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A Novel Self-Learning Network Integrating Contrastive Learning, Perceptual Learning and Masked image Modelling 15

A Novel Self-Learning Network Integrating Contrastive Learni...

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15th International conference on graphics and image processing, ICGIP 2023

作者： Chen, Yingxian Yang, Rui Lan, Rushi Guangxi Key Laboratory Image and Graphic Intelligent Processing Guilin University of Electronic Technology China

ISBN: (纸本)9781510675018

Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain similarities, particularly in their data augmentation techniques. Masking, a type of data augmentation, can be utilized for both contrastive learning and masked image modelling. this paper presents a novel deep learning approach on visual unsupervised learning. It integrates previous methods such as contrastive learning, perceptual learning, self-distillation and masked image modelling. In our method, we treat the network that handles the original images as the teacher network, and the network that handles the masked images as the student network. the student network employs the representations extracted by the projection head for contrastive learning, while the features generated by the decoder are employed for masked image modeling. the process of self-knowledge distillation is facilitated by perceptual learning between the teacher and student networks. this model aligns with the main idea of contrastive learning, which aims to pull similar images closer while pushing dissimilar images further apart. Simultaneously, it reflects the main idea of masked image modelling, which enables the extraction of semantic information from large scale masked pixel reconstruction tasks. Additionally, we compare the effect of self-supervised methods to the performance of the model. Our results show that with only 75 epochs of fine-tuning, our 29M-parameter model achieves 78.5% top-1 accuracy on the imageNet-1k dataset. © 2024 SPIE. All rights reserved.

关键词： Unsupervised learning

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Enhancing Video Surveillance with Deep Learning-Based Real-Time Handgun Detection and Tracking 1

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8th International conference on computer vision and image processing (CVIP)

作者： Yadav, Pavinder Gupta, Nidhi Sharma, Pawan Kumar Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India Natl Inst Technol Kurukshetra Kurukshetra 136119 Haryana India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

Handguns, pistols, and revolvers are commonly used in today's world for committing criminal acts, requiring the need for effective surveillance and control systems. However, despite the advancement of security systems, human monitoring and involvement are still necessary to effectively combat these crimes. this paper provides a robust automated handgun identification technique for recorded videos and live CCTV footage that may be used for both control and surveillance purposes. Automatic detection of firearms is crucial for improving people's protection and safety, however, it is a challenging task because of the numerous differences in design, size, and appearance of firearms. In recent years, object detectors have improved, yielding better findings and shorter inference times. the authors used cutting-edge object detector YOLOv7 for firearm detection. A varied and demanding dataset of 15,367 images for weapon identification is also proposed, which is carefully annotated for weapon localization and classification. After analysing the data, it is determined that the model achieves an accuracy rate of 96.80% and recall rate of 90.37%.

关键词： Deep Learning Machine Learning Weapon Detection Surveillance System YOLO

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Autism Spectrum Disorder Classification of Facial images Using Xception Model and Transfer Learning with image Augmentation 29th

Autism Spectrum Disorder Classification of Facial Images Usi...

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29th International conference on Neural Information processing

作者： Shrivastava, Trapti Singh, Vrijendra Agrawal, Anupam Indian Inst Informat Technol Dept Informat Technol Allahabad 211015 Uttar Pradesh India

ISBN: (纸本)9783031301100;9783031301117

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in which the neurology of an autistic person is severely hampered. A child with autism has difficulty answering his name, avoids eye contact, and cannot express his emotions. Early diagnosis can assist children with ASD enhance their intellectual abilities while reducing autistic symptoms. In computer vision, determining developmental disorder problems from facial image data is a significant but largely unexplored challenge. this paper proposed a method to classify autistic and non-autistic facial images using model 1 (Xception) and model 2 (Augmentation + Xception). Among Model 1 and Model 2, Model 2 achieved higher accuracy of 98% and a minimum loss of 0.08.

关键词： Autism Spectrum Disorder Facial image image Augmentation Transfer learning Xception Model

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Multichannel texture image segmentation using local feature fitting based variational active contours 12

Multichannel texture image segmentation using local feature ...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Prasath, V. B. Surya Palaniappan, K. Seetharaman, Guna Dept. of Computer Science University of Missouri Columbia MO United States Air Force Research Laboratory Rome NY United States

ISBN: (纸本)9781450316606

We study an efficient texture image segmentation model for multichannel images using a local feature fitting based active contours scheme. Using a chromaticity-brightness decomposition, we propose a flexible segmentation approach using multi-channel texture and intensity in a globally convex continuous optimization framework. We make use of local feature histogram based weights with the smoothed gradients from the brightness channel and localized fitting for the chromaticity channels. A fast numerical implementation is described using an efficient dual minimization formulation and experimental results on synthetic and real color images indicate the superior performance of the proposed method compared to related approaches. the novel contributions include the use of local feature density functions in the context of a luminance-chromaticity decomposition combined with a globally convex active contour variational method to capture texture variations for image segmentation. © 2012 ACM.

关键词： Luminance

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A Dataset and Model for Crossing indian Roads 13

A Dataset and Model for Crossing Indian Roads

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Brahmbhatt, Siddhi G H Patel College of Engineering and Technology Vallabh Vidyanagar Gujarat Anand India

ISBN: (纸本)9781450398237

Roads in medium-sized indian towns often have lots of traffic but no (or disregarded) traffic stops. this makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross indian roads. Yet, we currently lack datasets collected on indian roads from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in indian conditions. We introduce INDRA (indian Dataset for RoAd crossing), the first dataset capturing videos of indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional neural networks (CNNs). the best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant running DilatedRoadCrossNet, which can help the blind cross indian roads in real-time. the project webpage is https://***/Website/*** © 2022 ACM.

