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Improving texture pattern recognition by integration of multiple texture feature extraction methods

Improving texture pattern recognition by integration of mult...

引用

16th International Conference on Pattern Recognition, ICPR 2002

作者： Garcia, Miguel Angel Puig, Domènec Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Av. Països Catalans 26 Tarragona43007 Spain

ISBN: (纸本)0769516963

This paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. Experimental results with textured images of outdoor scenes show that the proposed technique yields lower classification errors than widely recognized texture classifiers based on specific families of texture methods. © 2002 IEEE.

关键词： Extraction

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Fire detection using autonomous aerial vehicles with infrared and visual cameras

Fire detection using autonomous aerial vehicles with infrare...

引用

作者： Martínez-De Dios, J. Ramiro Merino, Luis Ollero, Aníbal Robotics Computer Vision and Intelligent Control Group Escuela Superior de Ingenieros University of Seville Camino de los Descubrimientos s/n 41092 Seville Spain

ISBN: (纸本)008045108X

The paper presents a system for automatic fire detection based on the use of autonomous aerial vehicles. Particularly, the application of a helicopter with infrared and visual cameras is described. The paper presents the techniques used for fire segmentation in visual and infrared cameras, and the procedures to fuse the data obtained from both of them. Furthermore the paper presents the techniques for automatic geolocation of the detected fire alarms. Experimental results are shown. Copyright © 2005 IFAC.

关键词： Cameras

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Supervised texture segmentation through a multi-level pixel-based classifier based on specifically designed filters

Supervised texture segmentation through a multi-level pixel-...

引用

IEEE International Conference on Image Processing

作者： Jaime Melendez Xavier Girones Domenec Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain

This paper presents a new, efficient technique for supervised texture segmentation based on a set of specifically designed filters and a multi-level pixel-based classifier. Filter design is carried out by means of a neural network, which is trained to maximize the filters' discrimination power among the texture classes under consideration. Texture features obtained with these filters are then processed by a classification scheme that utilizes multiple evaluation window sizes following a top-down approach, which iteratively refines the resulting segmentation. The proposed technique is compared to previous supervised texture segmenters by using both synthetic compositions and real outdoor textured images.

关键词： Filter banks Image segmentation Feature extraction Gabor filters Adaptive filters Conferences Support vector machines

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Pixel classification through divergence-based integration of texture methods with conflict resolution

Pixel classification through divergence-based integration of...

引用

IEEE International Conference on Image Processing

作者： D. Puig M.A. Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper presents a new technique for combining multiple texture feature extraction methods in order to classify the pixels of an input image into a set of texture models of interest. The problem of integrating multiple texture methods for classification purposes is cast as a collaborative decision making problem. Each texture method is considered to be an expert that gives an opinion about the membership of every input image pixel to each texture model, along with a conviction about that judgement. A conviction measure based on the Kullback J-divergence between texture models is proposed, along with an arbitration mechanism that combines those convictions by taking into account conflicts that may occur when different experts disagree with a similar strength. The proposed technique is compared to previous pixel-based texture classifiers by using real textured images.

关键词： Pixel intelligent robots Feature extraction Collaboration Decision making Robot vision systems computer vision computer science Mathematics Soil

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Autonomous Detection of Safe Landing Areas for an UAV from Monocular Images

Autonomous Detection of Safe Landing Areas for an UAV from M...

引用

2006 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Sebastien Bosch Simon Lacroix Fernando Caballero LAAS CNRS Toulouse France Robotics Computer Vision and Intelligent Control Group University of Seville Spain

This paper presents an approach to detect safe landing areas for a flying robot, on the basis of a sequence of monocular images. The approach does not require precise position and attitude sensors: it exploits the relations between 2D image homographies and 3D planes. The combination of a robust homography estimation and of an adaptive thresholding of correlation scores between registered images yields the update of a stochastic grid, that exhibits the horizontal planar areas perceived. This grid allows the integration of data gathered at various altitudes. Results are presented

关键词： Unmanned aerial vehicles Layout intelligent robots Transmission line matrix methods Image reconstruction Robot sensing systems Robot vision systems Image sensors Robustness Yield estimation

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Recognizing specific texture patterns by integration of multiple texture methods

Recognizing specific texture patterns by integration of mult...

