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

共 1646 条记录，以下是1421-1430 订阅

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Feature based Fruit Quality Grading System using Support Vector Machine

Feature based Fruit Quality Grading System using Support Vec...

引用

IEEE international conference on Recent Trends in Electronics, Information & Communication Technology

作者： Shweta S. Deulkar Sunita S. Barve IT Academy of Engineering Alandi Pune India

ISBN: (数字)9781538624401

ISBN: (纸本)9781538624418

The conventional post-harvesting process of horticultural fruit and vegetables is exorbitant and highly subjective which susceptible to errors. This process is extremely unreliable in terms of accuracy, throughput, speed and cost. An automated fruit grading approach is desirable to reduce the manual efforts and increase the efficiency. In this paper, we are developing a computer vision based an image acquisition and classification system for fruit grading. This system is divided into three modules image pre-processing, image segmentation and Classifier. In the first phase, we are extracting the image features such as image quality, area, perimeter, mean, variance, color, intensity. Next Otsu algorithm works on automatic threshold calculation and image binarization part. In the second phase k-means clustering is used for image segmentation. In last phase the vectors of color, intensity, segments, and image quality feature were utilized for training of the support vector machine structure. The image k-means clustering approach is embedded with support vector machine classifier to improve the speed and accuracy of the classifier. As a result, the system will be able to classify the fruit into three different categories. The proposed algorithm is implemented with an example of tomato fruit grading. The experiments consider real tomato image dataset of 200 samples.75% of the total dataset is used for training and remaining 25% used for testing purpose. Training dataset is distinguished into three categories depending on the maturity of tomato.

关键词： image color analysis Support vector machines Feature extraction Classification algorithms image segmentation Clustering algorithms Histograms

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Diabetic Retinopathy Identification using Deep Believe Network 3

Diabetic Retinopathy Identification using Deep Believe Netwo...

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3rd international conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Syahputra, M.F. Rahmah, M. Jaya, I. Andayani, U. Abdullah, D. Sriadhi, S. Lalusu, E.Y. Tongko, M. Cahya, B.D. Fardian, N. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Public Health Universitas Tompotika Luwuk Banggai Indonesia Department of Nutrition Universitas Malikussaleh Aceh Utara Indonesia

Diabetic retinopathy is an eye disease caused by swelling in blood vessels. If it gets worse the blood vessels will rupture and this is the main factor of blindness. This disease is rarely found in children and symptoms can be seen during puberty. One way to find out this disease is a wide and comprehensive pupil jerky eye examination performed by an ophthalmologist manually. Manual examination takes a long time and can cause misidentification because the similarity of characteristics of diabetic retinopathy is difficult to see directly. Because of this problem a method is needed that can facilitate the eye doctor in identifying diabetic retinopathy by simply inserting a retinal image into the system. The method proposed in this study is Deep Belief Network (DBN). The steps taken before identification are the image of the retina undergoing pre-processing in the form of grayscale, median filter, contrast stretching, morphological close operation and feature extraction using the Grey level Counselling Matrix (GLCM). In this study it was shown that the proposed method was able to identify diabetic retinopathy with 84% accuracy, sensitivity 93%, and specificity 70%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Blood vessels

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On Real-Time Object Recognition by Single image Dehazing Method Using Deep Learning Approach

On Real-Time Object Recognition by Single Image Dehazing Met...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Shruti Pathak Amit Doegar Department of Computer Science National Institute of Technical Teachers Training and Research Chandigarh India

In the past decade, various haze removal techniques have been widely reported for object recognition. But hitherto little has been identified on the use of single image dehazing using transfer learning approach for object detection. Single image dehazing is an emerging computer vision technology which offers some of the extreme benefits over the existing techniques such as consumes less processing time, requires less space for real time dehaze purpose, etc. In this study, we combine both the object detection and image dehazing methods for real time applications such as-remote sensing, video surveillances, driverless automatic vehicles, etc. This paper presents an effective and efficient image dehazing method using transfer learning which helps to recognize objects in real time with more clarity and that can automatically detect objects with a high recognition rate and lesser probability of error. Our tests show that object detection becomes less accurate as the haze intensity increases; yet, under all haze circumstances (low, medium, or heavy), our jointly trained model AOD-net +YOLO v3 consistently outperforms non-joint and naïve YOLO v3 techniques.

