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Recent Trends in image processing and Pattern Recognition 1

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丛书名： Communications in computer and Information Science

1000年

作者： KC Santosh Aaisha Makkar Myra Conway Ashutosh K. Singh Antoine Vacavant Anas Abou el Kalam Mohamed-Rafik Bouguelia Ravindra Hegadi

ISBN: (数字)9783031530821

ISBN: (纸本)9783031530814

this book constitutes the refereed proceedings of the 6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota.;the 62 full papers included in this book were carefully reviewed and selected from 216 submissions. the papers are organized in the following topical sections:;Volume I:;Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications.;Volume II:;Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

关键词： computer Imaging, vision, Pattern Recognition and graphics Artificial Intelligence computer System Implementation computer Applications computer Applications

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Improving Person Re-Identification Systems: A Novel Score Fusion Framework for Rank-n Recognition 16

Improving Person Re-Identification Systems: A Novel Score Fu...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Barman, Arko Shah, Shishir K. Univ Houston 4800 Calhoun Rd Houston TX 77004 USA

ISBN: (纸本)9781450347532

Person re-identification is an essential technique for video surveillance applications. Most existing algorithms for person re-identification deal with feature extraction, metric learning or a combination of both. Combining successful state-ofthe-art methods using score fusion from the perspective of person re-identification has not yet been widely explored. In this paper, we endeavor to boost the performance of existing systems by combining them using a novel score fusion framework which requires no training or dataset-dependent tuning of parameters. We develop a robust and efficient method called Unsupervised Posterior Probability-based Score Fusion (UPPSF) for combination of raw scores obtained from multiple existing person re-identification algorithms in order to achieve superior recognition rates. We propose two novel generalized linear models for estimating the posterior probabilities of a given probe image matching each of the gallery images. Normalization and combination of these posterior probability values computed from each of the existing algorithms individually, yields a set of unified scores, which is then used for ranking the gallery images. Our score fusion framework is inherently capable of dealing with different ranges and distributions of matching scores emanating from existing algorithms, without requiring any prior knowledge about the algorithms themselves, effectively treating them as "black-box" methods. Experiments on widely-used challenging datasets like VIPeR, CUHK01, CUHK03, EthZ1 and EthZ2 demonstrate the efficiency of UPPSF in combining multiple algorithms at the score level.

关键词： Person Re-identification Score Fusion Posterior Probability Estimation

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Performance Estimation of Numerical Biospeckle Indexing Techniques Using Rotating Diffuser Based Fixed Correlation Modelling 6

Performance Estimation of Numerical Biospeckle Indexing Tech...

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6th International conference on computer Applications In Electrical Engineering-Recent Advances (CERA)

作者： Chatterjee, Amit Dhanotia, Jitendra Bhatia, Vimal Prakash, Shashi Indian Inst Technol Indore Discipline Elect Engn Signals & Software Grp Indore 453552 Madhya Pradesh India Devi Ahilya Univ Inst Engn & Technol Dept Elect & Instrumentat Engn Indore 452017 Madhya Pradesh India

ISBN: (纸本)9781509048748

In this paper, we propose a simple system model to generate a temporal sequence of biospeckle patterns which is based on speckle dynamicity by clockwise rotation of a diffuser plate in coherence illumination field. these generated patterns are applied to two popular biospeckle quantification strategies namely inertia moment and absolute value of difference. Performance of both algorithms is assessed and compared by using a number of fixed correlated synthetic speckle sequences.

关键词： image processing metrology biospeckle simulation model inertia moment absolute value difference

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A vision Based Positioning Gas Leakage Test Automation System 6

A Vision Based Positioning Gas Leakage Test Automation Syste...

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6th International conference on Control Engineering and Information Technology (CEIT)

作者： Savran, Akin Ilker Kumbasar, Tufan Arcelik Tumble Dryer Plant TR-59500 Cerkezkoy Tekirdag Turkey Istanbul Tech Univ Control & Automat Engn Dept TR-34469 Istanbul Turkey

ISBN: (纸本)9781538676417

the automation project with vision based positioning by using ABB IRB 140 robot is proposed, the coordinate transformation and camera calibration is applied, two types gas leakage test for HP dryers are proposed and the image processing algorithms for welding points of gas leakage test by dividing three areas of chassis are proposed. For this purpose, the robotic automation system has been installed and the real-time experimental studies are performed to show the effectiveness of the vision based positioning ABB IRB 140 robot for the gas leakage test by presentation the derivations of welding points positions.

