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

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Rough Terrain Autonomous Vehicle Control Using Google Cloud vision API

Rough Terrain Autonomous Vehicle Control Using Google Cloud ...

引用

international conference on Power and Embedded Drive Control (ICPEDC)

作者： M Sugadev Yogesh Prithvi Kumar Sanghamreddy Sai Krishna Samineni ECE Department Sathyabama Institute of Science and Technology Chennai

ISBN: (数字)9781728124148

ISBN: (纸本)9781728124155

computer vision (CV) represents a technology using which information contained in a visual scenery is obtained by capturing and digital processing of the images. computer vision is becoming more popular and widely used in automated vehicles, robotics to emulate human vision capability and industrial automation. Google cloud vision API is a remote real time image processing platform to process, classify and acquire required metadata from image and videos. In this work, design and implementation of a rough terrain autonomous vehicle using Rocker Bogie mechanism and Google cloud vision API for obstacle avoidance is presented. Rocker Bogie mechanism is employed for maneuvering the robot on rough terrains.

关键词： computer vision Wheels Google Computational modeling Roads Videos

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Improved Wavelet Denoising by Penalty Function and Regularization Parameter 2

Improved Wavelet Denoising by Penalty Function and Regulariz...

引用

2nd international conference on computer Information Science and Application Technology, CISAT 2019

作者： Wei, Wanhui Zhou, Wei Wu, Yongjun School of Traffic and Transportation Engineering Central South University Changsha Hunan410083 China Key Laboratory of Traffic Safety on Track Ministry of Education Changsha Hunan410083 China Joint International Research Laboratory of Key Technology for Rail Traffic Safety Changsha Hunan410083 China National and Local Joint Engineering Research Center of Safety Technology for Rail Vehicle Changsha Hunan410083 China

Wavelet-based convex optimization in sparse signal processing has attracted extensive research interest in recent years. Research demonstrates that the penalty function promotes the sparsity and improves the accuracy better than L1 norm method in dealing with convex and sparse signal reconstruction issues. This paper presents an improved denoising model which utilizes a penalty function to induce stronger sparsity in wavelet domain. In this paper, we have redefined the relationship model between signal noise levels and the regularization parameter to enhance sparsity caused by penalty functions in a wavelet domain. And three penalty functions are employed for wavelet coefficients to induce strong sparse wavelet coefficients under the wavelet domain. We then apply the improved wavelet denoising algorithm for image denoising to get the values of the PSNR are analysed under different noise level conditions. Experimental results clearly show that the improved wavelet denoising model is strength in terms of both quantitative measure and structural similarity quality, and outperforms many widely used denoising algorithms. © Published under licence by IOP Publishing Ltd.

关键词： image denoising

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Skin detection using hybrid colour space of RGB-H-CMYK 2nd

Skin detection using hybrid colour space of RGB-H-CMYK

引用

2nd international conference on Advanced Computing and Intelligent Engineering, ICACIE 2017

作者： Kumar, Ashish Shanmugavadivu, P. Department of Computer Science and Applications Gandhigram Rural Institute – Deemed University Gandhigram India

ISBN: (纸本)9789811317071

Skin detection is an essential step in human face detection and/or recognition, using digital image processing techniques. This paper presents a new human skin detection technique, termed as RGB-H-CMYK that uses triple colour spaces of an image, namely RGB (Red, Blue and Green), H (Hue of HSV) and CMYK (Cyan, Magenta, Yellow and Black). In this proposed method, threshold-based rules are applied on RGB, H and CMYK for skin classification. The input image in these three hybrid colour schemes is explored in different combination such as RC (RGB and CMYK), RH (RGB and H) and RHC (RGB and H and CMYK). The RHC_Vote qualifies the current pixel as skin pixel when at least two rules vote for it. The computational merit of this hybrid colour scheme-based skin detection is validated on the real-time dataset and ECU skin database. The average Recall and Accuracy of this method is recorded as 85% and 89%, respectively. This approach is confirmed to have an edge over its competitive methods, as it promises object localization, based on neighbourhood intensities without using the computationally complex approaches such as facial texture and geometric properties. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Face recognition

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Gabor filter meanPCA feature extraction for gender recognition 1

引用

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

作者： Gupta, Sandeep K. Nain, Neeta Department of Computer Science & Engineering Malaviya National Institute of Technology JaipurRajasthan302017 India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

This paper proposed Novel feature extraction techniques as Gabor-meanPCA for automatic gender recognition using faces of person. Feature extraction is the main stage on which accuracy of gender recognition system depended. Male and female have different edge and texture pattern on faces. Gabor filter is able to extract edges and texture pattern of faces but has a problem of huge dimension and high redundancy. In this paper, Gabor filter is used for extraction of edge pattern of faces using different angles. Problem of huge dimension and high redundancy is reduced by proposed two-level feature reduction technique. The proposed technique also provides better accuracy as well as compact feature vector for reducing classification time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature extraction

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Robust ellipse detection via duality principle with a false determination control 1

引用

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

作者： Dong, Huixu Chen, I-Ming Prasad, Dilip K. Robotics Research Centre Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

In this paper, we propose a novel ellipse detection approach that eliminates false detection-based parameter space decomposition, principal of symmetric tangents, and a novel geometric constraint utilizing properties of tangents of ellipses. The principle of symmetric tangents provides better computational efficiency through confirmation of the ellipse center in the decomposed parameter space. The geometric constraint is used for alleviating the false detection probability. The experimental results confirm that the approach detects ellipse with an excellent accuracy at a high speed. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Computational efficiency

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Edge-aware spatial filtering-based motion magnification 1

引用

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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image contrast enhancement using hybrid elitist ant system, Elitism-based immigrants genetic algorithm and simulated annealing 1

引用

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

作者： Kumar, Rajeev Gupta, Anand Gupta, Apoorv Bansal, Aman Department of Computer Engineering NSIT University of Delhi New Delhi India Department of Information Technology NSIT University of Delhi New Delhi India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

Contrast enhancement is a technique which is used to expand the range of intensities within the image to make its features more distinct and easily perceptible to the human eye. It has found many applications ranging from medical to satellite imagery where the primary aim is to find hidden or minute details within an image. Through literary research, the authors have realised that the existing approaches lag behind in enhancing the contrast of an image. Hence in the present paper, an improved contrast enhancement technique is proposed which is based on the hybrid combination of nature-based metaheuristics: Elitist Ant System (EAS), Elitism-based Genetic Algorithm (EIGA) and Simulated Annealing (SA). EAS and EIGA work together to search globally for the optimum solution which is then refined by SA locally. Through experiment, it is observed that the proposed algorithm is efficiently improving the contrast of an image when compared with existing algorithms. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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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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Adversarial network for edge detection 7

Adversarial network for edge detection

引用

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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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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