引用

IEEE International Conference on Image Processing

作者： D. Puig M.A. Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

A supervised pixel-based classifier for identifying the presence of a given set of texture patterns of interest in a complex textured image is described. The proposed technique integrates the outcome of multiple texture feature extraction methods belonging to different families. In this way, it yields lower classification rates than previous texture classifiers based on specific families of texture methods. Experimental results with real outdoor images are presented.

关键词： Pattern recognition Pixel Image recognition Feature extraction intelligent robots Layout Image segmentation Robot vision systems computer vision computer science

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3D simultaneous localization and modeling from stereo vision

3D simultaneous localization and modeling from stereo vision

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： M.A. Garcia A. Solanas Intelligent Robotics and Computer Vision Group /Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This work presents a new algorithm for determining the trajectory of a mobile robot and, simultaneously, creating a detailed volumetric 3D model of its workspace. The algorithm exclusively utilizes information provided by a single stereo vision system, avoiding thus the use both of more costly laser systems and error-prone odometry. Six-degrees-of-freedom egomotion is directly estimated from images acquired at relatively close positions along the robot's path. Thus, the algorithm can deal with both planar and uneven terrain in a natural way, without requiring extra processing stages or additional orientation sensors. The 3D model is based on an octree that encapsulates clouds of 3D points obtained through stereo vision, which are integrated after each egomotion stage. Every point has three spatial coordinates referred to a single frame, as well as true-color components. The spatial location of those points is continuously improved as new images are acquired and integrated into the model.

关键词： Stereo vision Simultaneous localization and mapping Robot kinematics Robot sensing systems Mobile robots intelligent robots Robot vision systems Laser modes Mathematical model computer vision

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Coordinated multi-robot exploration through unsupervised clustering of unknown space

Coordinated multi-robot exploration through unsupervised clu...

引用

IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： A. Solanas M.A. Garcia Intelligent Robotics and Computer Vision Group/Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

This paper proposes a new coordination algorithm for efficiently exploring an unknown environment with a team of mobile robots. The proposed technique subsequently applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the remaining unknown space into as many disjoint regions as available robots. Each robot is primarily responsible for exploring its assigned region and can help other robots on its way through. Unknown space is dynamically repartitioned as new areas are discovered by the team, balancing thus the overall workload among team members and naturally leading to greater dispersion over the environment and thus faster broad coverage than with previous greedy-like approaches, which guide robots based on maximum profit strategies that simply trade off between distance to the closest frontiers and amount of unknown cells likely to be discovered from them.

关键词： Robot kinematics Orbital robotics Robot sensing systems Mobile robots Space technology intelligent robots Layout computer science Mathematics Clustering algorithms

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Improving texture pattern recognition by integration of multiple texture feature extraction methods

Improving texture pattern recognition by integration of mult...

引用

International Conference on Pattern Recognition

作者： M.A. Garcia D. Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain

关键词： Pattern recognition Feature extraction Pixel Image recognition intelligent robots Image segmentation Robot vision systems computer vision computer science Mathematics

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Cooperative Fire Detection using Unmanned Aerial Vehicles

Cooperative Fire Detection using Unmanned Aerial Vehicles

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： L. Merino F. Caballero J.R. Martinez-de Dios A. Ollero Robotics Computer Vision and Intelligent Control Group Escuela Superior de Ingenieros University of Seville Seville Spain

The paper presents a framework for cooperative fire detection by means of a fleet of heterogeneous UAVs. computer vision techniques are used to detect and localize fires from infrared and visual images and other data provided by the cameras and other sensors on-board the UAVs. The paper deals with the techniques used to decrease the uncertainty in fire detection and increase the accuracy in fire localisation by means of the cooperation of the information provided by several UAVs. The presented methods have been developed in the COMETS multi-UAV project.

关键词： Fires Vehicle detection Unmanned aerial vehicles Infrared imaging Cameras Monitoring Infrared detectors Robots Helicopters Infrared sensors

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