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

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Degradation Aware Multi-Scale Approach to No Reference image Quality Assessment 23

Degradation Aware Multi-Scale Approach to No Reference Image...

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

作者： Siddharth Roheda Amit Satish Unde Loay Rashid Abhishek Ameta Samsung Research India

ISBN: (纸本)9798400716256

With the advent of smartphones and social media, images have become a popular medium for sharing information. As a result understanding the perceived quality of images has gained importance. In the recent years No-Reference image Quality Assessment (NR-IQA) has gained prominence due to the abundance of User Generated Content (UGC) being consumed. Applications ranging from photo capture assistance, suggestion of best images for sharing, creation of stories from user media gallery, and enhancement of images for best user experience require NR-IQA. This paper presents a novel Degradation Aware Multi-Scale NR-IQA technique that leverages the multi-scale nature of feature extraction in the Human Visual System (HVS) and also aims at understanding the perceived quality of images across various distortions. This is achieved via learning of two auxiliary tasks: (i) Identification of distortions present in images; (ii) Learning to rank images across distortions. Additionally, a unique data generation strategy is introduced to generate realistic distortions for learning cross-distortion ranking of image quality. The proposed approach achieves State-Of-The-Art (SOTA) performance on multiple IQA datasets and demonstrates generalization in cross-dataset testing. The results showcase the efficacy of the proposed method in identifying and ranking the quality of images across various distortions, making it a promising approach for NR-IQA in real-world applications.

关键词： Degradation Aware image Quality Assessment Multi-Scale No-Reference

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Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography images 22

Detecting Coronavirus (COVID -19) Disease Cues from Chest Ra...

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

作者： Athira Vinod Shilna Sreeni K G Anurenjan Purushothaman Jeena R S Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum India Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum Indochina Computer Vision Lab Dept. of Electronics and Communication CET India Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum India

ISBN: (纸本)9781450398220

This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature.

关键词： Residual connections Network in Network COVID-19 Computed Tomography

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Split and Knit: 3D Fingerprint Capture with a Single Camera 13

Split and Knit: 3D Fingerprint Capture with a Single Camera

引用

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

作者： Srivastava, Apoorva Namboodiri, Anoop Biometrics and Secure ID Lab CVIT IIIT Hyderabad India

ISBN: (纸本)9781450398237

3D fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, which is common in contact-based sensors, in addition to capturing depth information. Unfortunately, its adoption is limited due to high cost and system complexity. Photometric stereo provides an opportunity to build low-cost, simple sensors capable of high-quality 3D capture. However, it assumes that the surface being imaged is lambertian (unlike our fingers). We introduce the Split and Knit algorithm (SnK), a 3D reconstruction pipeline based on the photometric stereo for finger surfaces. It introduces an efficient way of estimating the direct illumination component, thus allowing us to do a higher-quality reconstruction of the entire finger surface. The algorithm also introduces a novel method to obtain the overall finger shape under NIR illumination, all using a single camera. Finally, we combine the overall finger shape and the ridge-valley point cloud to obtain a 3D finger phalange. The high-quality 3D reconstruction also results in better matching accuracy of the captured fingerprints. 1 © 2022 ACM.

关键词： image reconstruction

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image panoptic segmentation network based on Efficientnet 24

Image panoptic segmentation network based on Efficientnet

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proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Peng Wang Dongjie Zhou School of Mathematics and Physics Wuhan Institute of Technology Wuhan Hubei China

ISBN: (纸本)9798400713880

The complete perception and segmentation of images has become a research hotspot in the field of computer vision with the continuous development of the computer vision industry. This image segmentation technique needs the understanding of a scene which can neither figure out individual instances or different actors in the environment as well as the general background semantics. This newly emerging panopic segmentation problem in recent years is solved in this paper with a lightweight and efficient panoptic segmentation network, LEPSNet, which is based on Efficientnet. We designed this architecture based on a Two way PAN, with the ability of efficiently encoding and fusing semantically rich multi-scale features as a backbone network. A new semantic segmentation head is designed to capture fine context information in the background. Proposed is an improved separable convolutional module, and applied to the whole network. This module also results in adding it to Mask R-CNN, making the instance segmentation head. Experiments have demonstrated that the architecture can achieve a level of performance leadership while keeping the model lightweight. In particular, we achieved 65.0 PQ on Cityscapes and 50.5 PQ on indian Driving Dataset (IDD).

关键词： instance segmentation

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A New Method for Measuring Leveling Heights Using computer vision Techniques

A New Method for Measuring Leveling Heights Using Computer V...

