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IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Radu Orghidan Joaquim Salvi El Mustapha Mouaddib Computer Vision and Robotics Group Institute of Informatics and Applications Girona Catalonia Spain

Catadioptric sensors are combinations of mirrors and lenses made in order to obtain a wide field of view. In this paper we propose a new sensor that has omnidirectional viewing ability and it also provides depth information about the nearby surrounding. The sensor is based on a conventional camera coupled with a laser emitter and two hyperbolic mirrors. Mathematical formulation and precise specifications of the intrinsic and extrinsic parameters of the sensor are discussed. Our approach overcomes limitations of the existing omni-directional sensors and eventually leads to reduced costs of production.

关键词： Mirrors Cameras Mathematical model Robot sensing systems Equations Laser modes Lasers

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Inertial navigation framework for multimodal underwater Graph SLAM

Inertial navigation framework for multimodal underwater Grap...

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OCEANS

作者： Pau Vial Miguel Castillón Narcís Palomeras Marc Carreras Pere Ridao Computer Vision and Robotics Research Institute (VICOROB) University of Girona Girona Spain

Robot localization is a fundamental task in achieving true autonomy for Autonomous Underwater Vehicles (AUV). If inertial measurements from an Inertial Measurement Unit (IMU) or a Doppler Velocity Log (DVL) want to be fused with some perception system, such us a multibeam sonar or several acoustic beacons; a full Simultaneous Localization And Mapping (SLAM) problem must be solved. In contrast to filters, in a full SLAM problem the whole robot trajectory is estimated and loop closure events can be detected and closed along it. Common Inertial Navigation Systems (INS), based on filters, only maintain the estimation of the current robot pose. Therefore, these systems cannot be directly used in a full SLAM problem. In this paper we present a graph solution to integrate all inertial measurements in a factor graph that can be extended to different perception modalities and it is solved by applying Smoothing and Mapping (SAM) [1]. The Preintegrated IMU factor, proposed by [2], is combined with priors for other inertial measurements that have been specially designed. This framework is tested on real data from sea experiments, showing how our proposal performance is similar to the estimation provided by high grade commercial INS products based on filters. However, our system has the advantage of allowing for fusion with exteroceptive sensors in SLAM.

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Classifying textures when seen from different distances

Classifying textures when seen from different distances

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作者： Lladó, X. Petrou, M. Centre for Vision Speech and Signal Processing University of Surrey Guildford GU2 7XH United Kingdom Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona 17071 Girona Spain

The purpose of this work is to analyse what happens to the surface information when the image resolution is modified. We deduce how the same surface appears if seen from different distances. Using 4-source Colour Photometric Stereo, which provides the surface shape and colour information, a method for predicting how surface texture looks like when changing the distance of the camera is presented. We demonstrate this technique by classifying textured surfaces seen from different distances than the textured surfaces in the database. © 2002 IEEE.

关键词： Classification (of information)

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Biomimetic Centering for Undulatory Robots

Biomimetic Centering for Undulatory Robots

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： M. Sfakiotakis D.P. Tsakiris A. Vlaikidis Computational Vision and Robotics Laboratory Institute of Computer Science FORTH Heraklion Greece

Substantial work exists in the undulatory robotics literature on the mechanical design, modeling, gait generation and implementation of robotic prototypes. However, there appears to have been relatively limited work on the use of exteroceptive sensors in control schemes leading to more complex reactive undulatory behaviors. This paper considers a biologically-inspired sensor-based centering behavior for undulatory robots, originally developed for nonholonomic mobile robots. Adaptation to the significantly more complex dynamics of undulatory locomotors highlights a number of issues related to the use of sensors (possibly distributed over the elongated body of the mechanism) for the generation of reactive behaviors, to biomimetic neuromuscular control and to formation control of multi-undulatory swarms. These issues are explored via computational tools specifically geared towards undulatory locomotion in robotics and biology

关键词： Biomimetics Robot sensing systems Biosensors Mobile robots Biological system modeling Prototypes Mechanical sensors Neuromuscular Biology computing Computational biology

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Multi-Representation Multi-Heuristic A* Motion Planning for a Dual-Arm Underwater Vehicle Manipulation System

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IFAC-PapersOnLine 2019年第21期52卷 205-210页

作者： Roger Pi Dina Youakim Patrick Cieslak Pere Ridao Computer Vision and Robotics Institute Universitat de Girona Pic de Peguera 1317003 Girona Spain

