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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1468 条记录，以下是121-130 订阅

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MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scale Super-Resolution of Medical images 14

MoMSGAN: Mode Collapse based Degradation Agnostic Multi-Scal...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Dey, Samiran Chakraborti, Tapabrata Basuchowdhuri, Partha Mitra, Debasis Augustine, Robin Saha, Sanjoy Kumar Indian Association for the Cultivation of Science India The Alan Turing Institute University College London United Kingdom Indian Institute of Engineering Science and Technology India Uppsala University Sweden Jadavpur University India

ISBN: (纸本)9798400716256

Existing super-resolution (SR) models require separate training for different scales of SR because of the fixed upsampling levels in their architecture. We propose a novel one-time training approach for multi-scale SR models with a fixed upsampling unit, that can effectively super-resolve images to various scales. Another persisting problem relevant to medical image SR is that the models are biased to the level of degradation present in the low-resolution (LR) images and generate variations in anatomical structures and pathologies when applied to LR images corresponding to the same high-resolution (HR) image, varying only in degradation. But regardless of the degradation in the LR images, the anatomical structures and clinical findings in the SR images should remain consistent and unique to the patient's specific anatomy and health conditions. We thus propose a novel approach for GANs to blindly constrain the variations in super-resolved medical images. Further, we introduce an ensemble loss function to gain details and accurate textures, while enabling multi-scale SR and making the model unbiased towards degradation in LR images. Experimental results on lung CT scans demonstrate that our model, MoMSGAN, is an efficient one-time training approach for multi-scale SR and is also capable of reducing the degradation bias in reproducing accurate information in medical images, thus establishing state-of-the-art. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： computerized tomography

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Dual Stage Semantic Information Based Generative Adversarial Network For image Super-Resolution 14

Dual Stage Semantic Information Based Generative Adversarial...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sharma, Shailza Dhall, Abhinav Kumar, Vinay Singh, Vivek Thapar Institute of Engineering and Technology Punjab India IIT Ropar India Oxford Brookes University United Kingdom

ISBN: (纸本)9798400716256

Deep learning methods for the super-resolution problem are showing great performance compared to other traditional techniques. However, these methods are unable to learn complex spatial structures and high frequency details;which leads to over-smooth results. In the present paper, a novel Generative Adversarial Network based architecture named as Residue and Semantic feature based Dual Subpixel Generative Adversarial Network has been proposed for generator and discriminator networks to solve super-resolution problem. The generator network is residue and semantic feature based dual subpixel generative architecture. This architecture is divided into two stages: premier residual stage and deuxieme residual stage. These two stages are concatenated together to form a two stage upsamping process, which enhances the feature learning capability of our model. Inter and intra residual connections are made within these two stages;helping us to sustain the high texture details of images. Semantic based information is implanted in generator to enhance the quality of objects in an image. For embedding semantic information in generator, feature maps extracted from pre-trained model are merged with the input image. To stabilize the training process, we introduced spectral normalization in the discriminator. Visual perception and mean opinion score shows that proposed method outperforms the other state-of-the-art methods. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans 22

Efficient-PSP-Net: A Lightweight Model for the Segmentation ...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Chitimireddy Sindhura Harsha Vardhan Reddy Vengama Subrahmanyam Gorthi Electrical Engineering Indian Institute of Technology Tirupati IN Electrical Engineering Indian Institute of Technology - Tirupati IN

ISBN: (纸本)9781450398220

The segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-the-art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-tumorous regions, which would generally require extensive and complex deep learning architectures. The contributions of this paper are twofold; First, this paper proposes to encode information extracted at different scales using a “Pyramid Pooling Module” (PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight “Efficient-Net” as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-the-art architectures. The performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. The model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters.

关键词： CT.

