词： Convolutional neural networks

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Designing and Making the Intelligence Assistant Robot and controlling it by the fuzzy procedure

Designing and Making the Intelligence Assistant Robot and co...

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10th International conference on Electronics, computer and Computation (ICECCO)

作者： Zabbah, Iman Foolad, Shima Chaharaqran, Bahram Mazlooman, Reza Islamic Azad Univ Torbat E Heydarieh Branch Dept Comp Tehran Iran Semnan Univ Dept Comp Semnan Iran Maraghen Sanati Univ Dept Comp Maragheh Iran Natl Iranian Oil Prod Distribut Co Torbat Heydarieh Branch Torbat Heydarieh Iran

ISBN: (纸本)9781479933433

Mobile robots are those with the capability of moving in an environment. The controller of these robots needs to have the ability to do intelligent behaviours in an uncertain environment. Today fuzzy control of mobile robots in non-structured environments is increasing. Many represented algorithms for mobile robot navigation don't manifest appropriate performance in dynamic environments. This article aims at designing and making an intelligence robot. This robot is able to detect a specified target and track it. Velocity and steering of this robot is controlled by the fuzzy logic. Robot velocity is adjusted according to its distance from the target. This article is expressed the features of this mobile robot including different mechanical and electronic infrastructures, fuzzy navigation and vision controller. Navigation system of this robot makes its fuzzy decisions only based on the machine vision techniques and unlike many robots, it doesn't depend on sensor. It is concluded that robot with fuzzy navigation have 60% time saving than the non-fuzzy. The results obtained indicate its appropriate performance in order to navigate a target-oriented mobile robot in an undetermined and dynamic environment.

关键词： Mobile robot navigation Machine vision Robot fuzzy control Target tracking Intelligence robot

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Lightweight rovers for Mars science exploration and sample return

Lightweight rovers for Mars science exploration and sample r...

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

作者： Schenker, PS Sword, LF Ganino, AJ Bickler, DB Hickey, GS Brown, DK Baumgartner, ET Matthies, LH Wilcox, BH Balch, T Aghazarian, H Garrett, MS Jet Propulsion Lab. (United States)

ISBN: (纸本)0819426407

We report on the development of new mobile robots for Mars exploration missions. These ''lightweight survivable rover (LSR)'' systems are of potential interest to both space and terrestrial applications, and ape distinguished from more conventional designs by their use of new composite materials, collapsible running gear, integrated thermal-structural chassis, and other mechanical features enabling improved mobility and environmental robustness at reduced mass, volume, and power. Our first demonstrated such rover architecture, LSR-1, introduces running gear based on 2D composite struts and 3D machined composite joints, a navel collapsible hybrid composite-aluminum wheel design, a unit-body structural-thermal chassis with improved internal temperature isolation and stabilization, and a spot-pushbroom laser/CCD sensor enabling accurate, fast hazard detection and terrain mapping. LSR-1 is a similar to .7 x 1.0 meter(Lambda)2(WxL) footprint six-wheel (20 cm dia.) rocker-bogie geometry vehicle of similar to 30 cm ground clearance, weighing only 7 kilograms with an onboard 3 kilogram multi-spectral imager and spectroscopic photometer. By comparison, NASA/JPL's recently flown Mars Pathfinder rover Sojourner is an 11+ kilogram flight experiment (carrying a 1 kg APXS instrument) having similar to .45 x.6 meter(Lambda)2 (WxL) footprint and 15 cm ground clearance, and about half the warm electronics enclosure (WEE) volume with twice the diurnal temperature swing (-40 to +40 degrees C) of LSR-1 in nominal Mars environments. We are also developing a new, smaller 5 kilogram class LSR-type vehicle for Mars sample return - the travel to, localization of, pick-up, and transport back to an Earth return ascent vehicle of a sample cache collected by earlier science missions. This Sample Retrieval Rover R&D prototype has a. completely collapsible mobility system enabling rover stowage to similar to 25% operational volume, as well an actively articulated axle, allowing changeable pose of

关键词： mobile robots 3D sensing robotic sampling planetary science Mars sample return

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Object tracking - an ART based phase filtering approach

Object tracking - an ART based phase filtering approach

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Proceedings of the 1998 conference on intelligent robots and computer vision XVII: algorithms, techniques, and Active vision

作者： Chiou, Jeng-Yih Hwang, Kao-Shing Natl Chung Cheng Univ Chiayi Taiwan

An algorithm consisting of an artificial neural network and phase only match filter (POMF) method to tracking an object in a series of satellite images with variant intensity level is proposed. The notable adaptive resonance theory (ART) neural network is used as a high frequency noise filter for the POMF. The algorithm is examined by means of several satellite images. From the experimental results, the proposed method demonstrates that the performance of POMF can be successfully improved by reducing the high frequent noise of the background in the satellite images.

关键词： computer vision

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