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Feature learning for effective content-based image retrieval 4th

Feature learning for effective content-based image retrieval

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4th international conference on computer Vision and image processing, CVIP 2019

作者： Marab, Snehal Pawar, Meenakshi SVERI PandharpurMaharashtra India

ISBN: (纸本)9789811540141

Increase in the use of mobile phones for scene capturing turns into the exponential increase in the size of digital libraries. Content-Based image Retrieval (CBIR) is an effective solution to handle this enormous data. Most of the existing approaches are relied on the hand-crafted feature extraction for CBIR. these approaches could fail in large-scale databases. thus, in this paper, we have proposed an end-to-end deep network for CBIR. Proposed network is characterized by the residual learning and thus does not undergoes the vanishing gradient problem. Performance of the proposed method has been evaluated on two benchmark databases namely Corel-10K and GHIM-10K with the help of two evaluation parameters i.e. recall and precision. Comparison with the state-of-the-art methods reveiles that proposed method outperforms existing methods by a large margin on all of the databases. © Springer Nature Singapore Pte Ltd 2020.

关键词： digital libraries

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Apricot variety classification using image processing and machine learning approaches 4

Apricot variety classification using image processing and ma...

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4th international conference on Vision, image and Signal processing, ICVISP 2020

作者： Mirnezami, Seyed Vahid Hamidisepehr, Ali Ghaebi, Mahdi Hassan-Beygi, Seyed Reza Colaberry Incorporated Boston MA United States Agricultural Engineering Department University of Tehran Iran

ISBN: (纸本)9781450389532

Apricot which is a cultivated type of Zerdali (wild apricot) has an important place in human nutrition and its medical properties are essential for human health. the objective of this research was to obtain a model for apricot mass and separate apricot variety with image processing technology using external features of apricot fruit. In this study, five verities of apricot were used. In order to determine the size of the fruits, three mutually perpendicular axes were defined, length, width, and thickness. Measurements show that the effect of variety on all properties was statistically significant at the 1% probability level. Furthermore, there is no significant difference between the estimated dimensions by image processing approach and the actual dimensions. the developed system consists of a digital camera, a light diffusion chamber, a distance adjustment pedestal, and a personal computer. images taken by the digital camera were stored as (RGB) for further analysis. the images were taken for a number of 49 samples of each cultivar in three directions. A linear equation is recommended to calculate the apricot mass based on the length and the width with R2 = 0.97. In addition, ANFIS model with C-means was the best model for classifying the apricot varieties based on the physical features including length, width, thickness, mass, and projected area of three perpendicular surfaces. the accuracy of the model was 87.7. © 2020 ACM.

关键词： Machine learning

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GPU-based Hybrid Parallel Logic Simulation for Scan Patterns 4

GPU-based Hybrid Parallel Logic Simulation for Scan Patterns

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4th IEEE international Test conference in Asia (ITC-Asia)

作者： Lai, Liyang Zhang, Qiting Tsai, Hans Cheng, Wu-Tung Shantou Univ Key Lab Intelligent Mfg Technol Guangdong Prov Key Lab Digital Signal & Image Pro Minist Educ Elect Engn Shantou Guangdong Peoples R China Mentor Wilsonville OR USA

ISBN: (纸本)9781728189444

GPGPU, general-purpose computing on graphics processing units, has been witnessed growing from a niche to a mainstream computing paradigm in the last decade. It is widely deployed in machine learning, artificial intelligence, and many scientific applications. In this article, we study gate-level logic simulation for scan test patterns, one of the key algorithmic components for test generation, fault grading and design rule check. We are exploring if GPGPU can deliver scalable performance speedup as promised by its massive parallelism. We discuss the limitations of the state of art work. With GPUs' architectural features in mind, a novel algorithm of hybrid race-tolerant parallel logic simulation is proposed to unleash its immense power of parallelization. Its unique integration of oblivious simulation and event-driven simulation leads to a scalable realization of parallel logic simulation for scan patterns. For the first time in the literature, it is demonstrated that a modest GPU can handle an industrial design of over twenty million gates with excellent performance scalability.

关键词： event-driven simulation GPGPU oblivious simulation parallel logic simulation

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Two efficient image bag generators for multi-instance multi-label learning 4th

Two efficient image bag generators for multi-instance multi...