关键词： robotics industrial robots computer vision ABB IRB140 Robot

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Unsupervised domain adaptation without source domain training samples - a maximum margin clustering based approach 16

Unsupervised domain adaptation without source domain trainin...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Saha, Sudipan Banerjee, Biplab Merchant, Shabbir N. Indian Inst Technol Dept Elect Engn Bombay Maharashtra India Indian Inst Technol Dept Comp Sci Roorkee Uttar Pradesh India

ISBN: (纸本)9781450347532

Unsupervised domain adaptation (DA) techniques inherently assume the presence of ample amount of source domain training samples in addition to the target domain test data. the domains are characterized by domain-specific probability distributions governing the data which are substantially different from each other. the goal is to build a task oriented classifier model that performs proportionately in both the domains. In contrary to the standard unsupervised DA setup, we propose a maximum-margin clustering (MMC) based framework for the same which does not consider source domain labeled samples. Instead we formulate it as a joint clustering problem of all the samples from both the domains in a common feature subspace. the Geodesic Flow Kernel (GFK) based subspace projection technique in the Grassmannian manifold is adopted to cast the samples in a domain invariant space. Further, the MMC stage is followed to simultaneously group the data based on the maximization of margins and a classifier is learned for each group. the data overlapping problem is taken care of by specifically learning a SVM-KNN classifier for the potentially unreliable samples per group. We validate the framework on a pair of remote sensing images of different modalities for the purpose of land-cover classification and a generic object dataset for recognition. We observe that the proposed method exhibits performances at par with the fully supervised case for both the tasks but without the requirement of costly annotations.

关键词： Domain adaptation Max-margin clustering Geodesic Flow Kernel

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Dataset augmentation with synthetic images improves semantic segmentation 6th

Dataset augmentation with synthetic images improves semantic...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Goyal, Manik Rajpura, Param Bojinov, Hristo Hegde, Ravi Varanasi Varanasi221005 India Indian Institute of Technology Gandhinagar Gandhinagar382355 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantic Segmentation

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Enhancing Generalization Ability in Deepfake Detection via Continual Learning 24

Enhancing Generalization Ability in Deepfake Detection via C...

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15th indian conference on computer vision graphics and image processing

作者： Usmani, Shaheen Kumar, Sunil Sadhya, Debanjan ABV Indian Inst Informat Technol & Management Gwa Gwalior India

ISBN: (纸本)9798400710759

Deepfake technology generates counterfeit facial images by replicating the characteristics of genuine ones. Sophisticated manipulation tactics have enabled criminals to incite social panic or accrue illegal gains by creating deceptive media, such as forged facial images. Consequently, numerous techniques for detecting deepfakes have been developed to evaluate the authenticity of images. the existing deepfake detection techniques perform well if training and testing datasets exhibit similarity. However, then they do not provide good results if training and testing datasets are dissimilar, thereby not providing generalization ability. In this work, we have proposed the Continual Learning Based Enhanced vision Transformer (CLEViT) model for deepfake detection. the CLEViT model leverages the continual learning mechanism to enhance the model's generalization ability. Continual learning enables deepfake detection models to continuously adapt and update their knowledge based on new data, allowing them to identify manipulation techniques effectively. After that, a vision transformer is used to extract rich contextual information from images using self-attention mechanisms. To validate the efficacy of the model, we have created a multi-task dataset comprising FakeAVCeleb, RFF, RFFD, and the DFDC datasets. the proposed CLEViT model outperforms existing deepfake detection techniques, yielding superior results and simultaneously providing generalization ability.

