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

共 723 条记录，以下是81-90 订阅

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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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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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An Effective Trace-Guided Wave-front Navigation and Map Building Approach for Autonomous Mobile robots

An Effective Trace-Guided Wave-front Navigation and Map Buil...

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

作者： Luo, Chaomin Krishnan, Mohan Paulik, Mark Jan, Gene Eu Univ Detroit Mercy Dept Elect & Comp Engn Detroit MI 48221 USA Natl Taipei Univ Inst Elect Engn Taipei Taiwan

ISBN: (纸本)9780819499424

This paper aims to address a trace- guided real-time navigation and map building approach of an autonomous mobile robot. Wave-front based global path planner is developed to generate a global trajectory for an autonomous mobile robot. Modified Vector Field Histogram (M-VFH) is employed based on the LIDAR sensor information to guide the robot locally to be autonomously traversed with obstacle avoidance by following traces provided by the global path planner. A local map composed of square grids is created through the local navigator while the robot traverses with limited LIDAR sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. The real-time wave-front based navigation and map building methodology has been successfully demonstrated in a Player/Stage simulation environment. With the wave-front-based global path planner and M-VFH local navigator, a safe, short, and reasonable trajectory is successfully planned in a majority of situations without any templates, without explicitly optimizing any global cost functions, and without any learning procedures. Its effectiveness, feasibility, efficiency and simplicity of the proposed real-time navigation and map building of an autonomous mobile robot have been successfully validated by simulation and comparison studies. Comparison studies of the proposed approach with the other path planning approaches demonstrate that the proposed method is capable of planning more reasonable and shorter collision-free trajectories autonomously.

关键词： Wave-front path planning trace-guided navigation map building global path panning LIDAR-based navigator

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active Learning for Efficient Sampling of Control Models of Collectives

Active Learning for Efficient Sampling of Control Models of ...

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International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

作者： Alexander Schiendorfer Christoph Lassner Gerrit Anders Wolfgang Reif Rainer Lienhart Institute for Software & Systems Engineering University of Augsburg Germany Perceiving Systems Department Max Planck Institute for Intelligent Systems Germany Multimedia Computing and Computer Vision Lab University of Augsburg Germany

Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., Coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.

关键词： Power generation Production Cost function Schedules robots Switches

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Real time self-localization of omni-vision robot by pattern match system

Real time self-localization of omni-vision robot by pattern ...

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International Conference on Advanced Robotics and intelligent Systems (ARIS)

作者： Shu-Yin Chiang Xingzhi Guo Hsien-Wen Hu Department of Information and Telecommunications Engineering Ming Chuan University Taoyuan Taiwan

In this study, we designed an autonomous mobile robot according to the rules of the Federation of International Robot-soccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The real time self-localization scheme of the mobile robot is based on the algorithms extracting white lines from the soccer field featured in the images from the omni-vision system. The proposed algorithm uses the white line distances from 360 degree direction between the robot and the rotation angle of the robot obtained from the gyroscope as a featured vector. The feature vector is compared with the pre-built in patterns of the database. The location of the robot is obtained from the location of the matched pattern with the minimum error of the featured vector. The results demonstrate that the proposed algorithm is accurate, exhibiting a 10 cm (distance) and 1 degree (rotation) position error in a soccer field measuring 750 cm × 550 cm within processing time about 5 milliseconds. The real time self-localization system can be practically implemented in 2014 FIRA Robot Soccer Competition to fulfill the contest requirement.

关键词： Pattern matching Real-time systems Vectors Mobile robots Heuristic algorithms Feature extraction

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Shape and Texture based Facial Action and Emotion Recognition 14

Shape and Texture based Facial Action and Emotion Recognitio...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Li Zhang Kamlesh Mistry Alamgir Hossain Department of Computer Science and Digital Technologies Northumbria University

ISBN: (纸本)9781634391313

In this paper, we present an intelligent facial emotion recognition system with real-time face tracking for a humanoid robot. The system is able to detect facial actions and emotions from images with up to 60 degrees of pose variations. We employ the active Appearance Model to perform real-time face tracking and extract both texture and geometric representations of images. A POSIT algorithm is also used to identify head rotations. The extracted texture and shape features are employed to detect 18 facial actions and seven basic emotions. The overall system is integrated with a humanoid robot platform to further extend its vision APIs. The system proved to be able to deal with challenging facial emotion recognition tasks with various pose variations.

