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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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A Parallel Architecture for Successive Elimination Block Matching Algorithm

A Parallel Architecture for Successive Elimination Block Mat...

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

作者： Kota Naga Srinivasarao Batta Indrajit Chakrabarti Dept. of Electron. & Electr. Commun. Eng. Indian Inst. of Technol. Kharagpur Department of Electronics and Electrical Communications Engineering Indian Institute of Technology Kharagpur India

This paper proposes a parallel architecture for a successive elimination algorithm (SEA), which is used in block matching motion estimation. SEA effectively eliminates the search points within the search window and thus decreases the number of matching evaluation instances that require very intensive computations compared to the standard full search algorithm (FSA). The proposed architecture for SEA decreases the time to calculate the motion vector by 57 percent compared to FSA. The performance while applying the SEA to several standard video clips has been shown to be same compared to the standard FSA. The proposed architecture uses 16 processing elements accompanied with use of intelligent data arrangement and memory configuration. A technique for reducing external memory accesses has also been developed. The proposed architecture for SEA provides an efficient solution for applications requiring real-time motion estimations. For, it serves to compute motion vectors in less amount of time while requiring almost same power and some increase in area compared to a similar architecture for implementing the full search algorithm. A register-transfer level implementation as well as simulation results on benchmark video clips are presented. Relevant design statistics on area and power for comparing between SEA and FSA implementations are also provided.

关键词： Parallel architectures Motion estimation computer architecture Video compression Hardware computer vision computer graphics image processing Underwater communication Paper technology

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Towards Realistic Underwater Dataset Generation and Color Restorationg± 13

Towards Realistic Underwater Dataset Generation and Color Re...

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

作者： Jain, Neham Matta, Gopi Raju Mitra, Kaushik IIT Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450398237

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 © 2022 ACM.

关键词： image reconstruction

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A tweak on k-nearest neighbor decision rule

A tweak on k-nearest neighbor decision rule

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2012 International conference on image processing, computer vision, and Pattern Recognition, IPCV 2012

作者： Basu, Tanmay Murthy, C.A. Chakrabarty, Himadri Machine Intelligence Unit Indian Statistical Institute 203 B. T. Road Kolkata-700108 India Department of Computer Scinece Surendranath College University of Calcutta Kolkata - 700009 India

ISBN: (纸本)9781601322258

The k-nearest neighbor (knn) decision rule puts a point into a particular class if the class has the maximum representation among the k nearest neighbors of the point in the training set. If the difference between the number of points belonging to the two competing classes (i.e., two classes with maximum representation) among the k nearest neighbors is at least one then the point will be assigned to that class. This article presents a tweak on the knn rule to enhance the confidence of the knn voting process. This method proposes a discrimination criterion on the majority voting of knn by a predefined threshold to enhance the rigidity of the voting process. The method does not require the knowledge of neighborhood parameter k to execute knn. The empirical studies show the utility of the proposed method using some synthetic and well known benchmark data sets. It is observed from the experiments that the accuracy of the proposed method is significantly better than the traditional knn method.

关键词： Pattern recognition

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QAAD: Quality Aware Adaptive Denoising 11

QAAD: Quality Aware Adaptive Denoising

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11th IEEE International conference on Signal processing and Integrated Networks, SPIN 2024

作者： Parse, Tejas Ajay Awasthi, Tanishq Yadav, Dushyant Joshi, Piyush Indian Institute of Information Technology Department of Computer Science Sri City Chittoor India

ISBN: (纸本)9798350308433

image denoising is a fundamental task in computer vision and image processing, crucial for improving the visual quality and interpretability of images captured in noisy environments. In this research, we propose a quality-Aware adaptive denoising (QAAD) approach that leverages the Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) as a key factor in determining the optimal denoising algorithm for an image. Our methodology involves an initial assessment of image quality using BRISQUE scores for a diverse set of images. Subsequently, we employ various image quality enhancement algorithms to enhance the image quality. Through extensive experimentation, we assess the performance of these enhancement algorithms with respect to specific ranges of BRISQUE scores. This evaluation enables us to determine which denoising algorithm performs optimally for images falling within particular BRISQUE score ranges. The innovation in our approach lies in its adaptability. When an image proceeds for denoising, its BRISQUE score is calculated and, based on this score, the most suitable denoising algorithm for that specific image is selected. This adaptive strategy ensures that the chosen denoising algorithm is tailored to the inherent noise characteristics of the input image, leading to superior denoising results. Our experimental results demonstrate the effectiveness of our approach in enhancing image quality and achieving state-of-The-Art denoising results. This research contributes to the field of image processing by providing a data-driven and adaptive solution for image denoising, ultimately enhancing the quality of images in diverse real-world applications. © 2024 IEEE.

