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International Conference on robotics, computer vision and intelligent Systems (ROBOVIS)

作者： Elek, Renata Nagyne Karoly, Artur, I Haidegger, Tunas Galambos, Peter Obuda Univ Univ Res & Innovat Ctr Antal Bejczy Ctr Intelligent Robot Budapest Hungary Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ John von Neumann Fac Informat Budapest Hungary

ISBN: (纸本)9789897584794

Optical flow is an established tool for motion detection in the visual scene. While optical flow algorithms usually provide accurate results, they can not make a difference between image-space displacements originated from moving objects in the space and the ego-motion of the moving viewpoint. In the case of optical flow-based moving object segmentation, camera ego-motion compensation is essential. Hereby, we show the preliminary results of a moving viewpoint optical flow ego-motion filtering method, using two dimensional optical flow, image depth information and the camera holder robot arm's state of motion. We tested its accuracy through physical experiments, where the camera was fixed on a robot arm, and a test object was attached onto an other robot arm. The test object and the camera were moved relative to each other along given trajectories in different scenarios. We validated our method for optical flow background filtering, which showed 94.88% mean accuracy in the different test cases. Furthermore, we tested the proposed algorithm for moving object state of motion estimation, which showed high accuracy in the case of translational and rotational movements without depth variation, but lower accuracy, when the relative motion produced change in depth, or the camera and the moving object move in the same directions. The proposed method with future work including outlier filtering and optimisation could become useful in various robot navigation applications and optical flow-based computer vision problems.

关键词： Optical Flow Ego-motion Velocity Compensation Moving Object Segmentation robotics

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Locating mechanical switches using RGB-D sensor mounted on a disaster response robot

Locating mechanical switches using RGB-D sensor mounted on a...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Kanda, Takuya Miyakawa, Kazuya Hayashi, Jeonghwang Ohya, Jun Ogata, Hiroyuki Hashimoto, Kenji Sun, Xiao Matsuzawa, Takashi Naito, Hiroshi Takanishi, Atsuo Department of Integrative Bioscience and Biomedical Engineering Waseda University Tokyo Japan Department of Modern Mechanical Engineering Waseda University Tokyo Japan Department of System Design Engineering Seikei Univeristy Tokyo Japan Department of Mechanical Engineering Informatics Meiji University Kanagawa Japan

To achieve one of the tasks required for disaster response robots, this paper proposes a method for locating 3D structured switches' points to be pressed by the robot in disaster sites using RGBD images acquired by Kinect sensor attached to our disaster response robot. Our method consists of the following five steps: 1)Obtain RGB and depth images using an RGB-D sensor. 2) Detect the bounding box of switch area from the RGB image using YOLOv3. 3)Generate 3D point cloud data of the target switch by combining the bounding box and the depth image.4)Detect the center position of the switch button from the RGB image in the bounding box using Convolutional Neural Network (CNN). 5)Estimate the center of the button's face in real space from the detection result in step 4) and the 3D point cloud data generated in step3) In the experiment, the proposed method is applied to two types of 3D structured switch boxes to evaluate the effectiveness. The results show that our proposed method can locate the switch button accurately enough for the robot operation. © 2020 Society for Imaging Science and Technology.

关键词： Robots

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Deep learning method for height estimation of sorghum in the field using LiDAR

Deep learning method for height estimation of sorghum in the...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Waliman, Matthew Zakhor, Avideh University of California Berkeley BerkeleyCA United States

Automatic tools for plant phenotyping have received increased interest in recent years due to the need to understand the relationship between plant genotype and phenotype. Building upon our previous work, we present a robust, deep learning method to accurately estimate the height of biomass sorghum throughout the entirety of its growing season. We mount a vertically oriented LiDAR sensor onboard an agricultural robot to obtain 3D point clouds of the crop fields. From each of these 3D point clouds, we generate a height contour and density map corresponding to a single row of plants in the field. We then train a multiview neural network in order to estimate plant height. Our method is capable of accurately estimating height from emergence through canopy closure. We extensively validate our algorithm by performing several ground truthing campaigns on biomass sorghum. We have shown our proposed approach to achieve an absolute height estimation error of 7.47% using ground truth data obtained via conventional breeder methods on 2715 plots of sorghum with varying genetic strains and treatments. © 2020, Society for Imaging Science and Technology.

