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GUESS: Genetic uses in video encryption with secret sharing 1

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

作者： Sharma, Shikhar Kumar, Krishan Department of Computer Science & Engineering NIT SrinagarUttarakhand India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

Nowadays, video security systems are essential for supervision everywhere, for example video conference, WhatsApp, ATM, airport, railway station, and other crowded places. In multi-view video systems, various cameras are producing a huge amount of video content which makes it difficult for fast browsing and securing the information. Due to advancement in networking, digital cameras, and media, interactive sites, the importance of privacy and security is rapidly increasing. Hence, nowadays the security of digital videos become an emerging research area in the multimedia domain;especially when the communication happens over the Internet. Cryptography is an essential practice to protect the information in this digital world. Standard encryption techniques like AES/DES are not optimal and efficient in case of videos. Therefore, a technique is immediately required, which can provide the security to video content. In this paper, we address the video security-related issues and their solutions. An optimized version of the genetic algorithm is employed to solve the aforementioned issues through modeling the simplified version of genetic processes. It is used to generate a frame sequence such that the correlation between any two frames is minimized. The frame sequence determines the randomization in order of frames of a video. The proposed method is not only fast but also more accurate to enhance the efficiency of an encryption process. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Automatic Reading System for Analog Instruments Based on computer vision and Inspection Robot for Power Plant 10

Automatic Reading System for Analog Instruments Based on Com...

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10th international conference on Modelling, Identification and Control (ICMIC)

作者： Wang, Junzhe Huang, Jian Cheng, Rong Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538654163

In this paper, an automatic reading system for analog instruments has been designed for monitoring in power plants. In a general automatic reading system, there are many limitations to the reading correctness and system robustness such as the high cost for the reason that there is only one instrument could be monitored by a single system, the strict restrict of the camera angle, and being not suitable for instruments with dense scales and so on. We present some solutions to overcome these limitations. Firstly, we combine a mobile inspection robot with an image capture device and the image processing method to cut the cost of the monitoring of analog instruments in power plants. Then, we use Hough transform and perspective transform to correct the geometric distortion of images caused by camera angle. Eventually, we get the result of dense scaled instruments based on polar transform. Experiments show that our system performs quite well, and the reading error is less than the results which obtained from the general automatic reading system.

关键词： Analog instruments Routing inspection robot computer vision Automatic reading

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Effective denoising with non-local means filter for reliable unwrapping of digital holographic interferometric fringes 1

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

作者： Aparna, P.L. Waghmare, Rahul G. Mishra, Deepak Sai Subrahmanyam Gorthi, R.K. National Institute of Technology Surathkal575025 India Indian Institute of Space Science and Technology Trivandrum695547 India Indian Institute of Technology Tirupati517506 India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

Estimation of phase from the complex interference field has become an emerging area of research for last few decades. The phase values obtained by using arctan function are limited to the interval (-π, π]. Such phase map is known as wrapped phase. The unwrapping process, which produces continuous phase map from the wrapped phase, becomes tedious in presence of noise. In this paper, we propose a preprocessing technique that removes the noise from the interference field, thereby improving the performance of naive unwrapping algorithms. For de-noising of the complex field, real part and imaginary parts of the field are processed separately. Real-valued images (real and imaginary parts) are processed using non-local means filter with non-Euclidian distance measure. The de-noised real and imaginary parts are then combined to form a clean interference field. MATLAB’s unwrap function is used as unwrapping algorithm to get the continuous phase from the cleaned interference field. Comparison with the Frost’s filter validates the applicability of proposed approach for processing the noisy interference field. © 2018, Springer Nature Singapore Pte Ltd.

