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2nd International conference on Information and Communication Technology for Intelligent systems (ICTIS)

作者： Ahuja, Sanjay Kumar Shukla, Manoj Kumar Amity Univ AIIT Noida Uttar Pradesh India Amity Univ ASET Noida Uttar Pradesh India

ISBN: (纸本)9783319636450;9783319636443

There are various destructive as well as non-destructive techniques available to detect corrosion in metallic surfaces. Digital image processing is widely being used for the corrosion detection in metallic surface. This non-destructive approach provides cost effective, fast and reasonably accurate results. Several algorithms have been developed by different researchers and research groups for detecting corrosion using digital image processing techniques. Several algorithms related to color, texture, noise, clustering, segmentation, image enhancement, wavelet transformation etc. have been used in different combinations for corrosion detection and analysis. This paper reviews the different image processing techniques and the algorithms developed and used by researchers in various industrial applications.

关键词： Computer vision image processing Corrosion detection

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Gray Scale image Restoration Using Advanced Atmospheric Light Estimation

Gray Scale Image Restoration Using Advanced Atmospheric Ligh...

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International conference on image Information processing

作者： Sasmita Mahakud Pradipta Roy EOTS Integrated Test Range (DRDO) Chandipur Balasore Odisha India

visibility of scene is often degraded by different atmospheric phenomena which results in failure of many computer vision applications like outdoor object recognition systems, barrier detection systems, visual surveillance systems, intelligent transportation systems etc. Light coming out from the source and scattered by atmospheric particles towards the camera is the atmospheric light. In many existing image restoration algorithms the results depend on this light. So this paper introduces a image restoration method applied for fog removal which search atmospheric light in a superior way by dividing an image into blocks. Fog decreases slowly from infinite sky regions to the nearer camera regions horizontally downward direction. Consequently atmospheric light is estimated locally in each blocks from upper to lower. The global atmospheric light is the weighted average of all the local atmospheric light. The weight is calculated from histogram of each block. There are many algorithms which suffer from halo effects and edge hammering at the output. In this paper it is rectified by nonlinear filtering which is a pre-processing step. A new edge preserving transmission is also produced using this nonlinear filtering to reduce halo effects. Experimental results are verified and compared qualitatively and quantitatively with the existing haze removal methods. Comparison results shows a better performance in terms of saturated pixels and visual evaluation of different real time scenes.

关键词： image restoration Cameras Histograms Atmospheric modeling image edge detection Filtering Channel estimation

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Region Based Approach for Binarization of Degraded Document images 21st

Region Based Approach for Binarization of Degraded Document ...

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21st International Multi-conference on Advanced Computer systems, ACS 2018

作者： Michalak, Hubert Okarma, Krzysztof Department of Signal Processing and Multimedia Engineering Faculty of Electrical Engineering West Pomeranian University of Technology Szczecin 26 Kwietnia 10 Szczecin71-126 Poland

ISBN: (纸本)9783030033132

Binarization of highly degraded document images is one of the key steps of image preprocessing, influencing the final results of further text recognition and document analysis. As the contaminations visible on such documents are usually local, the most popular fast global thresholding methods should not be directly applied for such images. On the other hand, the application of some typical adaptive methods based on the analysis of the neighbourhood of each pixel of the images is time consuming and not always leads to satisfactory results. To bridge the gap between those two approaches the application of region based modifications of some histogram based thresholding methods has been proposed in the paper. It has been verified for well known Otsu, Rosin and Kapur algorithms using the challenging images from Bickley Diary dataset. Experimental results obtained for region based Otsu and Kapur methods are superior in comparison to the use of global methods and may be the basis for further research towards combined region based binarization of degraded document images. © 2019, Springer Nature Switzerland AG.

关键词： Character recognition

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An Elephant Detection System to Prevent Human-Elephant Conflict and Tracking of Elephant Using Deep Learning

An Elephant Detection System to Prevent Human-Elephant Confl...