关键词： algorithms Experimentation Facial emotion recognition active Appearance Model POSIT emotion recognition active appearance model experimentation Face algorithms textures texture face tracking Emotions Humanoid robots

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Simultaneous On-line Discovery and Improvement of Robotic Skill Options

Simultaneous On-line Discovery and Improvement of Robotic Sk...

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IEEE/RSJ International Conference on intelligent robots and Systems

作者： Freek Stulp Laura Herlant Antoine Hoarau Gennaro Raiola Robotics and Computer Vision Ecole Nationale Superieure de Techniques Avancees (ENSTA-ParisTech) France

ISBN: (纸本)9781479969357

The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm "Simultaneous On-line Discovery and Improvement of Robotic Skills" (SODIRS) that is able to autonomously discover and optimize skill options for such task variations. We formalize the problem in a reinforcement learning context, and use the PI~(BB) algorithm [2] to continually optimize skills with respect to a cost function. SODIRS discovers new subskills, or "skill options", by clustering the costs of trials, and determining whether perceptual features are able to predict which cluster a trial will belong to. This enables SODIRS to build a decision tree, in which the leaves contain skill options for task variations. We demonstrate SODIRS' performance in simulation, as well as on a Meka humanoid robot performing the ball-in-cup task.

关键词： Cost function Covariance matrices robots Visualization Vectors Clustering algorithms

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Improving Shape Context Using Geodesic Information And Reflection Invariance

Improving Shape Context Using Geodesic Information And Refle...

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

作者： Premachandran, Vittal Kakarala, Ramakrishna Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

ISBN: (纸本)9780819494351

In this paper, we identify some of the existing problems in shape context matching. We first identify the need for reflection invariance in shape context matching algorithms and propose a method to achieve the same. With the use of these reflection invariance techniques, we bring all the objects, in a database, to their canonical form, which halves the time required to match two shapes using their contexts. We then show how we can build better shape descriptors by the use of geodesic information from the shapes and hence improve upon the well-known Inner Distance Shape Context (IDSC). The IDSC is used by many pre- and post-processing algorithms as the baseline shape-matching algorithm. Our improvements to IDSC will remain compatible for use with those algorithms. Finally, we introduce new comparison metrics that can be used for the comparison of two or more algorithms. We have tested our proposals on the MPEG-7 database and show that our methods significantly outperform the IDSC.

关键词： object recognition shape matching shape descriptor computer vision

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Development of dog-like retrieving capability in a ground robot

Development of dog-like retrieving capability in a ground ro...

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

作者： MacKenzie, Douglas C. Ashok, Rahul Rehg, James M. Witus, Gary Mobile Intelligence Corp 13620 Merriman Rd Livonia MI 48150 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Turing Assoc Ann Arbor MI 48103 USA

ISBN: (纸本)9780819494351

This paper presents the Mobile Intelligence Team's approach to addressing the CANINE outdoor ground robot competition. The competition required developing a robot that provided retrieving capabilities similar to a dog, while operating fully autonomously in unstructured environments. The vision team consisted of Mobile Intelligence, the Georgia Institute of Technology, and Wayne State University. Important computer vision aspects of the project were the ability to quickly learn the distinguishing characteristics of novel objects, searching images for the object as the robot drove a search pattern, identifying people near the robot for safe operations, correctly identify the object among distractors, and localizing the object for retrieval. The classifier used to identify the objects will be discussed, including an analysis of its performance, and an overview of the entire system architecture presented. A discussion of the robot's performance in the competition will demonstrate the system's successes in real-world testing.

关键词： CANINE object recognition computer vision unmanned ground vehicle autonomy

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Loop Closure Detection Using Local Zernike Moment Patterns

Loop Closure Detection Using Local Zernike Moment Patterns

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

作者： Sariyanidi, Evangelos Sencan, Onur Temeltas, Hakan Istanbul Tech Univ Control & Automat Engn Dept TR-34469 Istanbul Turkey

ISBN: (纸本)9780819494351

This paper introduces a novel image description technique that aims at appearance based loop closure detection for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure is achieved by simply comparing the most recent image with the images in the past trajectory. The technique has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms of computation efficiency and loop closure precision.

关键词： Loop closure mobile robotics image processing SLAM computer vision

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