关键词： image denoising

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Road Surface Classification Using Texture Synthesis Based on Gray-Level Co-occurrence Matrix

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1st international conference on computer vision and image processing (cvip)

作者： Mukherjee, Somnath Pandey, Saurabh Kritikal Solut Pvt Ltd Comp Vis & Image Proc Unit Bangalore Karnataka India

ISBN: (纸本)9789811021046;9789811021039

Advance Driving Assistance System (ADAS) has been a growing area of interest in the research community for automotive domain where scene understanding and modeling is one of the principally focus area of activities. Texture synthesis using gray-level co-occurrence matrix (GLCM) of any rigid body is not an exceptional task in image processing area. The additional integration of this method is for texture characterization and use it for the road surface classifications which is the primary focus of this paper. We have also introduced that GLCM based road surface analysis in a line scan manner that can be used as a module for ADAS application.

关键词： Gray-level co-occurrence matrix Road texture The Advance Driving Assistance System (ADAS) etc

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image QUALITY ASSESSMENT BASED LABEL SMOOTHING IN DEEP NEURAL NETWORK LEARNING

IMAGE QUALITY ASSESSMENT BASED LABEL SMOOTHING IN DEEP NEURA...

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IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Chen, Zhuo Lin, Weisi Wang, Shiqi Xu, Long Li, Leida Nanyang Technol Univ Interdisciplinary Grad Sch ROSE Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Chinese Acad Sci NAOC Beijing Peoples R China China Univ Min & Technol Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781538646588

For many computer vision problems, deep neural networks are trained and validated based on the assumption that the input images are pristine (i.e., artifact-free). However, digital images are subject to a wide range of distortions in real application scenarios, while the practical issues regarding image quality in high level visual information understanding have been largely ignored. In this paper, in view of the fact that most widely deployed deep learning models are susceptible to various image distortions, distorted images are involved for data augmentation in the deep neural network training process to learn a reliable model for practical applications. In particular, an image quality assessment based label smoothing method, which aims at regularizing the label distribution of training images, is further proposed to tune the objective functions in learning the neural network. Experimental results show that the proposed method is effective in dealing with both low and high quality images in the typical image classification task.

关键词： Deep learning image quality assesment

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Integrated feature exploration for handwritten devanagari numeral recognition 1

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

作者： Arya, Shraddha Chhabra, Indu Lehal, G.S. Department of Computer Science Sri Guru Gobind Singh College Chandigarh India Department of Computer Science and Applications Panjab University Chandigarh India Computer Science Department Punjabi University PatialaPunjab India

ISBN: (数字)9789811078958

ISBN: (纸本)9789811078941

In this paper, the statistical feature extraction techniques are explored, incrementally combined using different methods and analyzed for the recognition of isolated offline handwritten Devanagari numerals. The techniques selected are zoning, directional distance distribution, Zernike moments, discrete cosine transform, and Gabor filter that encapsulate the mutually exclusive statistical features like average pixel densities, directional distribution, orthogonal invariant moments, elementary frequency components, and space frequency component, respectively. The standard benchmark handwritten Devanagari numeral database provided by ISI, Kolkata, is used for the experimentation and 1-nearest neighbor and support vector machine for classification. The accuracy achieved with individual feature extraction techniques ranges from 86.87% to 98.96%. Further, features are integrated with methods like feature concatenation, majority voting, and a new proposed methodology by us named winners pooling. The maximum recognition obtained through feature integration is 99.14%. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Gabor filters

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A direct visual servoing-based framework for the 2016 IROS Autonomous Drone Racing Challenge

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JOURNAL OF FIELD ROBOTICS 2018年第1期35卷 146-166页

作者： Jung, Sunggoo Cho, Sungwook Lee, Dasol Lee, Hanseob Shim, David Hyunchul Korea Adv Inst Sci & Technol Unmanned Syst Res Grp Dept Aerosp Engn Daejeon 34141 South Korea

This paper presents a framework for navigating in obstacle-dense environments as posed in the 2016 international conference on Intelligent Robots and Systems (IROS) Autonomous Drone Racing Challenge. Our framework is based on direct visual servoing and leg-by-leg planning to navigate in a complex environment filled with many similar frame-shaped obstacles to fly through. Our indoor navigation method relies on the velocity measurement by an optical flow sensor since the position measurements from GPS or external cameras are not available. For precision navigation through a sequence of obstacles, a center point-matching method is used with the depth information from the onboard stereo camera. The guidance points is directly generated in three-dimensional space using the two-dimensional image data to avoid accumulating the error from the sensor drift. The proposed framework is implemented on a quadrotor-based aerial vehicle, which carries an onboard vision-processing computer for self-contained operation. Using the proposed method, our drone was able to finished in first place in the world-premier IROS Autonomous Drone Racing Challenge.