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4th international conference on computer Vision and image processing, CVIP 2019

作者： Bhagat, P.K. Choudhary, Prakash Singh, Kh Manglem Department of Computer Science and Engineering National Institute of Technology Manipur Imphal India Department of Computer Science and Engineering National Institute of Technology Hamirpur HamirpurHP India

ISBN: (纸本)9789811540141

image annotation plays a vital role in dealing with effective organization and retrieval of a large number of digital images. Multi-instance multi-label (MIML) learning can deal with complicated objects by solving the ambiguity in both input and output space. image bag generator is a key component of MIML algorithms. A bag generator takes an image as its input and generates a set of instances for that image. these instances are the various subparts of the original image and collectively describe the image in totality. this paper proposes two new bag generators which can generate an instance for every possible object present in the image. the proposed bag generators effectively utilize the correlations among pixels to generate instances. We demonstrate that the proposed bag generators outperform the state-of-the-art bag generator methods. © Springer Nature Singapore Pte Ltd 2020.

关键词： image annotation

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Robust Iris Recognition Framework Using computer Vision Algorithms 4

Robust Iris Recognition Framework Using Computer Vision Algo...

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4th international conference on Smart Grid and Smart Cities (ICSGSC)

作者： Hussein, Nashwan Jasim Al Furat Al Awsat Tech Univ Tech Coll Management Najaf Iraq

ISBN: (纸本)9781728194042

Nowadays under human observations iris recognition is playing an essential role, it observed that there are some points are very essential for this method like accuracy, efficiency & processing time. there are several problems has been recorded for this kind of technique. Under previous researches, many concepts have been reported for iris-based segmentation, classification & feature extraction techniques. this research dealt with implementing a novel framework of robust iris segmentation utilizing computer vision technique, FFNN classifier & hybrid feature extraction technique. For the non-iris images method of the novel iris, segmentation has been proposed in this paper. the technique of novel iris segmentation based on 2 methods regards pupil segmentation, after that fusion of enhancing & shrinking visible contour has been framed for segmentation of iris through collaborating novel burden forcing for visible contour model. Moreover, then UN wrapped iris segmentation performed well with the technique of normalization non-circular iris. Whatever, for better iris segmentation research, utilized a proposed method of feature extraction under Discrete Wavelet Transform, letter utilized geometric & texture features of the segmented image. Achieved features have collaborated through a vector of a hybrid feature. For the reason of recognition, we are utilizing a vision algorithm with an FFNN classifier. this paper focused on the concepts related to a robust iris recognition framework using a vision algorithm. Daugman demodulates the yield of the Gabor channels to pack the information. It separates the stage data into four levels and for every conceivable quadrant in-plane to get a minimal 256-byte layout, which takes into consideration proficient capacity and correlation of irises. Experimental outputs have been described as the proposed robust iris segmentation method adopted greater accuracy on the challenging segmentation algorithm thousand databases.

关键词： Iris recognition segmentation algorithm Vision algorithm robust segmentation vision computer method

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Investigating Movement Detection in Unedited Camera Footage 4th

Investigating Movement Detection in Unedited Camera Footage

引用

4th international conference on Advances in Computing and Data Sciences, ICACDS 2020

作者： Sciberras, Samuel Vella, Joseph G. Faculty of ICT Department of Computer Information Systems University of Malta Msida Malta

ISBN: (纸本)9789811566332

digital evidence from CCTVs is an aid in crime scene investigations and there is a demand for more automation. this paper describes a system that detects motion induced events within a video clip based on user-defined criteria, such as filtering by colour and size of the moving object and then extracts features and regions where events have been detected. Post processing includes finding association rules between objects that appear simultaneously in a clip based on their colour. All processing techniques follow best practices. the available Wallflower dataset is used for evaluation, and confusion matrices are computed by comparing the results achieved by this system against the ground truth values for each image sequence. Ranges of effective pre-processing parameter values were set for erosion, dilation and background subtractor threshold and the system was tested across a wide array of parameter values. For each combination, measures are extracted and the F1 Score is calculated. the lowest and highest F1 Score obtained across all image sequences were of 67% and 95% respectively. It is noted that the image quality of clips and background affect the F1 scores. © 2020, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Tomographic Reconstruction using Range Restricted GMRES 20

Tomographic Reconstruction using Range Restricted GMRES

引用

4th international conference on graphics and Signal processing, ICGSP 2020

作者： Jaffri, Nasif Raza Shi, Liu Abrar, Usama School of Computer and Control North China Electric Power University Beijing China