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International conference on Information Technology Research (ICITR)

作者： K. S. P. Premarathna R. M. K. T. Rathnayaka J. Charles Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9781665414753

ISBN: (纸本)9781665414760

Human settlement is spreading to forest boundary areas because of the population growth, it triggers disputes between elephants and humans, leading to the loss of property and life. Continuous monitoring and tracking of elephants are difficult due to their large size and movement. Therefore, large-scale for real-time detection and alert of elephant intrusion into human settlements, monitoring is needed. Many methods had been implemented for the elephant's intrusion detection and warning systems. Wildlife conservation and the management of human-elephant conflict require a cost-effective method of monitoring elephant behavior. In this paper, a method for the identification of the elephant as an object using image processing is proposed. The major aim of the study is to minimize the human-elephant conflict in the forest border areas and the conservation of elephants from human activities as well as protect human lives from elephant attacks. We used a data set containing elephants and we developed an approach to distinguish elephants and other animals. We used the Convolutional Neural Network and achieved a maximum accuracy of 94 percent. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants.

关键词： Forestry Training Monitoring Classification algorithms Statistics Sociology Real-time systems

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IDOL: A Framework for IMU-DVS Odometry using Lines

IDOL: A Framework for IMU-DVS Odometry using Lines

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2020 IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Cedric Le Gentil Florian Tschopp Ignacio Alzugaray Teresa vidal-Calleja Roland Siegwart Juan Nieto Autonomous Systems Lab ETH Zurich Switzerland Centre for Autonomous Systems School of Mechanical and Mechatronic Engineering University of Technology Sydney Sydney New South Wales Australia Vision for Robotics Lab ETH Zurich Switzerland

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.

关键词： Three-dimensional displays Pipelines Lighting Graphics processing units Benchmark testing Trajectory Optimization

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image Artistic Style Transfer Based on Color Distribution Preprocessing 1

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4th International conference on Cognitive systems and Information processing, ICCSIP 2018

作者： Zhang, Yinshu Chen, Jiayi Si, Xiangyu Tian, Zhiqiang Lan, Xuguang School of Software Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’anShaanxi710049 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

Style transfer is an increasingly popular field that can capture the styles of a particular artwork and use them to synthesize a new image with specific content. Previous NST algorithms have the limitation to transfer styles to correct regions in the output image. Therefore, some regions in the output image have deformed structures of the source image. In this paper, we propose a color preprocessing-based neural style transfer method to overcome the limitation. To reduce impacts caused by color differences between source image and style, we propose three models based on a color iterative distribution transform algorithm (IDT). The first one is named original color-preprocessed (OCp) model, which uses IDT to transform the color probability density function (PDF) of source image into that of style image. The second one is named exposure-corrected original color-preprocessed (EC-OCp) model, which adds an automatic detail-enhanced exposure correction module before OCp model. When source image is underexposed, EC-OCp model can achieve better results than OCp model. The third one is style color-preprocessed (SCp) model. It uses IDT to transform the color PDF of style image into that of source image. The original structures are well protected in the output image. According to experiments, the proposed models are robust to the source images with more conditions. Therefore, they have more usage values than the original method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Color

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Unsupervised learning algorithms for MRI Brain Tumor Segmentation

Unsupervised learning algorithms for MRI Brain Tumor Segment...

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conference on Signal processing And Communication Engineering systems (SPACES)

作者： Rao, G. Sasibhusana Srinivas, B. Andhra Univ Coll Engn A Dept ECE Visakhapatnam Andhra Pradesh India MVGR Coll Engn A Dept ECE Vizianagaram India

