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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Liu, Guodong Jiang, Guolai Xiong, Rong Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728163215

Binocular depth estimation is a hot research topic in computer vision. Traditional methods need high precision camera calibration and key point matching, but the results are not ideal. In this paper, we introduce an approach of binocular depth estimation method based on deep learning. A new convolutional neural network is designed, which consists of two sub-networks. The first sub-network is a deep network with Siamese branches and 3D convolutional layer, it learns parallax and global information and generates a global depth estimation result in low resolution. The second is a fully convolutional deep network, which reconstructions the depth map to original resolution. The two sub-networks are connected by a pool pyramid. Experiments are taken on the Middlebury Stereo Dataset show that the proposed method can generate much more accurate depth image than traditional methods. © 2019 IEEE.

关键词： Convolution

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Discriminating the presence of the cerebral aneurysm using shape features obtained from medical images of the cerebral vessel

Discriminating the presence of the cerebral aneurysm using s...

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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Kikuchi, Kosei Yagi, Takanobu Rong, Xu Ohya, Jun Department of Modern Mechanical Engineering WASEDA University Japan WASEDA University. Japan

Towards the establishment of the preventive medical care for the cerebral aneurysm, this paper proposes an SVM based method for building a discrimination function that classifies the presence or absence of the cerebral aneurysm using the cerebral blood vessel's shape features obtained from medical images such as MR images. using the discrimination function, this paper explores how much each feature affects the onset of the cerebral aneurysm. This paper deals with the internal carotid artery (ICA). The blood vessel (ICA)'s shape features are extracted from medical images of 18 persons without cerebral aneurysm and 13 patients with a cerebral aneurysm. From the medical image, the cross sections and centerline of the ICA are obtained. The cross sections are divided into nine sections along the centerline. Shape features such as the cross sectional area, its circularity, curvature, torsion, length of the centerline and branch angles are obtained in each section;as a total, 113 features including the mean and variance of some features in each section are used for building the SVM. As a result of conducting the experiments, the accuracy for discriminating the presence/absence of the aneurysm by the SVM is 90.3%. In the obtained discrimination function, the coefficient values of the function can be considered how much the features affect the onset of the aneurysm. The features that could significantly cause the onset of the cerebral aneurysm are clarified, and the reasons why these features are significant are discussed. © 2018, Society for Imaging Science and Technology.

关键词： Blood vessels

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Predicting rapid fire growth (flashover) using conditional generative adversarial networks

Predicting rapid fire growth (flashover) using conditional g...

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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Yun, Kyongsik Bustos, Jessi Lu, Thomas Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: Firefighters. Firefighters' safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover ("angel fingers") and can be quantified by color, size, and shape. using a color video stream from a firefighter's body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred. © 2018, Society for Imaging Science and Technology.

关键词： computer vision

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Robotic assembly of smartphone back shells with eye-in-hand visual servoing

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robotics AND computer-INTEGRATED MANUFACTURING 2018年 50卷 102-113页

作者： Chang, Wen-Chung Natl Taipei Univ Technol Dept Elect Engn Taipei Tech Box 2125 Taipei 10608 Taiwan

Research concerning smartphone assembly has attracted increasing attention from the manufacturing industry. This paper presents an automatic back shell assembly system using real-time visual servoing in an eye-in-hand configuration. Look-then-move and look-and-move control approaches are sequentially proposed to handle distal and proximal assembly tasks, respectively. During distal assembly, binocular vision is employed to assist a robotic manipulator to locate and reconstruct the positions and orientations of a cellular phone and its back shell in the work space. After lifting the back shell up and then reaching the top of the cellular phone, there is no binocular field of view. A seemingly novel way of 3-D reconstruction by two uniocular fields of view of the two cameras is proposed. With the pair of monocular vision sensors, two monocular visual servo control laws are employed sequentially to perform the proximal assembly task autonomously. Based on the experimental results, the robotic manipulator can be driven to hold the back shell by a vacuum absorption device and position it precisely onto the smartphone to accomplish the automatic assembly task even if the vision system is only roughly calibrated. The proposed control approach for distal and proximal assembly appears to be efficient, flexible, and reliable for potential applications in industrial manufacturing. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Eye-in-hand vision Manipulation Robotic assembly Real-time vision Visual servoing

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Modeling Traffic Scenes for intelligent Vehicles using CNN-Based Detection and Orientation Estimation

Modeling Traffic Scenes for Intelligent Vehicles Using CNN-B...

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3rd Iberian robotics Conference (Robot)

作者： Guindel, Carlos Martin, David Maria Armingol, Jose Univ Carlos III Madrid Intelligent Syst Lab LSI Res Grp Leganes Spain

ISBN: (纸本)9783319708362;9783319708355

Object identification in images taken from moving vehicles is still a complex task within the computer vision field due to the dynamism of the scenes and the poorly defined structures of the environment. This research proposes an efficient approach to perform recognition on images from a stereo camera, with the goal of gaining insight of traffic scenes in urban and road environments. We rely on a deep learning framework able to simultaneously identify a broad range of entities, such as vehicles, pedestrians or cyclists, with a frame rate compatible with the strong requirements of onboard automotive applications. The results demonstrate the capabilities of the perception system for a wide variety of situations, thus providing valuable information to understand the traffic scenario.

