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IEEE International Conference on Image Processing

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Spain Department of Informatics Engineering Autonomous University of Madrid Spain

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.

关键词： Prototypes Gabor filters Pixel Feature extraction Filter bank Testing intelligent robots Image texture analysis Pattern recognition Image recognition

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A 3D local energy surface detector for confocal microscope imalges 3

A 3D local energy surface detector for confocal microscope i...

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3rd Australian and New Zealand Conference on intelligent Information Systems, ANZIIS 1995

作者： Pudney, Chris Kovesi, Peter Robbins, Ben Biomedical Confocal Microscopy Research Centre Department of Pharmacology United States Robotics and Vision Research Group Department of Computer Science University of Western Australia NedlandsWA6907 Australia

ISBN: (纸本)0864224303

The ability to detect features within confocal microscope images is important for the interpretation and analysis of such data. Most detectors are gradient-based, and so are sensitive to noise, and fail to accurately locate some feature types that are important in confocal microscopy. The local energy feature detector developed by Morrone and Owens marks locations where there is maximal congruence of phase in the Fourier components of an image. Points of maximal phase congruency occur at all common feature profiles: step and roof edges, line features and Mach bands. A 3D implementation of the local energy feature detector, suitable for confocal microscope data, is presented. The detector computes local energy by convolving an image with oriented pairs of 3D filters. The filters are 3D versions of Morlet wavelets. To increase the speed of the convolution, the filters are designed in frequency-space and multiplied by the image's Fourier transform. Results are presented for real confocal images and synthetic 3D image volumes. © 1995 IEEE. All rights reserved.

关键词： Microscopes

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Reducing the complexity of geometric selective disassembly

Reducing the complexity of geometric selective disassembly

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IEEE International Workshop on intelligent Robots and Systems (IROS)

作者： M.A. Garcia A. Larre B. Lopez A. Oller Department of Computer Science and Mathematics Intelligent Robotics and Computer Vision Group Tarragona Spain Department of Electrical Electronic and Automation Engineering Rovira i Virgili University Tarragona Spain

This paper presents an efficient technique for determining a low-cost disassembly sequence suitable to extract a subset of s components from an assembly containing n components, e of which are exterior (e/spl Lt/n). The most efficient solution to this so-called geometric selective disassembly problem is the wave propagation algorithm, which is reported to have a computational complexity of O(sn/sup 2/). Instead, the complexity of the proposed algorithm is O(enlogn) when s/spl Lt/n, and O(sn) when s/spl sime/n. Experimental results with synthetic 3D assemblies are presented.

关键词： Assembly intelligent robots robotics and automation Computational complexity Recycling Fasteners Polynomials Costs Robot vision systems computer vision

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On Adapting Pixel-based Classification to Unsupervised Texture Segmentation

On Adapting Pixel-based Classification to Unsupervised Textu...

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International Conference on Pattern Recognition

作者： Jaime Melendez Domenec Puig Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

ISBN: (纸本)9781424475421

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided.

关键词： Image segmentation Pixel Clustering algorithms Accuracy Feature extraction Classification algorithms Image edge detection

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A new methodology for evaluation of edge detectors

A new methodology for evaluation of edge detectors

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Domenec Puig Carme Julià Miguel Angel Garcia Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper defines a new methodology for evaluating edge detectors through measurements on edginess maps instead of on binary edge maps as previous methodologies do. These measurements avoid possible bias introduced by the application-dependent process of generating binary edge maps from edginess maps. The features of completeness, discriminability, precision and robustness, which a general-purpose edge detector must comply with, are introduced. The R, DS, P and FAR-measurements in addition to PSNR applied to the edginess maps are defined to assess the performance of edge detection. The R, DS, P and FAR-measurements can be seen as generalizations of previously proposed measurements on binary edge maps. Well-known and state-of-the-art edge detectors have been compared by means of the new proposed metrics. Results show that it is difficult for an edge detector to comply with all the proposed features.

关键词： Image edge detection Detectors intelligent robots Robustness Robot vision systems computer vision computer science mathematics Informatics Tin

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Robust color edge detection through tensor voting

Robust color edge detection through tensor voting

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IEEE International Conference on Image Processing

作者： Rodrigo Moreno Miguel Angel Garcia Domenec Puig Carme Julià Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

This paper presents a new method for color edge detection based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information via tensors in order to propagate them into a neighborhood through a voting process specifically designed for color edge detection by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Experiments show that the proposed algorithm is more robust and has a similar performance in precision when compared with the state-of-the-art.

