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Online International Conference on Green Engineering and Technologies (IC-GET)

作者： Muktai N. Joshi Dhanashri H. Gawali Dept. of E&TC NBN Sinhgad School of Engineering Pune India

ISBN: (纸本)9781509045570

Traditional computers data processing is limited by computer data input, output, storage, display. Further computing needs repeated binary-decimal conversions. With the expansion of data intensive computing needs of distributed computing, decimal computing of mass data is widely applied in banking, financial, signal processing, bio-medical, astronomy, geography, data acquisition and image compression and other fields. Independent decimal floating point unit is becoming important in these areas. A floating point unit is a part of a computer system specially designed to carry out operations on floating point numbers. Floating point unit have been implemented as a coprocessor rather than as an integrated unit in various systems. Today's floating point arithmetic operations are very important in the design of Digital Signal processing and application-specific systems. As Fixed-Point arithmetic logics are faster and more area efficient, but sometimes it is desirable to implement calculation using Floating-Point numbers. In most of the digital signal processing applications addition and multiplication is done frequently. This paper presents a review of the Floating Point unit for a signal processing applications, which has faster rate of operations.

关键词： Digital signal processing Floating-point arithmetic Computers Graphics processing units Dynamic range Signal processing algorithms

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Human detection and tracking in video surveillance system

Human detection and tracking in video surveillance system

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IEEE International Conference on Computational Intelligence and Computing Research

作者： K. V. Sriram R. H. Havaldar Department of E&C Engineering Visvesvaraya Technological University Belagavi Karnataka India KLE Dr. M.S.Sheshgiri College of Engineering and Technology Belagavi Karnataka India

ISBN: (纸本)9781509006137

The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the person, the original size of the video is reconstructed using Papoulis-Gerchberg method. The performance analysis is carried out by comparing with the state-of-the-art-algorithms. The experimental results show that the proposed method is well suited for detecting and tracking the person in lower processing time.

关键词： image resolution Conferences Computational modeling Computer vision Kalman filters image color analysis Video sequences

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Unsupervised Feature Selection Algorithm Based on Sparse Representation

Unsupervised Feature Selection Algorithm Based on Sparse Rep...

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International Conference on systems and Informatics

作者： Guoqing Cui Jie Yang Masoumeh Zareapoor Jiechen Wang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Complex Network and Control Lab Shanghai Jiao Tong University

ISBN: (纸本)9781509055227

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR.

关键词： Unsupervised feature selection Sparse representation Feature evaluation function Computation load Stability

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A Key Frame Selection Algorithm Based on Sliding Window and image Features

A Key Frame Selection Algorithm Based on Sliding Window and ...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Jigang Cao Linchen Yu Mianlong Chen Xinyue Cui School of Computer China University of Geosciences Wuhan

ISBN: (纸本)9781509053827

Network traffic associated with video increases sharply, how to choose the interested information for a number of Internet users is challenging. So, technologies and applications related with video, such as video search, video fast browsing, video index and storage are in great demand. Behind these technologies and applications, a core problem is how to quickly browse massive video data and obtain the main content of the video. To solve this problem, different key frame extraction algorithms have been proposed. Due to the diversity of video content, different video have different characteristics. So the design of general video key frame extraction algorithm to solve the problem is not the reality. The main trend for the problem is to design the key frame extraction algorithm based on the characteristics of the video itself. In this article, we mainly focus on videos with edited boundaries and shot conversions. Aiming at this kind of video, we have designed and implemented video key frame extraction algorithm based on sliding window, the global feature Gist and local feature point detection algorithm SURF. In this algorithm, we use Gist feature to construct the global scene information of frames, and the SURF keypoint detection algorithm to extract local keypoints as local feature for each frame. Then, shot segmentation based on sliding window and shot merging algorithm is applied to dividing the original video into several shots. After that, we select the most representative frames in each video shot as key frames. Finally we evaluate the result of the algorithm from the subjective and objective perspective. Results show that key frames extracted in the algorithm are of high quality and can basically cover the main content of the original video.

关键词： Feature extraction Algorithm design and analysis Signal processing algorithms Clustering algorithms Detection algorithms image color analysis Visualization

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Single image Dehazing Using Dark Channel Prior and Minimal Atmospheric Veil

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KSII TRANSACTIONS ON INTERNET AND INFORMATION systems 2016年第1期10卷 341-363页

作者： Zhou, Xiao Wang, Chengyou Wang, Liping Wang, Nan Fu, Qiming Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

Haze or fog is a common natural phenomenon. In foggy weather, the captured pictures are difficult to be applied to computer vision system, such as road traffic detection, target tracking, etc. Therefore, the image dehazing technique has become a hotspot in the field of image processing. This paper presents an overview of the existing achievements on the image dehazing technique. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on two main works, that is, image dehazing scheme based on atmospheric veil and image dehazing scheme based on dark channel prior. After the overview and a comparative study, we propose an improved image dehazing method, which is based on two image dehazing schemes mentioned above. Our image dehazing method can obtain the fog-free images by proposing a more desirable atmospheric veil and estimating atmospheric light more accurately. In addition, we adjust the transmission of the sky regions and conduct tone mapping for the obtained images. Compared with other state of the art algorithms, experiment results show that images recovered by our algorithm are clearer and more natural, especially at distant scene and places where scene depth jumps abruptly.