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Particle swarm optimization enabled filtering for fabric images in automated fabric inspection system

Particle swarm optimization enabled filtering for fabric ima...

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international conference on Computing for Sustainable Global Development (INDIACom)

作者： S. Sahaya Tamil Selvi Dept. of Computer Science St. Joseph's College for Women Tirupur India

ISBN: (纸本)9781467394178

Random variations in intensity, poor contrast, variations in illumination are the common reasons for the images to be usually corrupted. These corruptions must be handled by the early stages of vision processing. Even though there are wide varieties of filtering techniques available for the purpose of removing noise in digital images, the problem still remains challenging in research. This paper shows how, Particle Swarm Optimization (PSO) enabled filtering method has been implemented and its performance is compared with the other filtering techniques such as Median, Gaussian filtering methods. The performances of these filtering methods on digital fabric images are assessed based on the metrics Mean squared error (MSE) and Peak signal to noise ratio (PSNR). The implementation results are then compared and evaluated.

关键词： Filtering Fabrics Digital images Inspection conferences Measurement image processing

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Button Cell characterization testing technology research based on computer vision

Button Cell characterization testing technology research bas...

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3rd international conference on Mechatronics and Intelligent Materials (MIM 2013)

作者： Sun LingYu Sun LiXin Peng Ping LI Hui Zhang XiaoJun Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China

ISBN: (纸本)9783037857106

The use of computer vision to detect and recognize target objects in the industrial field has high academic value. In this paper, computer vision is used to identify cell's label and detect the surface scratch. Using inter variance threshold method of image acquisition threshold processing, to obtain a better image binarization, subsequent using characterization information to establish a template, and the template using mathematical morphology operations. Object matching is achieved by matrix change of the template, in order to achieve the detection. The experiments show that this method has higher accuracy and better real-time, so online detection can be used for battery characterization.

关键词： computer vision morphology operations template match button cell

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Diagnosis and Degree of Evolution in a Keratoconus-Type Corneal Ectasia from image processing 3rd

Diagnosis and Degree of Evolution in a Keratoconus-Type Corn...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

作者： Otuna-Hernández, Diego Espinoza-Castro, Leslie Yánez-Contreras, Paula Villalba-Meneses, Fernando Cadena-Morejón, Carolina Guevara, César Cruz-Varela, Jonathan Tirado-Espín, Andrés Almeida-Galárraga, Diego School of Biological Sciences and Engineering Universidad Yachay Tech San Miguel de Urcuquí Urcuqui100119 Ecuador Department of Human Development Universidad Yachay Tech San Miguel de Urcuquí Urcuqui100119 Ecuador Centro de Mecatrónica y Sistemas Interactivos - MIST Universidad Indoamérica Quito Ecuador Instituto de Ciencias Matemáticas ICMAT-CSIC Madrid28049 Spain Universidad de Otavalo Av. De los Sarances y Av. Los Pendoneros Otavalo Ecuador Facultad de Ingeniería - Sistemas de Información Pontificia Universidad Católica del Ecuador Quito Ecuador

ISBN: (纸本)9783031322129

Keratoconus is a degenerative ocular pathology characterized by the thinning of the cornea, thus affecting many people around the world since this corneal ectasia causes a deformation of the corneal curvature that leads to astigmatism and, in more severe cases, to blindness. Treating physicians use non-invasive instruments, such is the case of Pentacam®, which takes images of the cornea, both the topography and the profile of the cornea, which allows them to diagnose, evaluate and treat this disease;this is known as morphological characterization of the cornea. On the other hand, Berlin/Ambrosio analysis helps in the identification and subsequent diagnosis since this analysis uses a mathematical model of linear progression, which identifies the different curves with the severity of the disease. Therefore, the aim of this study is to use the images provided by Pentacam®, Berlin/Ambrosio analysis, and vision parameters in a convolutional neural network to evaluate if this disparity could be used to help with the diagnosis of keratoconus and, consequently, generate a more precise and optimal method in the diagnosis of keratoconus. As a result, the processing and comparison of the images and the parameters allowed a 10% increase in the results of specificity and sensitivity of the mean and severe stages when combining tools (corneal profile and vision parameters) in the CNN reaching ranges of 90 to 95%. Furthermore, it is important to highlight that in the early-stage study, its improvement was around 20% in specificity, sensitivity, and accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Novel block matching algorithm using predictive motion vector for video object tracking based on color histogram

Novel block matching algorithm using predictive motion vecto...