关键词： Continual learning vision transformer Deepfake Convolutional neural network

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A novel image encryption algorithm based on hash function

A novel image encryption algorithm based on hash function

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Iranian conference on Machine vision and image processing

作者： Seyedzade, Seyed Mohammad Atani, Reza Ebrahimi Mirzakuchaki, Sattar Department of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Computer Engineering University of Guilan Rasht Iran

ISBN: (纸本)9781424497065

In this paper, a novel algorithm for image encryption based on SHA-512 is proposed. the main idea of the algorithm is to use one half of image data for encryption of the other half of the image reciprocally. Distinct characteristics of the algorithm are high security, high sensitivity and high speed that can be applied for encryption of gray-level and color images. the algorithm consists of two main sections: the first does preprocessing operation to shuffle one half of image. the second uses hash function to generate a random number mask. the mask is then XORed with the other part of the image which is going to be encrypted. the aim of this work is to increase the image entropy. Both security and performance aspects of the proposed algorithm are analyzed and satisfactory results are achieved in various rounds. © 2010 IEEE.

关键词： Hash functions

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Video Dehazing using LMNN with respect to Augmented MRF 11

Video Dehazing using LMNN with respect to Augmented MRF

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Borkar, Kushal Mukherjee, Snehasis Indian Inst Informat Technol Sric Chittoor Andhra Pradesh India

ISBN: (纸本)9781450366151

the presence of haze within the atmospheric medium degrades the quality of videos captured by camera sensors. the expulsion of haze, referred to as dehazing, is typically performed subject to the physical degradation display, that involves an explanation of an ill-posed inverse drawback. A few efforts have been made for image dehazing, whereas, video dehazing still remains an unexplored area of research. this paper proposes an approach for video dehazing combining the concepts of single image dehazing, optical stream estimation and Markov Random Field (MRF). the proposed method enhances the temporal and spatial coherence of the hazy video. Assuming that the dark channel of the haze-free picture is zero, we acquire the raw transmission map. In the proposed approach, we focus on the raw transmission map obtained from the dark channel prior using guided filter. We assess the forward and reverse optical streams between the neighboring frames to locate individual pixels using Linear Discriminant Analysis. the color of the haze-free pixels in the frames is approximated by a few hundred discrete colors, which generate a fixed cluster in space and the directions of the pixel. the pixels at a given cluster are spread and can be determined by analyzing the forward and in reverse optical frames to predict its value after haze removal. Largest Margin Nearest Neighbor (LMNN) algorithm is applied to get the smooth transmission map of the foggy frames of the video to approximate the pixel value in the RGB space. the stream fields are utilized in an augmented MRF model on the transmission guide obtained to enhance the temporal and the spatial coherence of the transmission. the proposed method is compared against the state-of-the-art on both real and synthetic videos to preserve the information optimally.

关键词： video dehazing video-based modelling transmission estimation bayes largest margin nearest neighbor regularization

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Transferring Appearance with Mask-Guided Attention 24

Transferring Appearance with Mask-Guided Attention

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15th indian conference on computer vision graphics and image processing

作者： Bandla, Manikanta IISc Bangalore Comp Sci & Automat Bangalore Karnataka India

ISBN: (纸本)9798400710759

Despite significant advancements in large-scale text-to-image generation and text-conditioned image editing, appearance transfer remains relatively unexplored. Transferring appearance aims to transfer an object's appearance in an appearance image to an object in the structure image so that background details are preserved and accurately reflect the transferred object's characteristics. Appearance transfer has practical applications in areas like virtual try-on and e-commerce product placement. Existing methods often require fine-tuning text-to-image diffusion models or are not applicable to virtual try-on and e-commerce scenarios, which is not ideal. In this paper, we introduce a Mask-Guided attention mechanism that replaces the existing self-attention in U-net architecture of Stable diffusion [29]. this approach can be easily integrated into the Masactrl [6] framework, enabling appearance transfer without model fine-tuning and suitable for a wide range of applications. Our method uses masks of objects in images to guide the appearance transfer process, with these masks obtained from the Segment Anything Model (SAM) [17]. this integration of SAM-generated masks allows for precise object localization and more accurate appearance transfer. We have conducted comprehensive experiments on transferring various clothing items (shirts, jeans, t-shirts) onto people, as well as transferring sofas into living spaces.

关键词： Appearance transfer image editing virtual try-on sofa placement masked attention segmentation

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