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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

共 1407 条记录，以下是261-270 订阅

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Design of Real-time Steel Bars Recognition System Based on Machine vision 8

Design of Real-time Steel Bars Recognition System Based on M...

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8th international conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhao, Jianyu Xia, Xiangxiang Wang, Haodi Kong, Siwei Univ Jinan Sch Elect Engn Jinan Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781509007684

Currently, the bar counting is mainly completed by manpower, this method can make the workers fatigue easily, besides error counting, which can not guarantee the accurateness of packaging and match the high level of automatic equipment of steel rolling production line. To solve these problems, using machine vision and computer image processing technology, we design a real-time steel bars system. First we build up the hardware and software structure of system. Then we introduce the detailed working process, and design the detailed image processing algorithm. The results of experiments demonstrate the accuracy of bar counting in a single frame is up to 96% in this system, and its processing speed can meet the real-time requirements.

关键词： machine vision image segmentation bars recognition real-time

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Real Time Hand Gesture Recognition for Human computer Interaction 6

Real Time Hand Gesture Recognition for Human Computer Intera...

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IEEE 6th international Advance Computing conference (IACC)

作者： Agrawal, Rishabh Gupta, Nikita Savitribai Phule Pune Univ Dept Comp Engn Army Inst Technol Pune Maharashtra India

ISBN: (纸本)9781467382861

Most of the human computer interaction interfaces that are designed today require explicit instructions from the user in the form of keyboard taps or mouse clicks. As the complexity of these devices increase, the sheer amount of such instructions can easily disrupt, distract and overwhelm users. A novel method to recognize hand gestures for human computer interaction, using computer vision and image processing techniques, is proposed in this paper. The proposed method can successfully replace such devices (e.g. keyboard or mouse) needed for interacting with a personal computer. The method uses a commercial depth + rgb camera called Senz3D, which is cheap and easy to buy as compared to other depth cameras. The proposed method works by analyzing 3D data in real time and uses a set of classification rules to classify the number of convexity defects into gesture classes. This results in real time performance and negates the requirement of any training data. The proposed method achieves commendable performance with very low processor utilization.

关键词： Human computer Interaction (HCI) gesture recognition hand gestures convex hull convexity defects computer vision and image processing

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Research of the Defects in PET Preform 21

Research of the Defects in PET Preform

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21st international conference on Methods and Models in Automation and Robotics (MMAR)

作者： Laucka, Andrius Andriukaitis, Darius Markevicius, Vytautas Zilys, Mindaugas Kaunas Univ Technol Dept Elect Engn LT-51368 Kaunas Lithuania

ISBN: (纸本)9781509018666

image technologies nowadays are used not only for keeping personal events safe, but also are widely applied in conjunction with automated electronic systems. computer vision is widely used for inspection of the production quality in industries. Food industry is not an exception. Containers for food industry are made in very large quantities. This article contains of defect analysis of both external and side area of the bottleneck. Defects were divided into groups according to which the filters are created. For the control of PET preparation quality an automated computer vision algorithms were developed. The algorithms and methods were used for the detection of defective products mainly based on the image segmentation, digital production, erosion, smoothing. The most effective filters for the defect detection of the workpieces have been determined. It was carried out that efficiency of algorithms are close to 100 %.

关键词： PET digital image processing image segmentation computer vision smoothing methods

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Dermatological Disease Detection Using image processing and Machine Learning 3

Dermatological Disease Detection Using Image Processing and ...

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3rd international conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Kumar, Vinayshekhar Bannihatti Kumar, Sujay S. Saboo, Varun PES Inst Technol Dept Comp Sci Bengaluru Karnataka India

ISBN: (纸本)9781467391870

Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines computer vision and Machine Learning on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of Machine learning algorithms to identify diseases based on the histopathological attributes observed on analysing of the skin. Upon training and testing for the six diseases, the system produced an accuracy of up to 95 percent.

关键词： Dermatology image processing computer vision Machine Learning Data Mining Computational Intelligence Automated Disease Diagnosis

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Multiscale method for ridge detection on ophthalmological images 26

Multiscale method for ridge detection on ophthalmological im...

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26th international conference on computer Graphics and vision, GraphiCon 2016

作者： Mamaev, N.V. Yurin, D.V. Krylov, A.S. Laboratory of Mathematical Methods of Image Processing Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia

We propose a method for ridge detection and consider its application for detection of blood vessels on eye fundus images. The algorithm is based on multiscale application of non-maxima suppression to Laplacian of an image. The multiscale algorithm is based on the image pyramid similar to SIFT pyramid. This pyramid is used for a generalization of the multiscale non-maxima suppression method. Merging of the results of detection is performed using mathematical morphology methods. We test the proposed algorithm with DRIVE ophthalmological image database. © Proceedings of the 26th international conference on computer Graphics and vision, GraphiCon 2016. All rights reserved.

关键词： Mathematical morphology

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Ayurvedic Medicinal Plants Identification: A Comparative Study on Feature Extraction Methods 5th

Ayurvedic Medicinal Plants Identification: A Comparative Stu...

