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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1858 条记录，以下是921-930 订阅

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Illumination invariant skin texture generation using CGAN from a single image for haptic augmented palpation 3

Illumination invariant skin texture generation using CGAN fr...

引用

3rd IEEE international conference on Robotic Computing (IRC)

作者： Kim, Donghyun Kim, Suneung Kim, Mingi Ko, Myeongseob Kim, Kwangtaek Korea Univ 3D Informat Proc Lab Seoul South Korea Incheon Natl Univ Dept Informat & Telecommun Engn Incheon South Korea

ISBN: (纸本)9781538692455

The problem of illumination normalization in the field of computer vision is a subject that has been studied steadily so far. However, the treatment of strong illumination has not been done to date. In this paper, we present a method of illumination normalization using a deep learning method, the conditional generative adversarial network (CGAN), to reconstruct accurate 3D skin textures from a single image toward efficient haptic palpation. After normalizing the illumination through the conditional generative adversarial network in which the input image is conditioned to obtain the desired output image, bilateral filtering that removes noise while preserving the boundaries enhances fine wrinkles. As the refinement process of enhancing skin textures, intrinsic image decomposition is performed to obtain a shading layer image with histogram equalization that is merged into the normalized image obtained by the illumination normalization process to enhance skin tactile properties (wrinkle and roughness). Through these processes, we can obtain a depth image with normalized illumination and enhanced skin wrinkle texture. Using this, the depth of the skin surface texture is restored in three dimensions precisely. The superiority of the illumination normalization method proposed in this paper over three other illumination normalization methods (CIDRE, LDCT, and TT) is verified through comparison. We also confirm the illumination normalization performance of our method through restoration of the three-dimensional skin surface.

关键词： learning illumination normalization skin texture restoration skin image processing haptic palpation

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Terminal bulb segmentation of caenorhabditis elegans under small samples based on two-stage u-net network 3

Terminal bulb segmentation of caenorhabditis elegans under s...

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3rd international conference on computer Science and Artificial Intelligence, CSAI 2019 and its Workshop - The 11th international conference on Information and Multimedia Technology, ICIMT 2019

作者： Cao, Si Liu, Jun Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Department of Computer Science and Technology Wuhan University of Science and technology Wuhan430070 China

ISBN: (纸本)9781450376273

Convolutional neural network based on machine learning has become one of the most popular methods of image recognition and segmentation, but it needs huge data samples to get better results. In this study, based on U-Net network, a two-stage convolution neural network method for automatic segmentation of Terminal bulb of Caenorhabditis elegans in small samples was proposed. The method solves the problem that traditional singlestage network cannot be implemented in small samples. The Dice coefficient reaches 89.5%, which is higher than that of the baseline U-Net method. © 2019 Association for Computing Machinery.

关键词： computer vision

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Development of low-cost embedded vision system with a case study on 1D barcode detection 3rd

Development of low-cost embedded vision system with a case s...

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3rd international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

作者： Mishra, Vaishali Kapadia, Harsh K. Zaveri, Tanish H. Pinnamaneni, Bhanu Prasad Tirupati Andhra Pradesh517502 India

ISBN: (纸本)9789811317415

In the trend of miniaturization and smart systems/devices, many industries are still working with comparatively large and costly computer-based system as compared to embedded systems. The work discussed in the paper focuses on development on a small, low-cost, less power-consuming embedded vision-based one-dimensional barcode detection and decoding system by fusion of camera and embedded system. 1D barcodes are prevalent in retail, pharma, automobile, and many other industries for automatic product identification. Real-time application of 1D barcode localization and decoding algorithm in Python using OpenCV library was developed. image processing task will be performed by embedded systems, which proves that the performance of embedded systems is comparable to a computer. Results of barcode detection and computation time comparison over different hardware platforms are discussed in the results. © 2019, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Intelligent Interactive Edge Detection Method of image 19

Intelligent Interactive Edge Detection Method of Image

引用

Proceedings of the 3rd international conference on Big Data Research

作者： Zhang Bo Pan Jingchang Jiang Gaoyu School of Mechanical Electrical & Information Engineering Shandong University Weihai China