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international conference on Electronic computer Technology

作者： N. G. Chitaliya A. I. Trivedi Electronics & Communication Engineering Department Sardar Vallabhbhai Patel Institute of Technology Vasad Gujarat India Electrical Engineering Department Maharaja Sayajirao University of Baroda Vadodara Gujarat India

Block matching motion estimation plays significant role for video processing and computer vision. Various methods based on mean shift and particle trackers are used to track the object on video sequence. Block matching algorithm is normally used for video compression technique. A novel, simple and fast block matching algorithm has been proposed using predictive motion vector for object tracking. Color histogram is used for matching criteria for the motion tracking. The proposed algorithm is considered with a flexible size of block as well as pixel displacement. Also early termination of the object as well as object out of boundary is considered. Experiments were conducted on different types of real world movie sequences.

关键词： Target tracking Histograms Prediction algorithms image color analysis Algorithm design and analysis Motion estimation

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Maritime Target Detection and Tracking

Maritime Target Detection and Tracking

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2018 3rd international conference on computer Science and Information Engineering(ICCSIE 2018)

作者： Liu Jian Gao Wen Changchun University of Science and Technology Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of Sciences

Method of object detection and tracking is one of hot topics in image processing and computer vision field,which is significant not only in military such as imaging guidance and military target tracking,but also for civil use such as security and monitoring and the intelligent man-machine *** marine targets,how to eliminate interference effectively and achieve accurate detection of moving targets is a key problem to be *** this paper,a method of image segmentation and line detection is proposed to detect the horizon,which can help to determine the area to be *** knowledge is used to detect the ship target in this *** identify the suspected ship,we detect it in multiple *** identifying the ship,the mean shift algorithm is applied to lock tracked *** experiment shows that the algorithm we proposed has good effect of detection and tracking for the ship target in the infrared image,and this algorithm has low computational complexity and small *** has been realized in the embedded hardware system and has high application value.

关键词： object tracking object detection line detection

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Dynamic Hand Gesture Recognition Using computer vision and Neural Networks

Dynamic Hand Gesture Recognition Using Computer Vision and N...

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international conference for Convergence of Technology (I2CT)

作者： M. I. N. P. Munasinghe Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka

Using hand gestures is one of the most natural ways of interacting with the computer and most importantly correct interpretation of moving hand gestures in real-time has many applications. In this paper, the author has designed and developed a system which can recognize gestures in front of a web camera real time using motion history images (MHI) and feedforward neural networks. Firstly, background from captured frames is removed using Gaussian mixture based background/foreground segmentation algorithm in order to capture moving areas in the frame and thereafter median filtering has applied to remove random noise from the frame. Then binary thresholding with Otsu's binarization has applied and it will identify optimal threshold value and these processed frames are merged and cumulative motion history image is generated using a developed algorithm based on the structural similarity measure. Structural similarity between the cumulated image and the initial frame also calculated and used in this algorithm. Finally feed forward neural network with stochastic gradient-based optimizer has used to classify the gestures.

关键词： Gesture recognition History Biological neural networks Feature extraction Real-time systems image segmentation Classification algorithms

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Weighted Mean Filter for removal of high density Salt and Pepper noise

Weighted Mean Filter for removal of high density Salt and Pe...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Neela Chithirala Natasha B. Rubini N. Anisha Radhakrishnan Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham University Coimbatore India Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu IN Amrita Vishwa Vidyapeetham University

ISBN: (纸本)9781467392075

The essential constraint on the input images to any computer vision technology is its quality. Acquiring noise free digital images is a challenge as it depends on several factors. Developing algorithms to remove noise is one way to improve the image quality. Salt and pepper noise degrades the image. The challenge here is to restore the lost information without distorting the edges. This paper introduces a new algorithm that reduces high density salt and pepper noise from images. Restoration is done by calculating the weighted mean of the nearby pixels. Weights are assigned unsymmetrically to pre-processed and unprocessed pixels. The quality was judged based on the PSNR value. The algorithm restores information for highly corrupted images. Salt and pepper noise are usually filtered with variants of the median filter. This paper provides an alternate way for noise reduction.

关键词： Filtering algorithms Communication systems Signal processing algorithms image edge detection Information filters Noise measurement

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