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

共 1804 条记录，以下是971-980 订阅

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Edge-aware spatial filtering-based motion magnification 1

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

作者： Verma, Manisha Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

In this paper, we present a novel approach toward motion magnification using edge-aware filtering. Edge-aware filters have been used for applications such as image denoising, enhancement, and tone mapping. In this work, we have used fast local Laplacian filter followed by Eulerian motion magnification for enhancement of subtle changes in the video. The proposed method aims to reduce the noise and artifacts in the output video apart from motion magnification. Experiments are conducted on sample videos having minute changes which are imperceptible to see with the human eyes. A no-reference video quality assessment has been employed using a video quality score in order to quantify the motion magnification results. © Springer Nature Singapore Pte Ltd. 2018.

关键词： image denoising

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Multi-scale directional mask pattern for medical image classification and retrieval 2nd

Multi-scale directional mask pattern for medical image class...

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

作者： Dudhane, Akshay A. Talbar, Sanjay N. Department of Electronics & Telecommunication Engineering SGGSIE&T Nanded India

ISBN: (纸本)9789811078941

This paper presents a classification scheme for interstitial lung disease (ILD) pattern using patch-based approach and artificial neural network (ANN) classifier. A new feature descriptor, Multi-Scale Directional Mask Pattern (MSDMP), is proposed for feature extraction. Proposed MSDMP extracts microstructure information from a (31 × 31) size patches of the region of interest (ROI) which were marked by the radiologists. A two-layer feed-forward neural network is used for classification of ILD patterns. Also, proposed MSDMP feature descriptor has been tested on medical image retrieval system to check its robustness. Two benchmark medical datasets are used to evaluate the proposed descriptor. Performance analysis shows that the proposed feature descriptor outperforms the other existing state-of-the-art methods in terms of average recognition rate (ARR) and F-score. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multilayer neural networks

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Adversarial network for edge detection 7

Adversarial network for edge detection

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Joint 7th international conference on Informatics, Electronics and vision and 2nd international conference on Imaging, vision and Pattern Recognition, ICIEV-IVPR 2018

作者： Zeng, Zhiliang Yu, Ying Kin Hong Wong, Kin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781538651612

Edge detection is a fundamental problem in computer vision and has been explored for many decades. Due to the rapid development of machine learning techniques and their applications to image processing, there is a proliferation of neural network-based approaches to solve the edge detection problem. These methods, such as richer convolutional features for edge detection(RCF), have good performance and even outperform human beings. Most of the existing neural network-based systems use the convolutional network or its variant. They usually produce thick edges and the application of non-maximum-suppression to suppress the edge is necessary. In this paper, we explore another type of neural network called the conditional generative adversarial network (cGAN) to address the edge detection problem. cGAN is an innovative framework to do the image synthesis task. It can generate an image close to the real one. After training, our network can produce an edge map that contains more detailed information and thinner edges compared to the state-of-the-art methods that require the well-known non-maximum-suppression for post-processing. The proposed approach is able to produce a high quality edge map directly without further processing. Our solution is computation efficient. It can achieve a speed of 59 and 26 frames per second (fps) for an image resolution of [256×256×3] and [512×512×3], respectively. © 2018 IEEE.

关键词： Edge detection

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image Segmentation Using Multilevel Thresholding and Genetic Algorithm: An Approach 2

Image Segmentation Using Multilevel Thresholding and Genetic...

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2nd international conference on Data Science and Business Analytics (ICDSBA)

作者： de Oliveira, Pedro Ventura Yamanaka, Keiji Fed Univ Uberlandia UFU Nat & Artificial Inteligence Lab Fac Engn Eletr Uberlandia MG Brazil Fed Univ Uberlandia UFU Fac Engn Eletr Uberlandia MG Brazil

ISBN: (纸本)9781538684313

The segmentation aims the partitioning of a digital image into segments/objects in order to simplify its analyze. Its comprehensive application makes it an important section of computer vision studies where thresholding is one of the most important techniques. This paper proposes the application of a genetic algorithm associated with a thresholding method in a way that it makes more practical the location of optimal thresholds in view of the fact that the expansion in multiple thresholds of already consecrated methods to perform this processing might be too memory consuming and time exhaustive to the system. A comparison between values reached by the proposed method and Otsu's method are presented for one and two levels showing good results, in addition to the application and results in higher levels of the proposed algorithm which are visual satisfactory to distinguish the original objects by human vision with low range variations of the value.