关键词： Plant shutdowns

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Void detection and fiber extraction for statistical characterization of fiber-reinforced polymers

Void detection and fiber extraction for statistical characte...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Aguilar, Camilo Hanhan, Imad A. Agyei, Ronald F. Sangid, Michael D. Comer, Mary School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Aeronautics and Astronautics Purdue University West LafayetteIN United States

In this paper we propose a surrogate approach to extract fibers and voids from polymer matrix composites by combining results obtained from model-based methods to train convolutional neural networks. This approach focuses on microscopy images where labeled data is not readily available, but purely model based approaches can be too slow due to their computational complexity. In addition, we propose an encoder-decoder alternative to a fiber instance segmentation paradigm, showing a speedup in training and inference times without a significant decrease in accuracy with respect to alternative methods. The neural networks approach represent a significant speedup over model based approaches and can correctly capture most fibers and voids in large volumes for further statistical analysis of the data. © 2020, Society for Imaging Science and Technology.

关键词： Fibers

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Skin chromophore estimation from mobile selfie images using constrained independent component analysis

Skin chromophore estimation from mobile selfie images using ...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Polanía, Luisa F. Bala, Raja Purwar, Ankur Matts, Paul Maltz, Martin

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color;and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a low-dimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores. © 2020, Society for Imaging Science and Technology.

关键词： Independent component analysis

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Hazmat label recognition and localization for rescue robots in disaster scenarios

Hazmat label recognition and localization for rescue robots ...

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2019 intelligent robotics and industrial applications using computer vision Conference, iriacv 2019

作者： Edlinger, Raimund Zauner, Gerald Zauner, Michael University of Applied Sciences Upper Austria Wels Austria

Firefighting and rescue live victims operations are inherently dangerous, but the imminent danger of release of a hazardous substance creates an additional risk. Thus, identification of hazardous materials during robot assisted search and rescue missions can help e.g. firefighters or rescue teams to improve such rescue operations. The paper deals with the development of such a robotic machine vision system for hazmat label recognition. Classical computer vision methods but also state-of-the-art deep learning based detection algorithms were implemented and evaluated. Special focus was put on the robustness of detection and recognition with limited hardware resources and the influence of background image structures and light conditions. © 2019, Society for Imaging Science and Technology.

关键词： computer vision

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Extracting cow point clouds from multi-view RGB images with an improved YOLACT plus plus instance segmentation

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EXPERT SYSTEMS WITH applications 2023年第1期230卷

作者： Yang, Guangyuan Li, Rong Zhang, Shujin Wen, Yuchen Xu, Xingshi Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligent Yangling 712100 Shaanxi Peoples R China

Obtaining shape features from a farm environment is the basis for animal size measurement, body condition scoring, weight prediction, and individual identification. To improve the robustness of point cloud segmentation and fully utilize 2D (Two dimension) and 3D (Three dimension) information for segmenting animal shape data, a novel pixel-level segmentation method of cow point clouds was proposed by fusing real images with high resolution and point clouds. First, the 3D point clouds of cows were generated from the multi-view images by using the Structure from Motion (SfM) algorithm, and the corresponding relationship between the point clouds and the real image was established by the camera interior and exterior parameters. Second, for 2D image segmentation, the improved, single-stage instance segmentation algorithm YOLACT++ (You Only Look At CoefficienTs) was applied to improve the segmentation accuracy of cow body regions, and the average precision of multi-view images was 85.9%. Last, in point cloud segmentation, the image segmentation results of the cows were mapped to 3D point clouds by the correspondence between point clouds and images, and the visual points of different viewpoints were fused to obtain the 3D point cloud of the target cow. The average relative error of truncated areas between the proposed method and manual segmentation of the point cloud was 2.18%, and the change in the truncated area caused by the increase in images was less than 5% with the appropriate number of images (20-40). The results demonstrated the effectiveness of the proposed method in 3D point cloud segmentation of cows and its potential application in animal shape acquisition.