关键词： Aerial robotics autonomous drone racing collision avoidance stereo vision visual servoing

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LIP: Learning Instance Propagation for Video Object Segmentation

LIP: Learning Instance Propagation for Video Object Segmenta...

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international conference on computer vision Workshops (ICCV Workshops)

作者： Ye Lyu George Vosselman Gui-Song Xia Michael Ying Yang University of Twente Wuhan University

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we propose a single end-to-end trainable deep neural network, convolutional gated recurrent Mask-RCNN, for tackling the semi-supervised VOS task. We take advantage of both the instance segmentation network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS task. The instance segmentation network predicts masks for instances, while the visual memory module learns to selectively propagate information for multiple instances simultaneously, which handles the appearance change, the variation of scale and pose and the occlusions between objects. After offline and online training under purely instance segmentation losses, our approach is able to achieve satisfactory results without any post-processing or synthetic video data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017 dataset have demonstrated the effectiveness of our method for video object segmentation task.

关键词： Task analysis Object segmentation Visualization Logic gates image segmentation Head Training

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Multi-exposure image Fusion Using Propagated image Filtering

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1st international conference on computer vision and image processing (cvip)

作者： Patel, Diptiben Sonane, Bhoomika Raman, Shanmuganathan Indian Inst Technol Elect Engn Gandhinagar India

ISBN: (纸本)9789811021046;9789811021039

image fusion is the process of combining multiple images of a same scene to single high-quality image which has more information than any of the input images. In this paper, we propose a new fusion approach in a spatial domain using propagated image filter. The proposed approach calculates the weight map of every input image using the propagated image filter and gradient domain postprocessing. Propagated image filter exploits cumulative weight construction approach for filtering operation. We show that the proposed approach is able to achieve state-of-the-art results for the problem of multi-exposure fusion for various types of indoor and outdoor natural static scenes with varying amounts of dynamic range.

关键词： Computational photography Multi-exposure fusion HDR imaging Propagated image filter

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D-PNR: Deep license plate number recognition 1

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

作者： Kumar, Krishan Sinha, Shambhavi Manupriya, Piyushi Department of Computer Science and Engineering NIT Uttarakhand India

ISBN: (数字)9789811078989

ISBN: (纸本)9789811078972

Deep learning empowered the license plate recognition (LPR) system which can efficiently extract the information from a vehicle’s license plate. LPR has various applications in this digital world. Due to technology growing at a rocketing pace, there is a rapid growth in the number of vehicles on road. Even self-driving cars are soon to be a common sight. This is causing a fast and frequent growth in the accidents occurrence and other mishaps. Thus, there is a need for monitoring traffic and security surveillance. LPR technique is not new. However, traditionally the extracting features from an image/license plate were done hand-tunned which make the recognition process time-consuming and error-prone. In this paper, we proposed a novel machine learning approach to recognizing the license plate number. We used one of the most successful deep learning method, convolutional neural network (CNN) for extracting the visual features automatically. Suitable localization and segmentation techniques are employed before CNN model to enhance the accuracy of the proposed model. In addition to this, the D-PNR model also takes care of proper identification from images those are hazy and is not suitable-inclined or noisy images. Qualitative and quantitative evaluation is done in order to compare the performances of the proposed D-PNR model and state-of-the-art models. A computing analysis of our approach also shows that it meets the requirement of the real-time applications, i.e., monitoring traffic and security surveillance. © Springer Nature Singapore Pte Ltd. 2018.

关键词： License plates (automobile)

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