关键词： genetic algorithm image segmentation thresholding

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In-Line Burr Inspection Through Backlight vision 2nd

In-Line Burr Inspection Through Backlight Vision

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Fitti, Matteo Castellini, Paolo Paone, Nicola Zannini, Marco Zitti, Saverio Gambini, Marco Chiariotti, Paolo Università Politecnica delle Marche via Brecce Bianche Ancona60131 Italy Zannini SpA Via Che Guevara 63 Castelfidardo60022 Italy Zannini Poland Wojkowicka 60 Czeladź41-250 Poland

ISBN: (纸本)9783030307530

This paper presents a vision-based quality control system for detecting burrs (miniature metal filaments) in transverse holes of high precision turned hollow cylinders. The system performs 100% in-line quality control at the turning station. It exploits a camera with telecentric optics framing the sample from the outside in back-light condition. A specifically developed cylindrical illuminator provides radial diffuse back-light illumination over 360° and can be inserted within the part to be inspected. The possibility to detect burrs placed on both the outer and the inner surface of target holes is achieved by exploiting a customized rotating device integrated to a commercial gripping device. Overall, the system mimics the manual inspection normally performed by operators. Burrs are detected as modifications of the circular shape of each hole, through algorithms that identify the holes on grayscale images, perform circle identification by geometric matching and identify burrs through analysis of local deviations of the edge from circularity. Results acquired in a real production line over a batch of 2000 parts showed no false-positive or false-negative diagnosis. © 2019, Springer Nature Switzerland AG.

关键词： Inspection

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Facial Expression Detection using Neural Network for Customer Based Service 2

Facial Expression Detection using Neural Network for Custome...

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2nd international conference on computer, Communication, and Signal processing (ICCCSP) - Special Focus on Technology and Innovation for Smart Environment

作者： Ramani, Sandeep Kumar Sri Venkateswara Coll Engn Dept Elect & Commnuicat Sriperumbudur India

ISBN: (纸本)9781538611418

Facial expression recognition is a hot research direction in the field of pattern recognition and computer vision. In this paper, the images of customer's face are used to detect the facial expression in them to enhance the customer based services. The convolution neural network using the VGG-16 architecture is used as the deep learning model which extracts essential features in an image and enables us to recognize the expression of the customer. A customer service system architecture is designed using two neural networks, a convolution neural networks using the VGG-16 architecture our baseline and the Google cloud platform vision application program interface to explore the needs of different models based on the client usage.

关键词： Facial expression detection Visual geometry group neural network Google cloud platform vision API Customer service system architecture

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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...

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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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Wild animal detection using deep convolutional neural network 2nd

Wild animal detection using deep convolutional neural networ...

引用

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

作者： Verma, Gyanendra K. Gupta, Pragya Department of Computer Engineering National Institute of Technology Kurukshetra Haryana India

ISBN: (纸本)9789811078972

Wildlife monitoring and analysis are an active research field since last many decades. In this paper, we focus on wildlife monitoring and analysis through animal detection from natural scenes acquired by camera-trap networks. The image sequences obtained from camera-trap consist of highly cluttered images that hinder the detection of animal resulting in low-detection rates and high false discovery rates. To handle this problem, we have used a camera-trap database that has candidate animal proposals using multilevel graph cut in the spatiotemporal domain. These proposals are used to create a verification phase that identifies whether a given patch is animal or background. We have designed animal detection model using self-learned Deep Convolutional Neural Network (DCNN) features. This efficient feature set is then used for classification using state-of-the-art machine learning algorithms, namely support vector machine, k-nearest neighbor, and ensemble tree. Our intensive results show that our detection model using DCNN features provides accuracy of 91.4% on standard camera-trap dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Animals

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A novel pattern matching approach on the use of multi-variant local descriptor 1

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

作者： Dev, Deep Suman Kisku, Dakshina Ranjan Department of Information Technology Neotia Institute of Technology Management and Science Jhinga Diamond Harbour Road Amira South 24 Parganas KolkattaWest Bengal743368 India Department of Computer Science and Engineering National Institute of Technology Durgapur DurgapurWest Bengal713209 India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

The objective of pattern matching problem is to find the most similar image pattern in a scene image by matching to an instance of the given pattern. For pattern matching, most distinctive features are computed from a pattern that is to be searched in the scene image. Scene image is logically divided into sliding windows of pattern size, and all the sliding windows are to be checked with the pattern for matching. Due to constant matching between the pattern and the sliding window, the matching process should be very efficient in terms of space, time and impacts due to orientation, illumination and occlusion must be minimized to obtain better matching accuracy. This paper presents a novel local feature descriptor called Multi-variant Local Binary Pattern (MVLBP) for pattern matching process while LBP is considered as base-line technique. The efficacy of the proposed pattern matching algorithm is tested on two databases and proved to be a computationally efficient one. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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An Improved Visual Robot Moving Target Tracking Method

An Improved Visual Robot Moving Target Tracking Method

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IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Hao Liu Hong Zhou Jincheng College of Sichuan University Chengdu China

Moving target tracking has always been an important research direction in machine vision. Based on the careful study of the compression tracking algorithm, this paper proposes an improved algorithm for collecting a large number of samples in the tracking phase, using the positive and negative classifiers for judgment and large computational complexity: In the previous frame tracking result of the video, the window with the larger cross-correlation value of the target frame is used as the optimal tracking position in the corresponding region of the current frame, thereby reducing the calculation amount. The results of computer simulation experiments show that the algorithm improves the operation speed without affecting the tracking accuracy.

关键词： Target tracking image coding Feature extraction Classification algorithms Bayes methods Signal processing algorithms Sparse matrices

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