关键词： Point cloud segmentation Multi-view images Instance segmentation Dairy cow computer vision

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Automated optical inspection for abnormal-shaped packages

Automated optical inspection for abnormal-shaped packages

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2019 intelligent robotics and industrial applications using computer vision Conference, iriacv 2019

作者： Lin, Wei-Yu Hsu, Chen-Tao Chang, Chi-Chun Chuang, Jen-Hui Department of Computer Science National Chiao Tung University of Hsinchu Taiwan

In this paper, we develop an automated optical inspection method to detect yarn packages' defect. Although textile industry is regarded as a traditional industry, many new technologies, e.g., computer vision detection algorithms, are applied to this industry in recent years. Yarn packages are the semi-finished good of textile industry. Various factors may cause abnormal-shaped packages. In this study, we develop three defect detection algorithms to extract abnormal-shape packages. These algorithms can help manufacturer to avoid the disadvantages of human inspection effectively and improve the productive quality. © 2019, Society for Imaging Science and Technology.

关键词： Textile industry

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Optical system for industrial camera that achieves both close minimum object distance and high resolution

Optical system for industrial camera that achieves both clos...

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2019 intelligent robotics and industrial applications using computer vision Conference, iriacv 2019

作者： Sudoh, Yoshifumi Ricoh Co. Ltd. Ebina-shi Kanagawa Japan

In factories, automatic inspection is performed using images that are acquired using industrial cameras. In these industrial cameras, lenses with various focal lengths are used according to their intended purposes. It is important that the optical systems of industrial cameras have small outer diameters, close minimum object distances and high resolution. In an optical system with a long focal length, the movement of the moving group increases during focusing, and this movement makes it difficult to reduce the minimum object distance. In addition, to achieve higher resolution, it is important to suppress the degradation that occurs because of machining errors or assembly errors. To solve these problems, we developed a floating focus system in which the diaphragm is fixed, and we introduced a moving group that is held using a helicoid and a fitting structure. © 2019, Society for Imaging Science and Technology.

关键词： Cameras

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Detection and characterization of rumble strips in roadway video logs

Detection and characterization of rumble strips in roadway v...

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2020 intelligent robotics and industrial applications using computer vision Conference, iriacv 2020

作者： Aykac, Deniz Karnowski, Thomas Ferrell, Regina Goddard, James S. Electrical and Electronics Systems Research Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

State departments of transportation often maintain extensive "video logs"of their roadways that include signs, lane markings, as well as non-image-based information such as grade, curvature, etc. In this work we use the Roadway Information Database (RID), developed for the Second Strategic Highway Research Program, as a surrogate for a video log to design and test algorithms to detect rumble strips in the roadway images. Rumble strips are grooved patterns at the lane extremities designed to produce an audible queue to drivers who are in danger of lane departure. The RID contains 6,203,576 images of roads in six locations across the United States with extensive ground truth information and measurements, but the rumble strip measurements (length and spacing) were not recorded. We use an image correction process along with automated feature extraction and convolutional neural networks to detect rumble strip locations and measure their length and pitch. Based on independent measurements, we estimate our true positive rate to be 93% and false positive rate to be 10% with errors in length and spacing on the order of 0.09 meters RMS and 0.04 meters RMS. Our results illustrate the feasibility of this approach to add value to video logs after initial capture as well as identify potential methods for autonomous navigation. © 2020 Society for Imaging Science and Technology.

关键词： computer vision

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