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2020 International conference on computer, Control, Electrical, and Electronics Engineering, ICCCEEE 2020

作者： Aldalil, Mohammed University of Khartoum Department of Surveying Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

The levels and leveling techniques have developed over the last decades. The development of level instruments started with the appearance of precise levels, they are precise instruments used for precise leveling. The operation of digital levels is based on the digital processing of video indications of a coded staff. A new method for measuring leveling heights using computer vision techniques is proposed in this paper. The methodology implementation comprises capturing an image of a metric staff using a digital camera, then the height is computed by processing the image using a developed algorithm. The algorithm consists of two major steps. In the first step, the implemented software computes the meter and tenth of meter height values through recognizing numbers in the image. Then, it computes the rest of the decimal part digits from the coordinate difference between the image's middle row (reading line) and E shape's (or its mirror image) end row. The proposed algorithm works well for the tested precisions (1, 0.1, and 0.01milimeters), also it can measure the heights from a distance up to more than 100m. The testing results indicate that the measurement algorithm is workable and can be used as a low-cost alternative to the barcoded staff digital levels. © 2021 IEEE.

关键词： computer vision

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Dense or Sparse: Crowd Counting with Binary Supervision 13

Dense or Sparse: Crowd Counting with Binary Supervision

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

作者： Babu Sam, Deepak Joseph, Jimmy Agarwalla, Abhinav Radhakrishnan, Venkatesh Babu Indian Institute of Science Bangalore India

ISBN: (纸本)9781450398237

Dense crowd counting is one of the challenging problems where creating large labeled datasets turns out to be difficult. Typical crowd images have thousands of people positioned close to each other and annotating the locations of every person is tedious. Add to these the growing need to include crowds from as many diverse scenarios as possible for better generalization. In this context, labeling every head for various settings under consideration is not scalable and directly affects the performance of deep models on account of limited data. We mitigate this issue with a new binary labeling scheme. Every image is simply labeled to either dense or sparse crowd category, instead of annotating every single person in the scene. This leads to dramatic reduction in the amount of annotations required and becomes proportional to the number of images rather than the crowd count. For training counting models, we create noisy density maps directly from the edge density of the images, which are then improved through rectifier networks. There are separate rectifier networks for dense and sparse categories, trained in an unsupervised fashion. The proposed counting model is composed of a self-supervised backbone feature network and a regressor head. The ground truth density maps are generated using the binary labels and the rectifier networks for training the regressor. Experiments show that the proposed architecture achieves competitive performance than existing models at an extremely low annotation cost. © 2022 ACM.

关键词： image enhancement

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Masked Student Dataset of Expressions 13

Masked Student Dataset of Expressions

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

作者： Sola, Sridhar Gera, Darshan Sri Sathya Sai Institute of Higher Learning Karnataka Bengaluru India

ISBN: (纸本)9781450398237

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://***/SridharSola/MSD-E. © 2022 ACM.

关键词： computer vision

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Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification 23

Optimizing Knowledge Transfer in Sequential Models: Leveragi...

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

作者： Richa Sharma Santosh Kumar Abhishek Shrivastava Tejasv Bhatt Computer Science and Engineering SSTC Bhilai IN Computer Science and Engineering International Institute of Information Technology (IIIT)-Naya Raipur IN Computer Science and Engineering United College of Engineering & Research (UCER) Prayajraj Uttar Pradesh IN

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.

关键词： Deep-learning

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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 23

Advancing Fingerprint Recognition Quality Assessment: Introd...

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

作者： Prateek Jaiswal Arka Koner Anoop Namboodiri CVIT International Institute of information technology Hyderabad IN UIDAI IN CVIT IIIT Hyderabad IN

ISBN: (纸本)9798400716256

In the field of biometric security, the quality assessment of fingerprint images is paramount for boosting the accuracy of fingerprint recognition systems. These systems are fundamental for the secure and efficient authentication and identification of individuals. Our research presents FRBQ (Fingerprint Recognition-Based Quality), an innovative quality metric designed to navigate the limitations of the NFIQ2 model. FRBQ exploits deep learning algorithms in a weakly supervised setting and utilizes matching scores from DeepPrint, a Fixed-Length Fingerprint Representation Model. Each score is paired with labels indicating the robustness of fingerprint image matches. However, in a fully referenced setting, these labels can be subjective, lacking a clear definition of what "image quality" inherently means. This weakly labeled approach strives to capture diverse perspectives on image quality, potentially making it a more encompassing metric. In comparison to NFIQ2, our research showcases the superior performance of the FRBQ model. It not only correlates better with recognition scores but also effectively evaluates challenging images that NFIQ2 struggles with. Validated by the esteemed FVC 2004 dataset, FRBQ proves its efficacy in fingerprint image quality assessment. This study underscores the transformative potential of AI in biometrics, emphasizing its capability to capture details that traditional methods might overlook. Our work stresses the critical role of precise quality assessment in the evolution of fingerprint recognition systems.

关键词： Fingerprint image Quality Fingerprint Recognition System image Quality Assessment Weakly Supervised Learning

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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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