Recently, Intervention-Autonomous Underwater Vehicles (I-AUVs), started to grab researchers attention. While prior systems rely on variations of the task-priority redundancy control framework, our recent research demonstrated experimental results using search-based motion planning for a single arm floating-based intervention in known and unknown environments. In this work, we present a search-based motion planning algorithm applied to the coordinated manipulation problem of a dual-arm Intervention AUV, in an object transportation mission (e.g. pipe transportation). Two Challenges are considered: the high-dimensionality of the system, and the motion coordination between the mobile base and both working arms. The latter challenge is a major one if accurate execution is required, especially considering the floating nature of the AUV. Our approach relies on exploiting the loose coupling between the different system components. We show for the first time the use of a search-based planner on a dual-arm underwater manipulator. In addition, we support our claims with a simulated demonstration, successfully performing a coordinated dual-arm underwater intervention.

关键词： Underwater Manipulation Search-based Motion Planning dual-arm

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Supervised Local Subspace Learning for Continuous Head Pose Estimation

Supervised Local Subspace Learning for Continuous Head Pose ...

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IEEE Conference on computer vision and Pattern Recognition

作者： Dong Huang Markus Storer Fernando De la Torre Horst Bischof Robotics Institute Carnegie Mellon University Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781457703942

Head pose estimation from images has recently attracted much attention in computer vision due to its diverse applications in face recognition, driver monitoring and human computer interaction. Most successful approaches to head pose estimation formulate the problem as a nonlinear regression between image features and continuous 3D angles (i.e. yaw, pitch and roll). However, regression-like methods suffer from three main drawbacks: (1) They typically lack generalization and overfit when trained using a few samples. (2) They fail to get reliable estimates over some regions of the output space (angles) when the training set is not uniformly sampled. For instance, if the training data contains under-sampled areas for some angles. (3) They are not robust to image noise or occlusion. To address these problems, this paper presents Supervised Local Subspace Learning (SL~2), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL~2 learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting. Moreover, because SL~2 is a generative model, it can deal with image noise. Experimental results on the CMU Multi-PIE and BU-3DFE database show the effectiveness of our approach in terms of accuracy and computational complexity.

关键词： image noise pose estimation Head images computer human interaction Subspace image feature linear models Nonlinear Regression computer vision complexity classes

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A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation 37

A Single 2D Pose with Context is Worth Hundreds for 3D Human...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. © 2023 Neural information processing systems foundation. All rights reserved.

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A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

A new approach to segmentation based on fusing circumscribed...

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IEEE International Conference on Image Processing

作者： X. Munoz X. Cufi J. Freixenet J. Marti Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Catalonia Spain

ISBN: (纸本)0780362977

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed.

关键词： Image segmentation Image edge detection Layout Clustering algorithms computer vision Robot vision systems Informatics Application software Machine vision Proposals

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Robust Reference-Based Super-Resolution With Similarity-Aware Deformable Convolution

Robust Reference-Based Super-Resolution With Similarity-Awar...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Gyumin Shim Jinsun Park In So Kweon Robotics and Computer Vision Laboratory Korea Advanced Institute of Science and Technology Republic of Korea

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In this paper, we propose a novel and efficient reference feature extraction module referred to as the Similarity Search and Extraction Network (SSEN) for reference-based super-resolution (RefSR) tasks. The proposed module extracts aligned relevant features from a reference image to increase the performance over single image super-resolution (SISR) methods. In contrast to conventional algorithms which utilize brute-force searches or optical flow estimations, the proposed algorithm is end-to-end trainable without any additional supervision or heavy computation, predicting the best match with a single network forward operation. Moreover, the proposed module is aware of not only the best matching position but also the relevancy of the best match. This makes our algorithm substantially robust when irrelevant reference images are given, overcoming the major cause of the performance degradation when using existing RefSR methods. Furthermore, our module can be utilized for self-similarity SR if no reference image is available. Experimental results demonstrate the superior performance of the proposed algorithm compared to previous works both quantitatively and qualitatively.

关键词： Feature extraction Convolution Image reconstruction Image resolution Degradation Robustness Kernel

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Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

Improving Clustering Algorithms for Image Segmentation using...

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IEEE International Conference on Automation, Quality and Testing, robotics, AQTR

作者： Arnau Oliver Xavier Munoz Joan Batlle Lluis Pacheco Jordi Freixenet Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Spain

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

关键词： Clustering algorithms Image segmentation Partitioning algorithms Proposals Clustering methods computer vision Robot kinematics Robot vision systems Informatics Application software

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