ISBN: (纸本)9781450377812

this paper is concerned with the reconstruction of large ill determined and ill-posed problem using the iterative method. the ill-posed problems ascend from the discretization of ill-posed linear systems. the technique used in this work widely applicable to reconstruct the two-dimensional tomographic image. Reconstruction achieved after producing a right-hand side that can be done using any of the famous tomographic experimental arrangements (TDLAS, ECT, ultrasound, etc.). the problem discussed in this paper is with error contaminated right-hand side. the numerical solution of this matrix is fractionally complicated as the matrix is vast and ill-conditioned. Range-restricted GMRES (RRGMRES) used as regularization in this work. the iteration method is implemented using MATLAB to compute the inverse problem. © 2020 ACM.

关键词： Iterative methods

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Recaptured image Forensics Based on image Illumination and Texture Features 4

Recaptured Image Forensics Based on Image Illumination and T...

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4th international conference on Video and image processing, ICVIP 2020

作者： Yuzhan, Ma Jalab, Hamid Abdullah Hassan, Wasswa Fan, Ding Minjin, Ma Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science Texas Tech University Lubbock United States School of Computer and Communication Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9781450389075

Advanced devices like digital cameras and smart devices have triggered the greater possibilityof recapturing high-quality images from output media. these high-quality recaptured images are genuine enough to escape the detection of normal human eyes, and the existing image forensic techniques. Giving the malicious users opportunities to distribute these fraudulent images to execute illegalactivities. Due to the difference in illumination environments during original scene capture and recapturing processes, the recaptured image exhibits different illumination feature patterns comparedwith the original scene images. Furthermore, the fact that during every image capturing event the camera compresses the images, implies that a recaptured image suffers double compression which significantly alters its texture features. Various studies have showed illumination and texture featuresto be key features for generating statistical patterns that can give pertinent information for recaptured image detection. this study proposes an efficient method that combines the illumination and texture features for enhancing the recaptured image detection. the proposed approach begins with dividing the image into 16x16 blocks, extracts texture and illumination features from each image block and finally aggregates the extracted features for classification. Evaluated on a publicly available image dataset with high quality recaptured images, the proposed approach recorded good detectionresults with a detection accuracy of up to 94.4%, a recall of 95.6% and a false detection rateof 6.7%. © 2020 ACM.

关键词： image texture

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Development process of animal image recognition technology and its application in modern cow and pig industry 4

Development process of animal image recognition technology a...

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2020 4th international Workshop on Advances in Energy Science and Environment Engineering, AESEE 2020

作者： Liu, Ting Ting Feng Wu, Ding Wang, Li Yun Agricultural Information Institute of CAAS Beijing100081 China

the construction of digital farms had emerged since 2018. It used computer image processing and pattern recognition technology to study cow and pig recognition through interdisciplinary research. this paper introduced specific cases about using image recognition technology to build modern cow and pig farms. then reviewed the research on animal image recognition technology and had the following research directions: iris recognition, retinal recognition, nose pattern recognition and face recognition. In the end this article proposed how to "DIY"classification for pigs. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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A Denoising Method of Remote Sensing images Based on Improved BM3D 20

A Denoising Method of Remote Sensing Images Based on Improve...

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4th international conference on computer Science and Application Engineering, CSAE 2020

作者： Chen, Junyu Li, Haiwei Chen, Tieqiao Hu, Bingliang Zhang, Geng Wang, Shuang Liu, Song Xi'an Institute of Optics and Precision Mechanics of Cas Xi'an China and University of Chinese Academy of Sciences Beijing China Xi'an Institute of Optics and Precision Mechanics of Cas Xi'an China

ISBN: (纸本)9781450377720

Denoising of remote sensing images is always one of the hotspots in the field of digital image processing. the quality of denoising determines the precision of subsequent processing directly. the BM3D algorithm takes the high similarity of image neighborhoods into consideration, which shows good stability and accuracy in the field of image denoising. However, in the case of strong noise, BM3D can't match the image-block precisely, which reduces the denoising effect for the remote sensing image. therefore, in order to solve the problem of BM3D algorithm during the remote sensing image processing, we propose a local block-matching algorithm based on edge search strategy and residual filtering, according to the high similarity at the edge of the remote sensing image. We compare the proposed algorithm with the classic denoising method, and the experimental results show that the proposed algorithm not only protects the information of edge and detail, but also improves the value of PSNR. A better denoising effect was obtained on remote sensing images. © 2020 ACM.

关键词： image denoising

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