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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"

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An Adaptive Clustering Based Non-linear Filter for the Restoration of Impulse Corrupted Digital images

An Adaptive Clustering Based Non-linear Filter for the Resto...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： S. Saudia Justin Varghese Krishnan Nallaperumal Santhosh P Mathew S. Allwin S.K. Thakur Centre for Information Technology & Engineering Manonmaniam Sundaranar University Tirunelveli Tamil Nadu India Department of Computer Science & Engineering Infant Jesus College of Engineering and Technology Thoothukudi Tamil Nadu India Department of Computer Science & Engineering SAINTGITS College of Engineering Kottayam Kerala India Naval Research Board New Delhi India

The paper proposes a 2D impulse filtering algorithm for the restoration of salt & pepper impulse corrupted digital images. The algorithm incorporates a hard-C means clustering stage to facilitate pixel classification in the impulse detection phase and an efficient adaptive filtering scheme in the restoration phase. The impulse detection scheme of the algorithm avoids mis-classification of signals as impulses by clearly distinguishing the high frequency image details from impulse corrupted pixels. The adaptive restoration phase identifies the most suitable signal restorer from among the true signals of a reliable neighborhood. Experimental results in terms of subjective assessment and objective metrics favor the proposed algorithm at all impulse noise levels over many top-ranking filters.

关键词： Adaptive filters Digital filters image restoration Digital images Signal restoration Filtering algorithms Clustering algorithms Signal processing Phase detection Frequency

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EEG2image: image Reconstruction from EEG Brain Signals 48

EEG2IMAGE: Image Reconstruction from EEG Brain Signals

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Singh, Prajwal Pandey, Pankaj Miyapuram, Krishna Raman, Shanmuganathan Indian Institute of Technology Cvig Lab Gandhinagar India Indian Institute of Technology Brain Lab Gandhinagar India

ISBN: (纸本)9781728163277

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualize certain classes of Objects and English characters. We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128 × 128 images using a small number of images. Further, we conduct ablation studies and experiments to show the effectiveness of our proposed framework over other state-of-the-art methods using the small EEG dataset. © 2023 IEEE.

关键词： Electroencephalography

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Subjective Performance of Texture Based Algorithm for Face Verification: The Role of Databases

Subjective Performance of Texture Based Algorithm for Face V...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： R. Raghavendra Ashok Rao G. Hemantha Kumar Department of studies in Computer Science University of Mysore Mysore India Dept of Computer Science J.S.S. College of Arts Commerce and Science Mysore India

This paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. The proposed method consists of three parts. The first part is a Log-Gabor filtering on facial image. The second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. The third part is transforming the set of features using subspace methods such as principal component analysis (PCA) and independent component analysis (ICA). Thus, in this paper two methods namely Log-Gabor mixture model (LGMM) based on PCA and Log-Gabor mixture model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. The nature of type & size of databases chosen on the performance of these algorithms are also studied. The experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms.

关键词： Gabor filters image databases Principal component analysis Independent component analysis Face recognition Frequency computer science Feature extraction Kernel Transfer functions

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Visibility Analysis for New View Synthesis from images

Visibility Analysis for New View Synthesis from Images

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Bishwajit Sharma Subrata Rakshit Center for Artificial Intelligence and Robotics DRDO Complex Bangalore India

This work introduces a new analysis procedure to measure availability of information to render a new view, given a set of views from a camera in linear motion, or a linear array of cameras. The given set can be interpolated to generate a new view using a crossed-slit camera model. The analysis consists of finding regions in the neighborhood of the linear camera positions, where a full view, partial view, or no view can be generated. A visibility parameter, V, is introduced to quantitatively measure the amount of information that can be interpolated. The present work differs from previous methods which aim at minimizing the number of input viewpoints while ensuring full coverage of a specific object. Our work analyzes the visibility for a set of views captured by a linear camera array, and can be extended to a set of images from a 2D camera mesh. The result of this analysis, the visibility, V, is computed and verified with respect to the interpolated views. Visibility analysis helps in identifying blind areas while generating videos for a new camera trajectory. It is also useful in planning best scene capturing strategy for the purpose of generating new views, and finds application in virtual walkthroughs, special effects, the free viewpoint television, remote education, etc.

关键词： image analysis Cameras image motion analysis Information analysis Motion measurement Availability Rendering (computer graphics) Motion analysis Videos Strategic planning

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Quaternion Factorized Simulated Exposure Fusion 13

Quaternion Factorized Simulated Exposure Fusion

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

作者： Saini, Saurabh Narayanan, P.J. CVIT IIIT-Hyderabad India

ISBN: (纸本)9781450398237

image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a properly illuminated image. Given a single input image, exposure fusion can still be employed on a gsimulated' exposure stack, leading to direct single image contrast and low-light enhancement. In this work, we present a novel gQuaternion Factorized Simulated Exposure Fusion' (QFSEF) method by factorizing an input image into multiple illumination consistent layers. To this end, we use an iterative sparse matrix factorization scheme by representing the image as a two-dimensional pure quaternion matrix. Theoretically, our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components. We empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low-light image enhancement task. Furthermore, we provide three exposure fusion strategies which can be used with our simulated stack and provide a comprehensive performance analysis. Finally, in order to validate our claims, we show extensive qualitative and quantitative comparisons against relevant state-of-the-art solutions on multiple standard datasets along with relevant ablation analysis to support our proposition. Our code and data are publicly available for easy reproducibility and reference. 1 © 2022 ACM.