关键词： Robustness Image edge detection Tensile stress Voting Colored noise Color Detectors Eigenvalues and eigenfunctions intelligent robots computer vision

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Robust Color Image Segmentation through Tensor Voting

Robust Color Image Segmentation through Tensor Voting

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International Conference on Pattern Recognition

作者： Rodrigo Moreno Miguel Angel Garcia Domenec Puig Intelligent Robotics and Computer Vision Group Department of Computer Science and Mathematics Rovira i Virgili University Tarragona Spain Department of Informatics Engineering Autonomous University of Madrid Madrid Spain

ISBN: (纸本)9781424475421

This paper presents a new method for robust color image segmentation based on tensor voting, a robust perceptual grouping technique used to extract salient information from noisy data. First, an adaptation of tensor voting to both image denoising and robust edge detection is applied. Second, pixels in the filtered image are classified into likely-homogeneous and likely-inhomogeneous by means of the edginess maps generated in the first step. Third, the likely-homosgeneous pixels are segmented through an efficient graph-based segmenter. Finally, a modified version of the same graph-based segmenter is applied to the likely-inhomogeneous pixels in order to obtain the final segmentation. Experiments show that the proposed algorithm has a better performance than the state-of-the-art.

关键词： Pixel Image segmentation Tensile stress Robustness Image color analysis Image edge detection Noise measurement

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Heterogeneous sensor fusion framework for autonomous mobile robot obstacle avoidance

Heterogeneous sensor fusion framework for autonomous mobile ...

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2010 10th International Conference on intelligent Systems Design and Applications, ISDA'10

作者： Zia, Ali Gulrez, Tauseef Chaudhry, Tayyab Intelligent Machines and Robotics Research Group Department of Computer Science COMSATS Institute of Information Technology Lahore M. A. Jinnah Campus Defence Road Lahore Pakistan

ISBN: (纸本)9781424481354

This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approach we reparameterize the 2D gaussian distribution of the laser free-configuration eigenspaces by vision saliency gaussian kernel function. The approach uses bi-sensor paradigm to achieve greater effective mapping of the environment and improved accuracy in obstacle position estimation. Where the laser lower dimensional manifolds provide an eigenvector which corresponds to the free configuration space of the high order geometric representation of the environment and vision based edge detection followed by the saliency mapping provides the road detection and existance of dynamic obstacles on the road. We have shown that while the vectorial combination of eigenvectors at discrete time scan-frames of laser data manifest a trajectory, and once followed and fused with the vision sensor data, enables mobile robot to build an efficient and accurate online environment map free of obstacles. We demonstrated this process using real-time NAVLAB CMU (Autonomous Jeep's) data-set which is a good representation of autonomous mobile robot's navigation in an urban environment. © 2010 IEEE.

关键词： Eigenvalues and eigenfunctions

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Source Camera Device Identification from Videos

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SN computer science 2022年第4期3卷 316页

作者： Bennabhaktula, Guru Swaroop Timmerman, Derrick Alegre, Enrique Azzopardi, George Information Systems Group at the Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Group for Vision and Intelligent Systems at the Department of Electrical Systems and Automation University of León León Spain Researcher at INCIBE (Spanish National Cybersecurity Institute) León Spain

Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the vision data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of 72.75 ± 1.1 % on the benchmark vision data set. Furthermore, we also achieve state-of-the-art accuracy of 71.75 % on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the vision and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs. © 2022, The Author(s).

关键词： Constrained ConvNet Digital video forensics Sensor pattern noise Source camera identification

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Rapid shallow coastal coral reef mapping using the teardrop system

Rapid shallow coastal coral reef mapping using the teardrop ...

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OCEANS 2012 MTS/IEEE Hampton Roads Conference: Harnessing the Power of the Ocean

作者： Corpuz, Francis James Naval Jr., Prospero Capili, Eusebio Jauod, Jaylord Judilla, Roel John Soriano, Maricor Vision and Image Processing Group National Institute of Physics University of the Philippines Diliman Quezon City Philippines Computer Vision and Machine Intelligence Group Department of Computer Science University of the Philippines Diliman Quezon City Philippines Mapua Robotics Center Institutional Laboratory Mgmt Office Mapua Institute of Technology Intramuros Manila Philippines

ISBN: (纸本)9781467308298

We present a rapid shallow coastal reef mapping system using a previously developed inexpensive diver less video capture system coupled with a fast-image mosaicking technique, using an affordable GPS logging device. The system replaces the traditional manta tow system of assessing and monitoring large areas of coastal coral reefs and at the same time provides historical visual record for future references. The system utilizes Teardrop, a hollow, teardrop-shaped that houses a commercial underwater point-and-shoot camera. The camera is set to video mode and captures video of the reef while the Teardrop is being towed by a wooden fishing boat. The resulting video is parsed into frames and mosaicked by using fast image labeling. The approximate location of the mosaicked images is recorded by using an inexpensive GPS logger. The coordinates are embedded on the mosaicked images and overlaid on mapping software such as Google Earth for visual record and spatial information integration. The same points are used to compute for the total distance covered by the capturing process. The whole process from coral reef video capture to mosaicking the images and finally embedding the mosaics in mapping software can be done in as short as 1 day, providing coastal managers and local government units with a tool to rapidly visualize and monitor shallow coral reefs. © 2012 IEEE.

关键词： Mapping

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