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5th international conference on computer vision and image processing, cvip 2020

作者： Ahila Priyadharshini, R. Arivazhagan, S. Arun, M. Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Sivakasi Tamil Nadu India

ISBN: (纸本)9789811610912

Proper identification of medicinal plants is essential for agronomists, ayurvedic medicinal practitioners and for ayurvedic medicines industry. Even though many plant leaf databases are available publicly, no specific standardized database is available for Indian Ayurvedic Plant species. In this paper, we introduce a publicly available annotated database of Indian medicinal plant leaf images named as MepcoTropicLeaf. The research work also presents the preliminary results on recognizing the plant species based on the spatial, spectral and machine learnt features on the selected set of 50 species from the database. To attain the machine learnt features, we propose a six level convolutional neural network (CNN) and report an accuracy of 87.25% using machine learnt features. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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A Bayesian Approach to Gaussian-Impulse Noise Removal Using Hessian Norm Regularization 1

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7th international conference on computer vision and image processing, cvip 2022

作者： Maji, Suman Kumar Saha, Anirban Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801106 India

ISBN: (数字)9783031314070

ISBN: (纸本)9783031314063

The problem of removing mixed noise from images is a challenging problem due to their ill-posed nature. In this paper, we propose a Bayesian technique for the removal of mixed Gaussian-Impulse noise from images. The proposed optimization problem is derived from the maximum a posteriori (MAP) estimates of the noise statistics and makes use of a total variation (TV) and a nuclear norm of the Hessian as its two regularization terms. While TV ensures smoothness to the solution, the use of Hessian takes into account detail preservation in the final optimized output. The proposed problem is then solved under the framework of primal-dual algorithms. Experimental evaluation shows that the proposed method can significantly improve the restoration quality of the images, compared to the existing techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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MS-Net: A CNN Architecture for Agriculture Pattern Segmentation in Aerial images 6th

MS-Net: A CNN Architecture for Agriculture Pattern Segmenta...

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6th international conference on computer vision and image processing, cvip 2021

作者： Bhagat, Sandesh Kokare, Manesh Haswani, Vineet Hambarde, Praful Kamble, Ravi SGGS Institute of Engineering and Technology Nanded431606 India Indian Institute of Technology Ropar140001 India

ISBN: (纸本)9783031113451

computer vision for crop sciences is overgrowing with the advancement in pattern recognition and deep learning. Agriculture pattern segmentation is an important application such as segmentation of cloud shadow, waterway, standing water, weed cluster, planter skip, double plant, etc. However, the segmentation of agriculture patterns is challenging due to multi-scale variations of patterns and considerable overlap between classes. Furthermore, size and shape variation, unclear boundaries, and missing edges make this task more complex. To address this problem, we proposed the encoder-decoder architecture with input as a multi-scale image pyramid to improve the multi-scale feature extraction of agriculture patterns. EfficientNetB7 is utilized as an encoder for efficient feature extraction. In addition, the proposed Lateral-output layer shows rich semantic features by aggregating low level and high level features from the decoder, shows improvement in dice score. The proposed research uses the Agriculture vision dataset with aerial farmland images. The proposed method outperformed different state-of-the-art methods in the literature for six agricultural patterns segmentation with a mean dice score of 74.78, 68.11, and 84.23 for RGB, NIR, and RGB + NIR images, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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An Edge detection technique with image segmentation using Ant Colony Optimization: A review

An Edge detection technique with image segmentation using An...

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Online international conference on Green Engineering and Technologies (IC-GET)

作者： Kaur, Simranpreet Kaur, Prabhpreet GNDU Dept CSE Amritsar Punjab India

ISBN: (纸本)9781509045563

The image processing is the strategy which is put on process different characteristics of the image. Many image processing based programs require the detection and the acceptance of the things. Segmentation is a vital task for such purposes. In computer vision, image segmentation is the task of splitting a digital image into numerous segments.. The goal of segmentation is to simplify and/or change the representation of a picture into anything that is more significant and easier to analyze. In that work, means of edge detection is analyzed which is predicated on picture segmentation. Ant Colony Optimization presents an alternative method of image edge detection. Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging conduct of ants to locate estimated answers to difficult optimization problems.

关键词： image processing image segmentation Edge detection Ant Colony Optimization

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Automated Cashew Kernel Grading Using Machine vision

Automated Cashew Kernel Grading Using Machine Vision

引用

international conference on Next Generation Intelligent Systems (ICNGIS)

作者： Arun, M. O. Nath, Aneesh G. Shyua, A. TKM Coll Engn Comp Sci & Engn Dept Kollam India

ISBN: (纸本)9781509008704

Quality of food and agricultural products is vital for farmers and consumers. Quality based classification of these products is being carried out manually in the industry which is tedious and expensive. computer vision systems can be used to automate the classification process. Automation can reduce the production cost and improve the overall quality. A computer vision system captures the image of the underlying object and transmits it to an image processor. The processor, after processing the image, presents it to a pattern recognizer. The recognizer performs the quality assessments and classifies the underlying object into pre-specified quality classes. Previous studies in this regard used minimal features to perform classification which reduces the accuracy. In the proposed approach more features are used which significantly improves the automation process. Automation process faces several challenges like identifying appropriate features and the best classifier. This study tries to grade cashew nuts based on external features like color, texture, shape and size. The effect of various pre-processing operations on the grading process is also studied. Five different classifiers were used and their performance in terms of accuracy is observed. Among the classifiers, Back Propagation Neural Network proved to be the most optimal.

关键词： Automation Machine Learning image processing

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