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

共 1495 条记录，以下是131-140 订阅

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Insta Net: Recurrent Residual Network for Instagram Filter Removalg± 13

Insta Net: Recurrent Residual Network for Instagram Filter R...

引用

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

作者： Kansal, Priya Nathan, Sabari Couger Inc Shibuya Tokyo Japan

ISBN: (纸本)9781450398237

Social media filters cause various manipulations in the original image. While these manipulations make the images visibly pleasant, they can affect their authenticity and the performance of further image-processing tasks. Therefore, removing these social media filters is a crucial pre-processing step for the data pipeline of any image-processing task. In this paper, a recurrent residual UNet-based model is proposed to remove the filters. The recurrent residual block allows the units of each layer to evolve over discrete time steps so that the activity of each unit is modulated by the activities of its neighboring units. Because of this characteristic, the networks learn contextual information more efficiently and effectively. Assuming that a filter can be noise, the proposed model learns the feature representation of the filter, and then the original, unfiltered image is retrieved by taking the residuals of the input image and the filter representation. The experimental results show that the proposed model outperforms both supervised and unsupervised methods by a considerable margin in the IFFI dataset. © 2022 ACM.

关键词： image processing

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Enhanced Fetal Development Assessment via Contour Detection and CRL Estimation 3

Enhanced Fetal Development Assessment via Contour Detection ...

引用

3rd IEEE International conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Sriraam, Natarajan Chinta, Babu Suresh, Seshadhri Sudharshan, Suresh Ramaiah Institute of Technology Center for Medical Electronics and Computing Department of Medical Electronics Engineering Bangalore India Center for Medical Electronics and Computing Ramaiah Institute of Technology Bangalore India Mediscans Pvt Ltd Chennai India

ISBN: (纸本)9798350313703

Accurate fetal growth evaluation is critical for monitoring pregnancy health and delivering appropriate prenatal treatment. We provide a unique strategy for improving the precision of fetal growth assessment by merging sophisticated contour recognition algorithms with Crown-Rump Length (CRL) estimate in this work. We establish a more complete and reliable approach of measuring fetal growth and development by integrating cutting-edge computer vision and deep learning methods. Our technique uses powerful image processing methods to determine the shape of the fetal anatomy in ultrasound images, overcoming hurdles such as noise, artifacts, and fetal positional fluctuations. Following contour identification, exact CRL length estimate is performed, which is a critical predictor of gestational age and fetal well-being. The combination of these two components considerably improves accuracy of fetal growth assessment. © 2024 IEEE.

关键词： image enhancement

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

Advancing Fingerprint Recognition Quality Assessment: Introd...

引用

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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Advancing Fingerprint Recognition Quality Assessment: Introducing the FRBQ Metric for Enhanced Fingerprint Recognition 14

Advancing Fingerprint Recognition Quality Assessment: Introd...

引用

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

作者： Jaiswal, Prateek Koner, Arka Namboodiri, Anoop M. CVIT IIIT Hyderabad India India

ISBN: (纸本)9798400716256

关键词： image quality

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Effect of Particle image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentation✱ 22

Effect of Particle Image Velocimetry processing on CT Dicom ...

引用

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

作者： R Jenkin Suji W Wilfred Godfrey Dhar ABV Indian Institute of Information Technology and Management Gwalior IN Department of Computer Science and Engineering ABV Indian Institute of Information Technology and Management Gwalior IN Department of Applied Sciences ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9781450398220

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. The results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline.

关键词： CAD System

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A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism 23

A Novel Framework for Robust Fingerprint Representations usi...

