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

共 752 条记录，以下是271-280 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...

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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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Autonomous robot software development using simple software components

Autonomous robot software development using simple software ...

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

作者： Burke, TM Chung, CJ Lawrence Technol Univ Dept Math & Comp Sci Southfield MI 48075 USA

ISBN: (纸本)081945561X

Developing software to control a sophisticated lane-following, obstacle-avoiding, autonomous robot can be demanding and beyond the capabilities of novice programmers - but it doesn't have to be. A creative software design utilizing only basic image processing and a little algebra, has been employed to control the LTU-AISSIG autonomous robot - a contestant in the 2004 intelligent Ground Vehicle Competition (IGVC). This paper presents a software design equivalent to that used during the IGVC, but with much of the complexity removed. The result is an autonomous robot software design, that is robust, reliable, and can be implemented by programmers with a limited understanding of image processing. This design provides a solid basis for further work in autonomous robot software, as well as an interesting and achievable robotics project for students.

关键词： robotics autonomous robot computer science education simple software KISS image processing

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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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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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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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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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A method for integrating vision and laser range measurements in autonomous ground robotic vehicles

A method for integrating vision and laser range measurements...

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

作者： Riggins, RN Mutter, BV Bluefield State Coll Bluefield WV USA

ISBN: (纸本)081945561X

This paper presents a method to integrate non-stereoscopic vision information with laser distance measurements for Autonomous Ground Robotic Vehicles (AGRV). The method assumes a horizontally-mounted Laser Measurement System (LMS) sweeping 180 degrees in front from right to left every one second, and a video camera mounted five feet high pointing to the front and down at 45 degrees to the horizontal. The LMS gives highly accurate obstacle position measurements in a two-dimensional plane whereas the vision system gives limited and not-so-accurate information on obstacle positions in three dimensions. The vision system can also see contrasts between ground markings. Many AGRVs have similar sensors in similar arrangements. The method presented here is general enough for many types of distance measurements and cameras and lenses. Since the data from these two sensors are in radically different formats, AGRVs need a scheme to combine this data into a common format so that the data can be compared and correlated. Having a successful integration method allows the AGRV to make smart path-finding navigation decisions. Integrating these two sensors is one of the challenges for AGRVs that use this approach. The method presented in this paper employs a geometrical approach to combine the two data sets in real time. Tests, accomplished in simulation as well as on an actual AGRV, show excellent results.

关键词： Sensor Integration Autonomous Ground Robotic Vehicles vision Laser Measurement System

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Circle detection algorithm for counting canes in a bundle

Circle detection algorithm for counting canes in a bundle

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

作者： Yeong, WaiKai Batchelor, Bruce G. School of Computer Science Cardiff University 5 The Parade Cardiff CF24 3XF United Kingdom

A circle detection technique and its application in counting the number of pieces of canes in a bundle are described. Pre-processing is performed on the "raw" image from the camera, to obtain a binary image that is suitable for counting. Blobs in this image are then isolated in turn and each one is analysed to extract properties of interest, including left, right, top and bottom limits of the blobs, their centroid positions, sizes, and perimeters. These measurements are further analysed, so that objects that are approximately circular can be identified. In practice, factors such as lighting, the grade of cane and physical state of the cut ends may result in degraded circular objects being present in this binary image. Possible processes for identifying "circular" objects from a fragmented image are reviewed and a novel technique is proposed.

关键词： algorithms

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Teaching the computer subjective notions of feature connectedness in a visual scene for real time vision.

Teaching the computer subjective notions of feature connecte...

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

作者： Mundhenk, TN Landauer, C Bellman, K Arbib, NA Itti, L Univ So Calif Dept Comp Sci Henry Salvatori Comp Sci Ctr Los Angeles CA 90089 USA

ISBN: (纸本)081945561X

We discus a tool kit for usage in scene understanding where prior information about targets is not necessarily understood. As such, we give it a notion of connectivity such that it can classify features in an image for the purpose of tracking and identification. The tool VFAT (Visual Feature Analysis Tool) is designed to work in real time in an intelligent multi agent room. It is built around a modular design and includes several fast vision processes. The first components discussed are for feature selection using visual saliency and Monte Carlo selection. Then features that have been selected from an image are mixed into useful and more complex features. All the features are then reduced in dimension and contrasted using a combination of Independent Component Analysis and Principle Component Analysis (ICA/PCA). Once this has been done, we classify features using a custom non-parametric classifier (NPclassify) that does not require hard parameters such as class size or number of classes so that VFAT can create classes without stringent priors about class structure. These classes are then generalized using Gaussian regions which allows easier storage of class properties and computation of probability for class matching. To speed up to creation of Gaussian regions we use a system of rotations instead of the traditional Psuedo-inverse method. In addtion to discussing the structure of VFAT we discuss training of the current system which is relatively easy to perform. ICA/PCA is trained by giving VFAT a large number of random images. The ICA/PCA matrix is computed by features extracted by VFAT. The non-parametric classifier NPclasify it trained by presenting it with images of objects having it decide how many objects it thinks it sees. The difference between what it sees and what it is supposed to see in terms of the number of objects is used as the error term and allows VFAT to learn to classify based upon the experimenters subjective idea of good classification.

关键词： iRoom biological vision tool multi agent saliency real time

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