关键词： image enhancement

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A parallel progressive refinement image rendering algorithm on a scalable multithreaded VLSI processor array

A parallel progressive refinement image rendering algorithm ...

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1993 International conference on Parallel processing, ICPP 1993

作者： Nandy, S.K. Narayan, Ranjani Visvanathan, V. Sadayappan, P. Chauhan, Prashant S. Supercomputer Education and Research Center Indian Institute of Science Bangalore560012 India Ohio State Univ. ColumbusOH United States C-DOT Bangalore560052 India

ISBN: (纸本)0849389836

In this paper we develop a multithreaded VLSI processor linear array architecture to render complex environments based on the radiosity approach. The processing elements are identical and multithreaded. They work in Single Program Multiple Data (SPMD) mode. A new algorithm to do the radiosity computations based on the progressive refinement approach[2] is proposed. Simulation results indicate that the architecture is latency tolerant and scalable. It is shown that a linear array of 128 uni-threaded processing elements sustains a throughput close to 0.4 million patches/sec. © 1993 IEEE.

关键词： Rendering (computer graphics)

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Parametric Video Compression Using Epitome Based Texture Synthesis

Parametric Video Compression Using Epitome Based Texture Syn...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shruti Bansal Santanu Chahudhury Brejesh Lall Department of Electrical Engineering IIT Delhi India Indian Institute of Technology Guwahati Guwahati Assam IN

We present a video compression scheme using epitome based texture coding that uses a low quality video as side information and improves it by using the epitome. The side information is sent at different levels of quantization and resolution to optimize the quality against the bit rate. The concept of motion threading is used for propagation of epitome information from one frame to another. The proposed scheme shows promising results for low bit rate side information case because of the reduction in the level of blurring due to the introduction of high frequency information derived from the epitome.

关键词： image coding Decoding conferences image reconstruction Redundancy Bit rate Encoding

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indian Regional Sign Language Recognition 14

Indian Regional Sign Language Recognition

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

作者： Sujatha, C. Patil, Madhura K. Umadevi, F.M. Jain, Neha Badni, Akshata Angadi, Ranjeeta Gangishetty, Shankar KLE Technological University Hubli India IIIT Hyderabad India

ISBN: (纸本)9798400716256

Sign Language is a medium of communication in the Deaf and Hard of Hearing community (DHH community). According to WHO, there are approximately 63 million people in India, including 3.3 million from the state of Karnataka, with 0.3 million children belonging to DHH community. The sign language gestures vary globally, making communication difficult between the DHH community and others. Hence, Sign Language Recognition has gained significant attention in recent years. There is a limited publicly available dataset on indian Sign Language. In addition, the dataset for regional languages especially the Kannada dataset, is unavailable for the sign recognition tasks. Hence initially, in our work, we built a dataset of indian Regional Kannada Sign Language (IRKSL) for different levels of data at character, word, and sentence levels. We propose a method for recognizing the indian sign language and translating it into Kannada regional language. We demonstrate the proposed method on the IRKSL dataset and obtained an accuracy of 81.69% and 73.87% with and without feature augmentation, respectively, on the IRKSL dataset. We also evaluated our work on the INCLUDE 50 dataset and obtained a better accuracy of 93.42% as compared to the state-of-art method. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Audition

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Estimation of the orientation and distance of a mirror from Kinect depth data

Estimation of the orientation and distance of a mirror from ...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Tanwi Mallick Partha Pratim Das Arun Kumar Majumdar Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Delhi New Delhi Delhi IN

In many common applications of Microsoft Kinect™ including navigation, surveillance, 3D reconstruction, and the like; it is required to estimate the geometry of mirrors or other reflecting surfaces existing in the field of view. This often is difficult as in most positions a mirror does not support diffuse reflection of speckles and hence cannot be seen in the Kinect depth map. A mirror shows up as unknown depth. However, suitably placed objects reflecting in the mirror can provide important clues for the orientation and distance of the mirror. In this paper we present a method using a ball and its mirror image to set-up point-to-point correspondence between object and image points to solve for the geometry of the mirror. With this simple estimators are designed for the orientation and distance of a plane vertical mirror with respect to the Kinect camera. In addition an estimator is presented for the diameter of the ball. The estimators are validated through a set of experiments.

关键词： Mirrors Geometry Cameras Estimation Spatial resolution Three-dimensional displays Noise

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C-SAW: Self-Supervised Prompt Learning for image Generalization in Remote Sensing 23

C-SAW: Self-Supervised Prompt Learning for Image Generalizat...

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

作者： Avigyan Bhattacharya Mainak Singha Ankit Jha Biplab Banerjee Indian Institute of Technology Bombay IN

ISBN: (纸本)9798400716256

We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive zero-shot generalization performance, their effectiveness is limited when dealing with diverse domains during training and testing. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts, which results in a drop in performance while dealing with such multi-domain data. To address these challenges, we propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features. We observe that CLIP’s vision encoder struggles to identify contextual image information, particularly when image patches are jumbled up. This issue is especially severe in optical remote sensing images, where land-cover classes exhibit well-defined contextual appearances. To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features. We keep the CLIP backbone frozen and introduce a small set of projectors for both the CLIP encoders to train C-SAW contrastively. Experimental results demonstrate the superiority of C-SAW across multiple remote sensing benchmarks and different generalization tasks.

关键词： vision-language models

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