ISBN: (纸本)9781450372015

Edge detection is a fundamental problem in image processing and computer vision, it plays an important role in the field of computer vision. Edge detection highlights significant changes in the image by identifying points in the digital image where the brightness changes significantly, and filters the secondary information while retaining the key features of the image, thus reducing the amount of *** on more than 3,000 historical geographic images of different time nodes, this study proposes a user-oriented interactive edge detection algorithm, establishes a spatially evolved vector model, extracts historical geographic boundary information, and internal location information and realize the digitization of space-time features. The main work of this study is as follows:1. Select the ROI area, specify the historical geographic area that needs to be extracted, filter the interference information, reduce the processing time, and increase the accuracy.2. The edge extraction algorithm based on Sobel operator is improved, and the edge information of historical geographic regionalization is extracted to realize the detailed modification and improvement of the edge information interactive. 3. Extract the location information within the historical geographic regionalization to fill the framewor of historical geographic regionalization, improve the details of the spatial framework, and facilitate accurate spatial location of information objects.

关键词： ROI Area Interactive Edge Detection Text Recognition Sobel Operator

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Comprehensive Study on Automated image Detection by using Robotics for Agriculture Applications 3

Comprehensive Study on Automated Image Detection by using Ro...

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3rd international conference on Electronics and Communication and Aerospace Technology, ICECA 2019

作者： Barhate, Deepti Nemade, Varsha Computer Department MPSTME SVKM's NMIMS Shirpur Maharashtra India

ISBN: (纸本)9781728101675

In Field of agriculture, the cultural manual inspection remains as a slow and tedious work along with the high error factor, in order to overcome this problem, this research work illustrates the different techniques used to initiate automated robots to perform the agricultural task. Intelligent system is required for cultivation, and harvesting of crops, which can identify the crops according to its features, margin, and colour. As the need of agriculture is Automation, Information technology and robotics for harvesting. This paper is the study of various technologies of robotics for cultivation. This paper shows how different robots work in the field of agriculture for capturing the fruits, cultivation of cotton, identification of weed and crops etc. by using the Machine learning, Convolutional Neural network(CNN), and multilayer preceptor. Further the study shows that the fruit detection was done by trained algorithm of image processing with extraction of only the required features. The colour, shape, margin, texture was provided as the input test *** main goal of this paper is to develop an automated fruit recognition and picker system using the laser sensor and machine vision that remains similar to that of human picker. In the field of agriculture, the cultural manual inspection remains as a slow and tedious work along with high error factor. To overcome this problem this paper shows the different techniques used to make the automated robots to perform the agricultural task. The intelligent system is required for cultivation, and harvesting of crops in order to identify the crops according to its features, margin, and colour. © 2019 IEEE.

关键词： Crops

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Diabetic Retinopathy Diagnosis with Ensemble Deep-Learning 2019

Diabetic Retinopathy Diagnosis with Ensemble Deep-Learning

引用

Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Snehil Kumar Jadavpur University Kolkata WB India

ISBN: (纸本)9781450376259

Diabetic retinopathy is an eye disease, is a condition in which retina is damaged due to diabetes mellitus. It is a major cause of blindness. Although many artificial intelligence methods has been applied to diabetic retinopathy diagnosis. This method is a new approach in this problem domain. It's early detection can help in tackling the future damages due to the disease. Here, model is ensemble of pre-existing GoogleNet, AlexNet and ResNet50 architectures. The best machine learning models that were used was GoogLeNet achieving highest accuracy for this job. Here, The results are standing out with the GoogLeNet's accuracy.

关键词： computer vision Transfer Learning Deep learning AI in healthcare Ensemble learning

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Research on Digital image Recognition Scheme Based on 3D Technology

Research on Digital Image Recognition Scheme Based on 3D Tec...