关键词： image dehazing dark channel prior atmospheric veil guided filtering bilateral filtering tone mapping

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Massive Online Crowdsourced Study of Subjective and Objective Picture Quality

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IEEE TRANSACTIONS ON image processing 2016年第1期25卷 372-387页

作者： Ghadiyaram, Deepti Bovik, Alan C. Univ Texas Austin Dept Elect & Comp Engn Lab Image & Video Engn Austin TX 78712 USA

Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at http://***/research/ChallengeDB/***.

关键词： Perceptual image quality subjective image quality assessment crowdsourcing authentic distortions

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AprilTag 2: Efficient and robust fiducial detection

AprilTag 2: Efficient and robust fiducial detection

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： John Wang Edwin Olson Computer Science and Engineering Department at the University of Michigan in Ann Arbor USA

ISBN: (纸本)9781509037636

AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.

关键词： Detectors image edge detection image segmentation Robustness Robots Decoding Visualization

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Real-Time Human Detection for Robots using CNN with a Feature-Based Layered Pre-filter

Real-Time Human Detection for Robots using CNN with a Featur...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： E. Martinson V. Yalla Toyota InfoTechnology Center Mountain View CA 94043 USA

ISBN: (纸本)9781509039302

Convolutional neural networks (CNNs), in combination with big data, are increasingly being used to engineer robustness into visual classification systems including human detection. One significant challenge to using a CNN on a mobile robot, however, is the associated computational cost and detection rate of running the network. In this work, we demonstrate how fusion with a feature-based layered classifier can help. Not only does score-level fusion of a CNN with the layered classifier improve precision/recall for detecting people on a mobile robot, but using the layered system as a pre-filter can substantially reduce the computational cost of running a CNN - reducing the number of objects that need to be classified while still improving precision. The combined real-time system is implemented and evaluated on a two robots with very different GPU capabilities.

关键词： image segmentation Classification algorithms Robots Neural networks Graphics processing units Cameras Mathematical model Robot Sorting algorithm Cameras image segmentation pre-filter Neural network Graphics processing Unit Mathematical Model Mobile robots layered systems big data

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Optimization of image processing techniques to detect and reconstruct the image of concealed blade for MMW imaging system

Optimization of image processing techniques to detect and re...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bambam Kumar Prabhat Sharma Rohit Upadhyay Dharmendra Singh K P Singh Department of Electronics & Communication Engineering Indian Institute of Technology Roorkee Roorkee India Instrument Research and Development Establishment Raipur Dehradun Uttarakhand Department of Electronics Engineering Indian Institute of Technology (BHU) Varanasi India

ISBN: (纸本)9781509033331

The concealed weapon, like blade, detection and identification is one of the most puzzling task faces by security agency. Researchers have demonstrated MMW imaging systems to detect concealed targets like gun, knife and scissors but detection of small size target like blade with different orientation is still challenging due to resolution limitation of MMW imaging system. The success of small size concealed target detection depends upon scanning step size of imaging system and dielectric property of covering cloths and hidden object. Therefore, resolution enhancement techniques may play a very important role for small size concealed target detection. To perceive such challenges, active V-band MMW radar conjunction with image processing techniques has been demonstrated for detection and identification of concealed blade and obtained two dimensional good quality of images of concealed blade under different cloths at various angle. For this purpose, a critical analysis of various signal and image processing has been carried out and integrated following algorithms like singular value decomposition (SVD) for clutter reduction, discrete wavelet transform (DWT) for resolution enhancement, thresholding for target detection and in last artificial neural network (ANN) based algorithm for rotation invariant target identification. An image processing based methodology has been proposed by which the concealed target like blade can be successfully detected.

关键词： Blades Discrete wavelet transforms image resolution Imaging Radar imaging Artificial neural networks

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Smart Surveillance System for Detecting Interpersonal Crime

Smart Surveillance System for Detecting Interpersonal Crime

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International Conference on Communication and Signal processing

作者： Robin Singh Sidhu Mrigank Sharad Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur WB 721302 India

ISBN: (纸本)9781467385497

We propose information processing techniques for CCTV based surveillance systems employed in (a) work environments and (b) public places and transport, for automated identification of scenes of inter-personal crime. Although both the scenarios presented in this work employ similar signal processing and learning algorithms, the objective involved are significantly different. In (a) we aim to preserve confidentiality and privacy of official meetings and discussions, while ensuring detection of un-becoming behavior, like: bullying, harassment and assault. In the proposed method we identify such critical conditions using a combination of image and speech processing and ensue conditional video recording and saving. In (b), the target is to identify the occurrence of interpersonal crime using video and voice processing, in order to raise alert at the local surveillance station, which may be receiving numerous CCTV videos from neighboring areas. This can be an assistance to the security personnel, responsible to monitor large number of screens. The proposed methods can be useful curbing inter-personal violence, and crime against women, in the form of eve teasing, and harassment.

关键词： Surveillance system Video processing Speech processing

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