ISBN: (纸本)9781538623695

The structure of the brain can be seen by the Magnetic Resonance (MR) image output. MR scanned image of the brain is utilized for the entire study in this paper. The MR image filter is more agreeable than some other outputs for analysis. It will not influence the human body since it does not hone any radiation. In digitization of MR scanned image, segmentation of brain tumor is one kind of challenging problems and it is critical to clinical diagnosis. So segmentation needs to be accurate, robust, and efficient to avoid impacts caused by various large and complex biases added to images. Clustering algorithms have been widely used for the segmentation. In this paper, the K-means (KM) clustering and Fuzzy C-means (FCM) clustering algorithms are used to locate the tumor and extract it. Comparative analysis in terms of Segmented area, Relative area, Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) is performed between K-means clustering and FCM clustering algorithms. The obtained performance measures from the experiments indicate the superiority of the chosen FCM algorithm over the K-means algorithm. That is 0.93% of relative segmented tumor area for FCM shows that the area which was effected by the tumor in the original MR image is segmented as a tumor. The FCM Algorithm has less processing time of 8.639 seconds compared to 22.831 seconds for KM algorithm.

关键词： Unsupervised Learning Method MRI Brain Segmentation Fuzzy C means K-means(KM) clustering algorithm Magnetic Resonance image (MRI)

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Improving the criteria for quality assessment of image processing algorithms

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Journal of Physics: conference Series 2021年第1期1862卷

作者： D V Dubinin A I Kochegurov V E Geringer Tomsk State University of Control Systems and Radioelectronics Lenin Avenue 40 Tomsk 634050 Russian Federation National Research Tomsk Polytechnic University Lenin Avenue 30 Tomsk 634050 Russian Federation Baden-Wuerttemberg Cooperative State University Faculty of Engineering Fallenbrunnen 2 D-88045 Friedrichshafen Germany

Quality criteria are the essence of a measurement system. The goal of assessing the quality of software elements (algorithms) used for image processing is to ensure control of technical performance indicators of the system as a whole or its individual functional units under the reduction of costs associated with minimizing the loss function (tuning and debugging). The correct choice of individual metrics for the generalized quality indicator to solve tasks in a particular subject area is one of the key steps in system optimization. It ensures the most flexible approach to testing the developed software elements, identifying and eliminating their functional shortcomings. At the same time, we can say that in this way the generalized indicator of the entire system (its goal function) is optimized.

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Spectral Constrained Frequency Selective Extrapolation for Rapid image Error Concealment 25

Spectral Constrained Frequency Selective Extrapolation for R...

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25th International conference on systems, Signals and image processing (IWSSIP)

作者： Genser, Nils Seiler, Juergen Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781538669792

High quality error concealment plays a crucial role in image and video processing. In general, those algorithms introduce a high computational load as they calculate complex models to estimate the missing samples. In the following, a highspeed algorithm is introduced, which succeeds the state-of-the-art Frequency Selective Extrapolation. Therefore, two novel spectral constraints are introduced in this paper. Firstly, processing the DC part of the distorted spectrum separately allows to decrease computational complexity and to increase the reconstruction quality. Secondly, by exploiting spectral properties, only a subset of basis functions has to be processed. Moreover, complex-conjugated pairs of basis functions are selected to generate a symmetric Fourier spectrum and an according real-valued output signal. Taking these constraints into account, a PSNR gain of up to 0.36 dB is achieved compared to the state-of-the-art algorithm, while the execution speed is approximately doubled.

关键词： Error Concealment Inpainting Signal Extrapolation

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A Portable image processing Accelerator using FPGA 7

A Portable Image Processing Accelerator using FPGA

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7th International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Tsiktsiris, Dimitris Ziouzios, Dimitris Dasygenis, Minas Univ Western Macedonia Dept Informat & Telecommun Engn Kozani 50100 Greece

ISBN: (纸本)9781538647882

One of the most common uses of FPGAs is as implementation platforms for graphics processing applications. Their structure can exploit spatial and temporal parallelism, but such parallelisation depends on the processing model and hardware constraints of the system. Those restrictions can force the designer to reformulate the algorithm. In this paper we present an FPGA design as a portable USB accelerator device which implements the Grayscale and Sobel Edge Detection algorithms, two of the most fundamental algorithms in digital image processing.

关键词： Acceleration image processing Field programmable gate arrays Programmable logic arrays Application software

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