关键词： Convolutional neural networks

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MSDNet: A novel multi-stage progressive image dehazing network 21

MSDNet: A novel multi-stage progressive image dehazing netwo...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Manu, Chippy M. Sreeni, K.G. Computer Vision Lab Dept. Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

this paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. the proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. the network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. the performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness image Quality Evaluator (NIQE) and Blind/Referenceless image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. the experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques. © 2021 ACM.

关键词： image reconstruction

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Towards interpretable facial emotion recognition 21

Towards interpretable facial emotion recognition

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Malik, Sarthak Kumar, Puneet Raman, Balasubramanian Indian Institute of Technology Roorkee India

ISBN: (纸本)9781450391276

In this paper, an interpretable deep-learning-based system has been proposed for facial emotion recognition. A novel approach to interpret the proposed system's results, Divide & Conquer based Shapley additive explanations (DnCShap), has also been developed. the proposed approach computes 'Shapley values' that denote the contribution of each image feature towards a particular prediction. the Divide and Conquer algorithm has been incorporated for computing the Shapley values in linear time instead of the exponential time taken by the existing interpretability approaches. the experiments performed on four facial emotion recognition datasets, i.e., FER-13, FERG, JAFFE, and CK+, resulted in the emotion classification accuracy of 62.62%, 99.68%, 91.97%, and 99.67%, respectively. the results show that DnCShap has consistently interpreted the highly relevant facial features for the emotion classification for various datasets. © 2021 ACM.

关键词： Emotion Recognition

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Improved weighted histogram for illumination invariant mean-shift tracking 14

Improved weighted histogram for illumination invariant mean-...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Phadke, Gargi Velmurugan, Rajbabu Indian Institute of Technology Bombay Mumbai400076 India

ISBN: (纸本)1595930361

Object tracking is a critical task in surveillance and activity analysis. One of the major issues in visual target tracking is variation in illumination. In this paper, we propose an improved weighted histogram approach for mean shift tracking to achieve illumination invariance. the proposed scheme can effectively reduce changes in the illumination of targets with respect to background. Depending on changes in the target appearance, the target model is updated. the experimental results show that the proposed scheme can lead to faster convergence, more accurate localization and robust tracking in several illumination scenarios, when compared to existing methods. Copyright 2014 ACM.

关键词： Target tracking

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indian Plant Recognition in the Wild 7th

Indian Plant Recognition in the Wild

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Muthireddy, Vamsidhar Jawahar, C.V. IIIT Hyderabad Hyderabad India

ISBN: (纸本)9789811586965

Conservation efforts to protect biodiversity rely on an accurate identification process. In the case of plant identification, traditional methods used are manual, time-consuming and require a degree of expertise to operate. As a result, there is an increasing interest today for an automated plant identification system. Such a system can help in aiding plant-related education, promoting ecotourism, creating a digital heritage for plant species among many others. We propose a solution using modern convolutional neural network architectures which achieves state-of-the-art performance for plant classification in the wild. An exhaustive set of experiments are performed to classify 112 species of plants from the challenging Indic-Leaf dataset. the best performing model gives Top-1 precision of 90.08 and Top-5 precision of 96.90. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Biodiversity

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Voxel-based Morphometry and Minimum Redundancy Maximum Relevance Method for Classification of Parkinson's Disease and Controls from T1-Weighted MRI 16

Voxel-based Morphometry and Minimum Redundancy Maximum Relev...

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

作者： Rana, Bharti Juneja, Akanksha Saxena, Mohit Gudwani, Sunita Kumaran, S. Senthil Agrawal, R. K. Behari, Madhuri Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi India Case Western Reserve Univ Dept Neurol Case Med Ctr Cleveland OH 44106 USA All India Inst Med Sci Dept NMR Delhi India All India Inst Med Sci Dept Neurol Delhi India

ISBN: (纸本)9781450347532

Parkinson's disease (PD) is a neurodegenerative disorder, which needs to be accurately diagnosed in early stage. Voxel-based morphometry (VBM) has been extensively utilized to determine focal changes between PD patients and controls. However, it is not much utilized in differential diagnosis of an individual subject. thus, in this study, VBM findings in conjunction with minimum redundancy maximum relevance (mRMR) method are utilized to obtain a set of relevant and non-redundant features for computeraided diagnosis (CAD) of PD using T1-weighted MRI. In the proposed method, firstly, statistical features are extracted from the clusters obtained from statistical maps, generated using VBM, of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) independently and their different combinations. then mRMR, a multivariate feature selection method, is utilized to find a minimal set of relevant and non-redundant features. Finally, support vector machine is utilized to learn a decision model using the selected features. Experiments are performed on newly acquired T1-weighted MRI of 30 PD patients and 30 age & gender matched controls. the performance is evaluated using leave-one out cross-validation scheme in terms of sensitivity, specificity and classification accuracy. the maximum accuracy of 88.33% is achieved for GM+WM and GM+WM+CSF. In addition, the proposed method outperforms the existing methods. It is also observed that the selected clusters belong to regions namely middle and superior frontal gyrus for GM, inferior, middle frontal gyrus and insula for WM and lateral ventricle for CSF. Further, correlation of UPDRS/H&Y staging scale with GM/WM/CSF volume is observed to be not significant. Appreciable classification performance of the proposed method highlights the potential of the proposed method in CAD support system for the clinicians in PD diagnosis.

关键词： Parkinson's disease computer-aided diagnosis Voxel-based morphometry Minimum redundancy maximum relevance Magnetic resonance imaging

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