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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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CDA-Net: computer vision based automated car damage analysis 14

CDA-Net: Computer vision based automated car damage analysis

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Kannan, Iyshwarya Ratthi Balasubramanian, Yogameena Saravana Permaal, S. Menaka, K. Ramesh, Sivasankari Thiagarajar College of Engineering Tamil Nadu Madurai India National Institute of Technical Teachers' Training and Research TamilNadu Chennai India Velammal Institute of Technology TamilNadu Chennai India

ISBN: (纸本)9798400716256

Car insurance claims are rising in tandem with the rising tide of car users. Every insurance claim requires an engineer's manual assessment and a surveyor's actual examination. This procedure can last anywhere from a few days to several weeks. Current deep-learning techniques have paved the way for this type of mechanization. Both the business and the client would benefit from a comprehensive system. To assess the damage cost for the insurance claim process, the make and model of the car, damaged parts, damage type, and severity of the damage are important parameters. We introduced two datasets, a piqued car make and model (CMM) dataset containing images of the most popular 23 car makes and 148 vehicle models available in the indian automotive market. The second dataset consists of 11,380 images collected from insurance offices and web resources of different types of car damage, including annotations. In addition, it provides an assessment of the damage to each part, the severity of the damage (dents, scratches, bent, broken, cracks, smashed, punched, and pushed), and the location of the damage (front, back, side). The assessment helps estimate the damage cost when combined with structured data. The proposed CDA_YOLOv5 car damage assessment framework outperformed existing state-of-the-art one-stage models with an average per-class accuracy of 87.36% and an overall accuracy of 90.45%. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Damage detection

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Detecting and tracking the position of suspicious objects using vision system

Detecting and tracking the position of suspicious objects us...

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conference on Artificial Intelligence for Security and Defence Applications

作者： Muthukrishnan, Ramkumar Kannan, Somasundar Prabhu, Radhakrishna Zhao, Yafan Bhowmick, Parijat Robert Gordon Univ Sch Engn Aberdeen Scotland Indian Inst Technol Elect & Elect Engn Dept Gauhati India

ISBN: (纸本)9781510667136;9781510667143

vision-based object tracking is crucial for both civil and military applications. A range of hazards to cyber safety, vital infrastructure, and public privacy are posed by the rise of drones, or unmanned aerial vehicles (UAV). As a result, identifying suspicious drones/UAV is a serious issue that has attracted attention recently. The key focus of this research is to develop a unique virtual coloured marker based tracking algorithm to recognise and predict the pose of a detected object within the camera field-of-view. After detecting the object, proposed method begins by determining the area of detected object as reference-contour. Following that, a Virtual-Bounding Box (V-BB) is developed over the reference-contour by meeting the minimum area of contour criteria. In order to track and estimate the precise location of the detected object in two-dimensions during observations, a Virtual Dynamic Crossline with a Virtual Static Graph (VDC-VSG) was constructed to follow the motion of V-BB, which is considered as a virtual coloured marker. Additionally, the virtual coloured marker helps to avoid issues linked to ambient lighting and chromatic variation. To some extent, it can function efficiently during obstructions like rapid position fluctuations, low resolution and noises etc. The efficacy of the developed algorithm is evaluated by testing with significant number of aerial sequences, including benchmark footage and the outputs were outstanding, with better results. The suggested method will support future industry of computer vision-based intelligent systems. Potential applications of the proposed method includes object detection and analysis applied to the field of security and defence.

关键词： image processing Cascade classifier Object detection Object tracking Aerial images

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A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense 22

A Globally-Connected and Trainable Hierarchical Fine-Attenti...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Wahengbam Kanan Kumar Manoj Paidimarri Arijit Sur CSE Indian Institute of Technology Guwahati IN

