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An Algorithm to Calculate Retinal Vessel Diameter in Fundus images 14

An Algorithm to Calculate Retinal Vessel Diameter in Fundus ...

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

作者： Yoga Sri Varshan, V. Sahayam, Subin Jayaraman, Umarani Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India

ISBN: (纸本)9798400716256

Retinal blood vessels are the arteries and veins that supply blood to the human eye. Fundus images obtained through a fundus camera capture retinal information like, the macula, optic disc, cup, fovea, retinal blood vessels, and abnormalities. The retinal blood vessels are miniature and are usually measured in micrometers. It is difficult and time-consuming to study retinal vessels from a fundus image. Generally, a detailed vessel study requires costly equipment like Ocular Coherence Tomography (OCT) and is comparatively less available at hospitals than a fundus camera. Blood vessels are connected throughout the body. Studying retinal blood vessel health can help ophthalmologists and doctors understand the overall health of blood vessels across the body non-invasively. Narrowing of blood vessels can make a person prone to many diseases, like hypertension, cardiovascular disease, and stroke. A few methods aim to automate the task using computer Aided Diagnosis (CAD). The work proposes a novel and relatively less complex algorithm for calculating the diameter of a given retinal blood vessel at a given distance from the optic disc center in a fundus image. The approach aims to compute the artery-to-vein diameter ratio of retinal blood vessels at a given distance from the center of the optic disc. The proposed method has been tested on the LES-AV 2020 dataset and it achieved an approximate artery-to-vein diameter ratio (AVR) of 2:3, which is in line with the average healthy AVR in the literature. Therefore, the proposed method is accurate and can be extended to find AVR in various clinical scenarios to detect and study any abnormalities. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blood vessels

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Semantic Map Injected GAN Training for image-to-image Translation 12th

Semantic Map Injected GAN Training for Image-to-Image Transl...

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

作者： Kshatriya, Balaram Singh Dubey, Shiv Ram Sarma, Himangshu Chaudhary, Kunal Gurjar, Meva Ram Rai, Rahul Manchanda, Sunny Computer Vision Group Indian Institute of Information Technology Sri City Chittoor India Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India Bangalore India

ISBN: (纸本)9789811941351

image-to-image translation is the recent trend to transform images from one domain to another domain using a generative adversarial network (GAN). The existing GAN models perform the training by only utilizing the input and output modalities of transformation. In this paper, we perform the semantic injected training of GAN models. Specifically, we train with original input and output modalities and inject a few epochs of training for translation from input to semantic map. Let us refer to the original training as the training for the translation of the input image into the target domain. The injection of semantic training in the original training improves the generalization capability of the trained GAN model. Moreover, it also preserves the categorical information in a better way in the generated image. The semantic map is only utilized at the training time and is not required at the test time. The experiments are performed using state-of-the-art GAN models over CityScapes and RGB-NIR stereo datasets. We observe the improved performance in terms of the SSIM, FID, and KID scores after injecting semantic training as compared to the original training. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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

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

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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 ...

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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 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

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. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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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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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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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Single View Homography Estimation for an Inclined Textured Planar Surface: Overcoming the Inverse and Ill-Posed Challenge! 23

Single View Homography Estimation for an Inclined Textured P...

引用

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

作者： Pooja Kumari Sukhendu Das IIT Madras IN Indian Institute of Technology Madras IN

ISBN: (纸本)9798400716256

Homography estimation is a crucial step in many computer vision problems involving the planar transformation of an image from one view to another. Generally, from two image views of a scene with a planar patch, one can compute the homography matrix (H) using Direct Linear Transformation (DLT) or its variants. In this study, we work on the inverse scenario to solve an ill-posed problem, where given only a single image (Math 1) of an inclined textured planar surface, we attempt to estimate both H and the orthogonal view (Math 2) of the planar surface. We propose to solve this problem of homography Estimation using an optimization framework. To the best of our knowledge, there has barely been any work done on this inverse scenario of homography estimation from only a single image view. The research presented in this paper successfully achieves image rectification and homography computation by assuming a perspective transformation on an upright planar patch when viewed by a camera placed exactly in front of it. A cost function with suitable constraints is utilized to refine homography estimation. Experimental results reveal the efficiency of the proposed approach in the case of images of checkerboard and other textured planar surfaces (real-world images with lines and other prominent textures).

关键词： DLT Homography LM image perspective optimization texture

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ViLP: Knowledge Exploration using vision, Language, and Pose Embeddings for Video Action Recognition 23

ViLP: Knowledge Exploration using Vision, Language, and Pose...

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

作者： Soumyabrata Chaudhuri Saumik Bhattacharya IIT Bhubaneswar IN E&ECE Indian Institute of Technology Kharagpur IN

ISBN: (纸本)9798400716256

Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promising results in this domain. In literature, 2D skeleton or pose modality has often been used for this task, either independently or in conjunction with the visual information (RGB modality) present in videos. However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks. In this paper, we present the first pose augmented vision-language model (VLM) for VAR. Notably, our scheme achieves an accuracy of 92.81% and 73.02% on two popular human video action recognition benchmark datasets, UCF-101 and HMDB-51, respectively, even without any video data pre-training, and an accuracy of 96.11% and 75.75% after kinetics pre-training.

关键词： Action recognition vision-language model multimodal training

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