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检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1800 条记录，以下是111-120 订阅

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Color Grading in Tomato Maturity Estimator using image processing Technique

Color Grading in Tomato Maturity Estimator using Image Proce...

引用

2nd IEEE international conference on computer Science and Information Technology

作者： Syahrir, W. Md. Suryanti, A. Connsynn, C. Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia

ISBN: (纸本)9781424445189

This Tomato Maturity Estimator is developed to conduct tomato color grading using machine vision to replace human labor. Existing machine has not been widely applied in Malaysia since the cost is too expensive. The major problem in tomato color grading by human vision was due to the subjectivity of human vision and error prone by visual stress and tiredness. Therefore, this system is carried out to judge the tomato maturity based on their color and to estimate the expiry date of tomato by their color. Evolutionary methodology was implemented in this system design by using several image processing techniques including image acquisition, image enhancement and feature extraction. Fifty sample data of tomatoes were collected during image acquisition phase in the format of RGB color image. The quality of the collected images were being improved in the image enhancement phase;mainly converting to color space format (L*a*b*), filtering and threshold process. In the feature extraction phase, value of red-green is being extracted. The values are then being used as information for determining the percentage of tomato maturity and to estimate expiry date of tomato. According to the testing results, this system has met its objectives whereby 90.00% of the tomato tested has not rotten yet. This indicates that the judgment of tomato maturity and the estimation of tomato's expiry date were accurate in this project.

关键词： color grading tomato maturity image processing

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Early detection of Parkinson's disease using image processing and artificial neural network 7

Early detection of Parkinson's disease using image processin...

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Joint 7th international conference on Informatics, Electronics & vision (ICIEV) / 2nd international conference on Imaging, vision and Pattern Recognition (icIVPR)

作者： Rumman, Mosarrat Tasneem, Abu Nayeem Farzana, Sadia Pavel, Monirul Islam Alam, Md Ashraful BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka Bangladesh

ISBN: (纸本)9781538651636

Early detection of Parkinson's disease (PD) is very crucial for effective management and treatment of the disease. Dopaminergic images such as Single Photon Emission Tomography (SPECT) using I-123-Ioflupane can substantially detect PD at an early stage. However, till today, these images are mostly interpreted by humans which can manifest interobserver variability and inconsistency. To improve the imaging diagnosis of PD, we propose a model in this paper, for early detection of PD using image processing and artificial neural network (ANN). The model used 200 SPECT images, 100 of healthy normal and 100 of PD, obtained from Parkinson's Progression Marker's Initiative (PPMI) database and processed them to find the area of caudate and putamen which is the region of interest (ROI) for this study. The area values of ROI were then fed to the ANN which is hypothesized to mimic the pattern recognition of a human observer. The simple but fast ANN built, could classify subjects with and without PD with an accuracy of 94%, sensitivity of 100% and specificity of 88% Hence it can be inferred that the proposed system has the potential to be an effective way to aid the clinicians in the accurate diagnosis of PD.

关键词： Parkinson's disease ROI segmentation artificial neural network for Parkinson's disease detection computer aided early detection

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image encryption using chaotic 3-D Arnold’s cat map and logistic map 2nd

Image encryption using chaotic 3-D Arnold’s cat map and log...

引用

2nd international conference on computer vision and image processing, cvip 2017

作者： Musanna, Farhan Rani, Asha Kumar, Sanjeev Department of Mathematics IIT Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811078972

image encryption is different from that of traditional texts or binary data because of some inherent properties of images such as large data capacity, i.e., enormous size and high redundancy (statistical and psycho-visual), making them difficult to handle by traditional methods. In recent years, chaos theory has been explored to find efficient ways to develop secure image cryptosystems. Due to the desirable properties of mixing and sensitivity to initial conditions and parameters (butterfly effect), chaotic systems have found great deal in the domain of image encryption. In this paper, the 2-D chaotic cat map is generalized to its 3-D map counterpart for designing a real-time secure and reliable symmetric encryption design. This new scheme deploys the 3-D Arnold’s cat map to shuffle the positions of image pixels and uses the other chaotic logistic map to perplex the relationship between the plain-image and the cipher-image, thereby significantly enhancing the robustness to differential and statistical attacks. Experimental tests are carried out with comprehensive analysis, demonstrating the high security of the scheme. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Chaotic systems

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image Enhancement for Iris Analysis Using Deep Learning Algorithms - A Brief Survey 2

Image Enhancement for Iris Analysis Using Deep Learning Algo...

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2nd international conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Poovayar Priya, M. Ezhilarasan, M. Pondicherry Engineering College Dept. of Computer Science and Engineering Puducherry India Pondicherry Engineering College Dept. of Information Technology Puducherry India

ISBN: (纸本)9798350328202

With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processing and computer vision. There have been many algorithms for improving images established in the past. More extensive work on image-enhancing techniques has been done utilizing deep learning models in recent years, as these algorithms have achieved their peak in vision applications. In this study, we present a thorough analysis of machine learning, deep learning, and literature. The literature on image enhancement is then summarised, and future approaches for iris analysis are discussed. © 2024 IEEE.

关键词： image enhancement

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A bias correction variational level set image segmentation model combining structure extraction 2

A bias correction variational level set image segmentation m...

