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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...

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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...

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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

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.

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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Measuring (In)variances in convolutional networks 7th

Measuring (In)variances in convolutional networks

引用

7th International Conference on Cloud Computing and Big Data, JCC and BD 2019

作者： Quiroga, Facundo Torrents-Barrena, Jordina Lanzarini, Laura Puig, Domenec Instituto de Investigación en Informática LIDI Facultad de Informática Universidad Nacional de La Plata La Plata Argentina Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona Spain

ISBN: (纸本)9783030277123

Convolutional neural networks (CNN) offer state-of-the-art performance in various computer vision tasks such as activity recognition, face detection, medical image analysis, among others. Many of those tasks need invariance to image transformations (i.e., rotations, translations or scaling). This work proposes a versatile, straightforward and interpretable measure to quantify the (in)variance of CNN activations with respect to transformations of the input. Intermediate output values of feature maps and fully connected layers are also analyzed with respect to different input transformations. The technique is applicable to any type of neural network and/or transformation. Our technique is validated on rotation transformations and compared with the relative (in)variance of several networks. More specifically, ResNet, AllConvolutional and VGG architectures were trained on CIFAR10 and MNIST databases with and without rotational data augmentation. Experiments reveal that rotation (in)variance of CNN outputs is class conditional. A distribution analysis also shows that lower layers are the most invariant, which seems to go against previous guidelines that recommend placing invariances near the network output and equivariances near the input. © Springer Nature Switzerland AG 2019.

关键词： Neural networks

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Ridge polynomial neural network with error feedback for recursive multi-step forecast strategy: A case study of carbon dioxide emissions forecasting 2

Ridge polynomial neural network with error feedback for recu...

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2nd International Conference on computer and Information Sciences, ICCIS 2020

作者： Waheeb, Waddah Shah, Habib Jabreel, Mohammed Puig, Domenec School of Computing Asia Pacific University of Technology Innovation Kuala Lumpur Bukit Jalil57000 Malaysia College of Computer Science King Khalid University Department of Computer Science Abha62529 Saudi Arabia IRCV - Intelligent Robotics and Computer Vision Group Universitat Rovira i Virgili Tarragona43007 Spain

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

Neural networks (NNs) have been used extensively for forecasting problems. NN with error feedbacks is a type of NNs that showed more accurate forecasts compared to feedforward NNs and NNs with output feedbacks with some forecasting problems. The main issue with NN s with error feedbacks appears when there is a need for recursive multi-step forecast strategy because the observed values must be known in order to calculate network errors. This paper proposes to use the last calculated error after finishing training NNs with error feedbacks because the observed values are unknown. This last calculated error is used as a fixed value when producing forecasts using recursive multi-step forecast strategy. For that, this paper investigated this simple solution with a NN with error feedback called the ridge polynomial neural network with error feedback (RPNN-EF). Carbon dioxide emissions for three countries in the organization of the petroleum exporting countries (OPEC) were used in this investigation. The forecasting accuracy of RPNN-EF was compared with seven forecasting methods. According to the obtained results, on average, the proposed solution produces reasonable forecasts compared to the seven forecasting methods. Therefore, this solution can be suggested for NNs with error feedbacks for recursive multi-step forecast strategy. © 2020 IEEE.

关键词： Polynomials

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