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

作者： Alka Ranjan Nishkal Prakash Santhoshkumar Peddi Debasis Samanta Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

Fingerprint recognition systems are highly dependent on the quality and accuracy of the fingerprint representation. Traditional fingerprint recognition algorithms often employ handcrafted features designed manually based on domain knowledge and heuristics. However, these methods may struggle to capture the intricate patterns and minute details present in fingerprints, leading to limited discriminatory power and vulnerability to noise and variations in imaging conditions. This paper presents a novel approach for learning robust fingerprint representation by leveraging the power of deep convolution networks (DCN) along with attention mechanisms. The proposed method consists of a dual-module framework. The first module extracts local features, i.e., minutiae-based features, and the second module focuses on extracting global features, i.e., texture-based features. The extracted local and global features are concatenated into a single fixed-length feature vector. It aims to capture fine-grained details and complex patterns present in fingerprint images, thereby enhancing the accuracy and robustness of fingerprint recognition systems. Experimental results show that the proposed method outperforms state-of-the-art (SOTA) fingerprint recognition performances across a variety of benchmark datasets, containing fingerprints from various sensors and having different resolutions and sizes. It harnesses the finer features of fingerprints, as well as efficiently boosts the performance of the framework to progress SOTA.

关键词： Attention Mechanism Deep Convolutional Networks Fingerprint Embedding Fingerprint Matching. Fingerprint Recognition

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Degradation Aware Multi-Scale Approach to No Reference image Quality Assessment 14

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

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

作者： Roheda, Siddharth Unde, Amit Rashid, Loay Ameta, Abhishek Samsung Research Institute Bangalore 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. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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Measuring System for Elongation at Break of Cable Insulation Sheath Based on Machine vision 14

Measuring System for Elongation at Break of Cable Insulation...

引用

14th International conference on Digital image processing, ICDIP 2022

作者： Su, Xu Wang, Gangwei Zhang, Zhiqiang Yang, Jiale Zhang, Zhijia School of Artificial Intelligence Shenyang University of Technology China Land and Resources Exploration Center of Hebei Geological and Mineral Resources Exploration and Development Bureau China

ISBN: (纸本)9781510657564

In the production of power cables, the performance test of the cable insulation sheath is an important part. Compared with traditional testing methods, machine vision has the advantages of stable operation, high precision, and high efficiency. Because of this situation, firstly, based on machine vision theory, the structure of the old-fashioned tensile machine was reconstructed, and the whole tensile test process of the cable insulation sheath test was imaged by a CMOS camera, and the color recognition algorithm, effective area segmentation algorithm, and workpiece were proposed. The fracture judgment detection algorithm and the corrosion difference algorithm are used to calculate the distance between the marked lines and then calculate the elongation at the break of the cable material. Through systematic experiments on the same batch of cable jackets, the deviation of the elongation at break measured by visual inspection is the largest, no more than 1%. The experimental results and practical applications show that the machine vision-based visual inspection system has higher accuracy, faster efficiency, and more stable and reliable operation than the traditional inspection system. © 2022 SPIE.

关键词： computer vision

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Variational Augmentation for Enhancing Historical Document image Binarizationg± 13

Variational Augmentation for Enhancing Historical Document I...

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

作者： Dey, Avirup Nasipuri, Prof. Mita Das, Nibaran Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9781450398237

Historical Document image Binarization is a well-known seg-mentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework-The first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-The-Art methods. © 2022 ACM.

关键词： image enhancement

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Attention-guided Feature Fusion Network for Crowd Counting 14

Attention-guided Feature Fusion Network for Crowd Counting

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14th International conference on Digital image processing, ICDIP 2022

作者： He, Qing Yang, Qianqian Xia, Yinfeng Peng, Sifan Yin, Baoqun Department of Automation University of Science and Technology of China No.443 Huangshan Road Anhui Hefei230000 China

ISBN: (纸本)9781510657564

How to solve the scale variation and background interference faced by crowd counting algorithms in practical applications is still an open problem. In this paper, to tackle the above problems, we propose the Attention-guided Feature Fusion Network (AFFNet) to learn the mapping between the crowd image and density map. In this network, the Channel-attentive Receptive Field Block (CRFB) is constructed by parallel convolutional layers with different expansion rates to extract multi-scale features. By adopting attention masks generated by high-level features to adjust low-level features, the Feature Fusion Module (FFM) can alleviate the background interference problem at the feature level. In addition, the Double Branch Module (DBM) generates a density estimation map, which further erases the background interference problem at the density level. Extensive experiments conducted on several challenging benchmark datasets including ShanghaiTech, UCF-QNRF and JHU-CROWD++ demonstrate our proposed method is superior to the state-of-the-art approaches. © 2022 SPIE.

关键词： computer vision

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