引用

IEEE international conference of Safety Produce Informatization (IICSPI)

作者： Yue Gao Beijing Institute of Technology Beijing China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

image is a reflection of objective reality in a certain sense, which conveys information to human beings in different modes. As an important information carrier, image is intuitive, rich in content and easy to communicate, which is an important content of multimedia. With the wide application of 3D images, image recognition based on 3D technology has attracted more researchers' attention, and there are more and more recognition methods. Human beings rely on two-dimensional images to perceive the three-dimensional world, and using computers to accomplish the same thing is the main research content in the field of computer vision. At any particular moment, the points on the target in the image correspond one-to-one with the points on the real three-dimensional target object. The images acquired by the imaging system are often not directly applicable due to various conditions and random interference. Based on the three-dimensional technology, this paper studies the digital image recognition scheme, and analyzes the image processing such as grayscale correction and noise filtering.

关键词： image segmentation image recognition Three-dimensional displays Digital images Imaging Interference Wavelet coefficients

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image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos 1

引用

3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

作者： Rohan, Raju Dhanakshirur ara Patel, Zeba Yadavannavar, Smita C. Sujata, C. Mudengudi, Uma KLE Technological University Hubli India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

In this paper, we propose a geometric feature and frame segmentation based approach for video summarization. Video summarization aims to generate a summarized video with all the salient activities of the input video. We propose to retain the salient frames towards generation of video summary. We detect saliency in foreground and background of the image separately. We propose to model the image as MRF (Markov Random Field) and use MAP (Maximum a-posteriori) as final solution to segment the image into foreground and background. The salient frame is defined by the variation in feature descriptors using the geometric features. We propose to combine the probabilities of foreground and background segments being salient using DSCR (Dempster Shafer Combination Rule). We consider the summarized video as a combination of salient frames for a user defined time. We demonstrate the results using several videos in BL-7F dataset and compare the same with state of art techniques using retention ratio and condensation ratio as quality parameters. © Springer Nature Singapore Pte Ltd, 2019.

关键词： image segmentation

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Illumination Compensation And image Denoising for Low-Light images Based on Deep Learning 2019

Illumination Compensation And Image Denoising for Low-Light ...

引用

Proceedings of the 3rd international conference on vision, image and Signal processing

作者： Hong Li Yao Xia Guoqing Yang Pan Lv Zhejiang University Hangzhou China

ISBN: (纸本)9781450376259

image denoising is one of the basic low-level computer vision problems, but low-light denoising is challenging due to low photon count and low SNR. Therefore, we propose an end-to-end encoded and decoded network of illumination compensation and image denoising in low-light condition based on deep learning, which is used to denoise low-light images and adaptively brighten images without over-amplifying the brighter part of the images with high dynamic *** the network, the illumination compensation branch network eliminates the disadvantage that the magnification must be selected externally. Different simulation gain and exposure time are used to train the multi-light compensation coefficient, which can eliminate the residual errors caused by inaccurate gain and various exposure time effectively. The results show that the model is suitable for the recovery and reconstruction of natural low-light images with different degrees of degradation due to the advantages of flexibility and data driving.

关键词： Low Photon Count Low Snr image Denoising Illumination Compensation

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Dynamic image Networks for Human Fall Detection in 360-degree Videos 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

作者： Saurav, Sumeet Madhu Kiran, T.N.D. Sravan Kumar Reddy, B. Sanjay Srivastav, K. Singh, Sanjay Saini, Ravi CSIR-CEERI Pilani India Birla Institute of Technology and Sciences Pilani India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

Detection of falls of elderly people is a trivial yet an immediate problem due to the growing age of the population. This demands the need for autonomous self care systems for providing a quick assistance. The three basic approaches used for fall detection include non-invasive vision based devices, ambient based devices and wearable devices. The paper tries to improve upon the state-of-art of accuracy to 98% using vision based system. This was achieved through transfer learning by extending the idea of action recognition using dynamic images which is a standard RGB image containing the appearance and dynamics of a whole video sequence. Such information is vital in dealing with applications like human action recognition. Since we are also looking for a cheap and scalable solution, the use of a 360∘ camera seems reasonable and reliable. The top view provided by this camera gives a better perspective than any other alternatives by giving an un-obstructive view of the subjects. © Springer Nature Singapore Pte Ltd, 2019.

关键词： Fall detection

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