ISBN: (纸本)9781450398220

Deep Neural Network (DNN) inferences have been proven highly susceptible to carefully engineered adversarial perturbations, presenting a pivotal hindrance to real-world computer vision tasks. Most of the existing defenses have poor generalization ability due to their dependence on relatively limited Adversarial Examples (AE). Furthermore, the existing adversarial training necessitates continually retraining a target network with the sort of attack required to be repelled. The defense strategies that are primarily based on processing the perturbed image eventually fall short when pitted against constantly developing threats. Protection of DNN against adversarial attacks remains a difficult challenge on challenging datasets such as Fashion MNIST and CIFAR10. This paper proposes a GAN-based two-stage adversarial training model named Globally Connected and Trainable Hierarchical Fine Attention (GCTHFA). The first stage of the proposed GCTHFA GAN is to create a reconstructed image that is a purified version of an adversarial example. The proposed approach has used a trainable and globally linked attention map to teach the Generator about the different types of representations an image might have in different convolutional layers located at different levels in a network. The discriminator’s reliance on feature vectors produced by transfer learning eliminates the traditional dependency on standard image pixels. The second step involves adversarial training of a target classifier to provide resistance to such attacks. Extensive testing on the MNIST, Fashion MNIST, and CIFAR10 datasets with different classifiers and attacks show that the proposed model can handle adversarial attack settings for various target models. The proposed model uses only one type of adversarial training, with no requirement for retraining based on the type of attack.

关键词： Adversarial Perturbation Deep Learning Object Classification Generative Adversarial Network

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Spectrum-inspired Low-light image Translation for Saliency Detection 22

Spectrum-inspired Low-light Image Translation for Saliency D...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Kitty Varghese Sudarshan Rajagopalan Mohit Lamba Kaushik Mitra Indian institute of technology Madras India Madras Institute of Technology India Indian Institute of Technology Madras India IIT Madras India

ISBN: (纸本)9781450398220

Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pixel-level annotations, which is not only tedious and time-consuming but also infeasible if a huge training corpus is required. We propose a technique that performs classical band-pass filtering in the Fourier space to transform well-lit images to low-light images and use them as a proxy for real low-light images. Unlike popular deep learning approaches which require learning thousands of parameters and enormous amounts of training data, the proposed transformation is fast and simple and easy to extend to other tasks such as low-light depth estimation. Our experiments show that the state-of-the-art saliency detection and depth estimation networks trained on our proxy low-light images perform significantly better on real low-light images than networks trained using existing strategies.

关键词： Low-light and salient object detection

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Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification 14

Optimizing Knowledge Transfer in Sequential Models: Leveragi...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sharma, Richa Kumar, Santosh Shrivastava, Abhishek Bhatt, Tejasav Shri Shankaracharya Institute of Engineering and Technology Chhattisgarh Bhilai India IIIT-Naya Raipur Raipur India United College of Engineering and Research Uttar Pradesh Prayagraj India

ISBN: (纸本)9798400716256

Lung cancer is a prevalent and life-threatening disease characterized by abnormal cell growth in lung tissue. Early detection and accurate classification of lung cancer are crucial for timely treatment and improved patient outcomes. Our work proposes, a novel framework for accurate multi-class categorization of lung cancer using deep learning. The proposed framework uses a customized Densenet-201 model to leverage the knowledge of transfer learning, which acts as a parent architecture, enhanced by a residual structure as the child architecture. The performance of our model was examined by conducting experiments on the LCS25000 data set. The results demonstrate the outstanding accuracy of our proposed framework, achieving a remarkable accuracy of 95% on the test data set. This signifies the model's ability to accurately classify different types of lung cancer based on histopathology images. Our model also achieved remarkably well results in the TCGA lung cancer dataset, hence proving its generalization. These findings have important repercussions for improving pulmonary pathology diagnostic abilities and hold promise for enhancing patient care in the field of lung cancer diagnosis. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image classification

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Towards Realistic Underwater Dataset Generation and Color Restoration✱ 22

Towards Realistic Underwater Dataset Generation and Color Re...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Neham Jain Gopi Raju Matta Kaushik Mitra IIT Madras IN

ISBN: (纸本)9781450398220

Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. This can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to image translation network to bridge the gap between the synthetic and real domains by translating images from synthetic underwater domain to real underwater domain. Using this multimodal domain adaptation technique, we create a dataset that can capture a diverse array of underwater conditions. We then train a simple but effective CNN based network on our domain adapted dataset to perform color restoration. Code and pre-trained models can be accessed at https://***/nehamjain10/TRUDGCR

关键词： underwater image restoration domain adaptation

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