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2nd international conference on image, vision and Computing, ICIVC 2017

作者： Xili, Wang Li, Hu Xiyuan, Wang School of Computer Science Shaanxi Normal University Xi'an China School of Physics and Electronic Electrical Engineering Ningxia University Yinchuan China

ISBN: (纸本)9781509062379

The authors propose a variational level set image segmentation method for intensity inhomogeneous texture image. The method first extracts the main image structure by a relative total variation image decomposition method, which can better decompose the image into structural and textural parts. Then only uses the structural part as the input image for the variational level set segmentation. The intensity bias are estimated simultaneously in the level set curve evolution procedure. And the intensity inhomogeneity is solved by bias correction. The experiments indicate that the proposed method can remove texture, retain and smooth the structure information, estimate and correct intensity bias effectively. Thus, it improves the segmentation results compared with the level set image segmentation methods that take no account of intensity inhomogeneity and texture in images. © 2017 IEEE.

关键词： image segmentation

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Age Invariant Face Recognition using Frangi2D Binary Pattern 19

Age Invariant Face Recognition using Frangi2D Binary Pattern

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2nd international conference on image and Graphics processing (ICIGP) / 5th international conference on Virtual Reality

作者： Afroze, Sabah Beham, M. Parisa Tamilselvi, R. Maraikkayar, S. M. Seeni Mohamed Aliar Rajakumar, K. Sethu Inst Technol Dept Elect & Commun Engn Pulloor 626115 Tamil Nadu India

ISBN: (纸本)9781450360920

The field of computer vision is devoted to discovering algorithms, data representations and computer architectures that embody the principles underlying visual capabilities. computer vision is an interdisciplinary field that deals with how computers can be made for gaining high level understanding from digital images or videos. While very promising result has been shown on face recognition related problems, age invariant face recognition still relics a challenge. Facial appearance of a human varies over time, which results in substantial intra-class variations. In order to address this problem, we propose Frangi2D method for normalization, Linear Binary pattern (LBP) for feature extraction and Sparse Representation Classifier (SRC). Extensive results on a well-known public domain face aging dataset: MORPH. The experimental results show the superiority of our proposed method in age invariant face recognition.

关键词： Age Invariant Face recognition Frangi 2D sparse representation classifier (SRC) Local binary pattern (LBP)

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Leaf Disease Detection and Grading using computer vision Technology & Fuzzy Logic 2

Leaf Disease Detection and Grading using Computer Vision Tec...

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2nd international conference on Signal processing and Integrated Networks (SPIN)

作者： Rastogi, Aakanksha Arora, Ritika Sharma, Shanu Sopra Noida Uttar Pradesh India Young & Shando Noida Uttar Pradesh India Amity Univ ASET Noida Uttar Pradesh India

ISBN: (纸本)9781479959914

In Agriculture, leaf diseases have grown to be a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automated recognition of diseases on leaves plays a crucial role in agriculture sector. This paper imparts a simple and computationally proficient method used for leaf disease identification and grading using digital image processing and machine vision technology. The proposed system is divided into two phases, in first phase the plant is recognized on the basis of the features of leaf, it includes pre-processing of leaf images, and feature extraction followed by Artificial Neural Network based training and classification for recognition of leaf. In second phase the disease present in the leaf is classified, this process includes K-Means based segmentation of defected area, feature extraction of defected portion and the ANN based classification of disease. Then the disease grading is done on the basis of the amount of disease present in the leaf.

关键词： computer vision color image segmentation disease grading artificial Neural Network (ANN) fuzzy logic

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vision-based human motion recognition: A survey

Vision-based human motion recognition: A survey

引用

2nd international conference on Intelligent Networks and Intelligent Systems, ICINIS 2009

作者： Wei, Wei An, Yunxiao Hebei University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9780769538525

With the development of computer vision and image processing technology, human motion recognition, because of its wide range of applications, now has been attracting extensive attention in the field of computer vision. vision-based recognition of people's motion not only includes the knowledge of image recognition and computer vision, but also involves the theory of recognition and artificial intelligence, so it is an extremely challenging interdisciplinary project. The research work in recent years have been analyzed and summarized, respectively from the classification of human motion, human motion feature extraction and recognition algorithm to introduce a number of human motion recognition. © 2009 IEEE.

关键词： Feature extraction

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computer vision system for robotic complex based on Arduino and Raspberry Pi 2

Computer vision system for robotic complex based on Arduino ...

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2nd IEEE international conference on Control in Technical Systems (CTS)

作者： Petrova, K. A. St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781538607770

This paper is devoted to traffic sign recognition problem in real time. The recognition process consists of three steps. The first step is a search of image parts which probably contain a traffic sign. The second step is about parts extraction and simple classification by shape. The last step consists of classification of extracted parts with previously learned multilayered neural net. The results of experimental setup based on real data show feasibility of the proposed methods.

关键词： traffic sign recognition image processing neural net classification

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No reference image quality assessments based on edge-blur measure and its applications in printed sheet blurs classification 2

No reference image quality assessments based on edge-blur me...

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2nd international conference on image, vision and Computing, ICIVC 2017

作者： Rixing, Huang College of Computer Science and Engineering University of Electronic Science and Technology of Chinaname of Organization Chengdu China

ISBN: (纸本)9781509062379

No reference image quality assessments based on edge are important research methods, but these algorithms do not consider blur direction. This does not meet the needs of our work, printed sheet image blur classification. This paper includes two main parts. First, analyzing rules of image blurs and design a novel no reference image quality analysis based on edge, edge-blur measure (EBM). It not only takes overall-blur of image assessment, but also takes image blur assessment in some directions. Second, use these methods to classify the printed sheet blurs (sharp, defocused and shake). Extract the image blur features from EBM. Then by regression analysis, all blur features are discretized into three values. Then the discrete blur features will be input into naive Bayes classifier for blur classification. Finally, our experimental results demonstrate the effect of EBM and classification algorithm. © 2017 IEEE.

关键词： Regression analysis

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