关键词： Object detection Viewpoint estimation Deep learning intelligent vehicles

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Canonical Correlation-Based Feature Fusion Approach for Scene Classification 1

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17th International Conference on intelligent Systems Design and applications, ISDA 2017

作者： Arunnehru, J. Yashwanth, A. Shammer, Shaik Department of Computer Science and Engineering SRM University Vadapalani Campus ChennaiTamilnadu India

ISBN: (数字)9783319763484

ISBN: (纸本)9783319763477

vision-based scene recognition and analysis is an emerging field and actively conceded in computer vision and robotics area. Classifying the complex scenes in a real-time environment is a challenging task to solve. In this paper, an indoor and outdoor scene recognition approach by linear combination (fusion) of global descriptor (GIST) and Local Energy based Shape Histogram (LESH) descriptor with Canonical Correlation Analysis (CCA) is proposed. The experiments have been carried out using publicly available 15-dataset and the fused features are modeled by Random forest and K-Nearest Neighbor for classification. In the experimental results, K-NN exhibits the good performance in our proposed approach with an average accuracy rate of 81.62%, which outperforms the random forest classifier. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Decision trees

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Bio-inspired Algorithms for Engineering

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2018年

作者： Alma Y. Alanis Nancy Arana-Daniel Carlos López-Franco

ISBN: (数字)9780128137895

ISBN: (纸本)9780128137888

Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control.

关键词： computer algorithms Natural computation Evolutionary computation Natural computation Evolutionary computation

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A Survey of computer vision Based Corrosion Detection Approaches 2nd

A Survey of Computer Vision Based Corrosion Detection Approa...

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2nd International Conference on Information and Communication Technology for intelligent Systems (ICTIS)

作者： Ahuja, Sanjay Kumar Shukla, Manoj Kumar Amity Univ AIIT Noida Uttar Pradesh India Amity Univ ASET Noida Uttar Pradesh India

ISBN: (纸本)9783319636450;9783319636443

There are various destructive as well as non-destructive techniques available to detect corrosion in metallic surfaces. Digital Image Processing is widely being used for the corrosion detection in metallic surface. This non-destructive approach provides cost effective, fast and reasonably accurate results. Several algorithms have been developed by different researchers and research groups for detecting corrosion using digital image processing techniques. Several algorithms related to color, texture, noise, clustering, segmentation, image enhancement, wavelet transformation etc. have been used in different combinations for corrosion detection and analysis. This paper reviews the different image processing techniques and the algorithms developed and used by researchers in various industrial applications.

关键词： computer vision Image processing Corrosion detection

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Assessment of object detection using deep convolutional neural networks 2nd

Assessment of object detection using deep convolutional neur...

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2nd International Conference on intelligent Computing and Communication, ICICC 2017

作者： Pathak, Ajeet Ram Pandey, Manjusha Rautaray, Siddharth Pawar, Karishma University Bhubaneswar India Pune India

ISBN: (纸本)9789811072444

Detecting the objects from images and videos has always been the point of active research area for the applications of computer vision and artificial intelligence namely robotics, self-driving cars, automated video surveillance, crowd management, home automation and manufacturing industries, activity recognition systems, medical imaging, and biometrics. The recent years witnessed the boom of deep learning technology for its effective performance on image classification and detection challenges in visual recognition competitions like PASCAL VOC, Microsoft COCO, and ImageNet. Deep convolutional neural networks have provided promising results for object detection by alleviating the need for human expertise for manually handcrafting the features for extraction. It allows the model to learn automatically by letting the neural network to be trained on large-scale image data using powerful and robust GPUs in a parallel way, thus, reducing training time. This paper aims to highlight the state-of-the-art approaches based on the deep convolutional neural networks especially designed for object detection from images. © 2018, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Line-scan stereo using binary descriptor matching and regularization

Line-scan stereo using binary descriptor matching and regula...

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intelligent robotics and industrial applications using computer vision 2017, iriacv 2017

作者： Huber-Mörk, Reinhold Valentín, Kristián Blaschitz, Bernhard Stolc, Svorad Center for Vision Automation and Control AIT Austrian Institute of Technology Vienna Austria

We present a line-scan stereo system and descriptor-based dense stereo matching for high-performance vision applications. Additionally we introduce a post-processing step based on total variation (TV) regularization for robust disparity estimation. Descriptor-based matching utilizes the Stochastic Binary Local Descriptor (STABLE). The performance of STABLE was shown to be superior to other binary descriptors, both w.r.t. stereo reconstruction quality as well as runtime performance. Regularized estimation of disparity maps is suggested as a hierarchical and iterative post-processing procedure where the Pseudo-Huber-TV norm was employed. We describe the hardware setup consisting of two line-scan cameras mounted in a car trailer and observing the road surface. Presented are results of 3D road surface reconstruction which are used in applications of road infrastructure maintenance. © 2017, Society for Imaging Science and Technology.

关键词： Light trailers

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