关键词： image denoising

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image Denoising Using Matched Biorthogonal Wavelets

Image Denoising Using Matched Biorthogonal Wavelets

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

作者： Sanjeev Pragada Jayanthi Sivaswamy Center for Visual Information Technology International Institute of Information Technology Hyderabad India

Current denoising techniques use the classical ortho normal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. The use of available biorthogonal wavelets in image denoising is less common because of their poor performance. In this paper, we present a method to design image-matched biorthogonal wavelet bases and report on their potential for denoising. We have conducted experiments on various image datasets namely Natural, Satellite and Medical with the designed wavelets using two existing thresholding strategies. Test results from comparing the performance of matched and fixed biorthogonal wavelets show an average improvement of 35% in MSE for low SNR values (0 to 18 db) in every dataset. This improvement was also seen in the PSNR and visual comparisons. This points to the importance of matching when using wavelet-based denoising.

关键词： image denoising Noise reduction Signal design Additive white noise Information technology Design methodology image coding Gaussian noise Kernel Finite impulse response filter

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Improved low resolution heterogeneous face recognition using re-ranking 6th

Improved low resolution heterogeneous face recognition using...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

作者： Mudunuri, Sivaram Prasad Venkataramanan, Shashanka Biswas, Soma Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9789811300196

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. This can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Face recognition

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Multi-resolution Tracking in Space and Time

Multi-resolution Tracking in Space and Time

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

作者： Sumantra Dutta Roy Son Dinh Tran Larry Steven Davis B. Sreenivasa Vikram Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Computer Science University of Maryland College Park USA Bharti School of Telecom Tech & Management Indian Institute of Technology Delhi India

This paper proposes efficient and robust methods for tracking a moving object at multiple spatial and temporal resolution levels. The efficiency comes from optimising the amounts of spatial and temporal data processed. The robustness results from multi-level coarse-to-fine state-space searching. Tracking across resolution levels incurs an accuracy-versus-speed trade-off. For example, tracking at higher resolutions incurs greater processing cost, while maintaining higher accuracy in estimating the position of the moving object. We propose a novel spatial multi-scale tracker that tracks at the optimal accuracy-versus-speed operating point. Next, we relax this requirement to propose a multi-resolution tracker that operates at a minimum acceptable performance level. Finally, we extend these ideas to a multi-resolution spatio-temporal tracker. We show results of extensive experimentation in support of the proposed approaches.

关键词： Spatial resolution Bayesian methods Filtering computer vision Space technology computer science Educational institutions Robustness Costs Spatiotemporal phenomena

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Annotating Dance Posture images Using Multi Kernel Feature Combination

Annotating Dance Posture Images Using Multi Kernel Feature C...

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

作者： Ehtesham Hassan Santanu Chaudhury M. Gopal Department of Electrical Engineering Indian Institute of Technology Delhi India

We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct a cyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments have been performed on the image collection belonging to two indian classical dances (Bharatnatyam and Odissi). The annotation model has been tested using SIFT and the proposed feature individually and by optimally combining both the features. The experiments have shown promising results.

关键词： Kernel Vectors Support vector machines image representation Vocabulary image color analysis Feature extraction

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document image Binarization 14

TransDocUNet: A Transformer-based UNet Architecture for Degr...

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

作者： Biswas, Risab Sarkhel, Soumik Roy, Swalpa Kumar Pal, Umapada Optiks Innovations Pvt. Ltd. Maharashtra Mumbai India Alipurduar Government Engineering and Management College West Bengal India Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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Animation Geometry Compression Using the Linear Discriminant Analysis

Animation Geometry Compression Using the Linear Discriminant...

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

作者： A.R. Vamsidhar P.K. Bora Sanjib Das Department of Electronics and Communication Engineering Indian Institute of Technology Guwahati Guwahati India

We propose a new method to compress the geometry component of 3D animation sequence. It is based on the linear discriminant analysis (LDA) of the animation geometry data. The redundancy across the animation frames has been exploited by using the LDA in the temporal direction. Owing to the redundancy between the frames of a class, the covariance matrix of that class for the LDA computation may become singular. To overcome this drawback, we first transform the data into a new basis using the principal component analysis (PCA) and then apply the LDA on a few principal components. The reconstruction is simple and involves two stages: firstly for the LDA and then for the PCA. The experimental results show that the proposed method has the advantage of better reconstruction error at high compression ratios.

关键词： Animation Geometry Linear discriminant analysis Principal component analysis image coding image reconstruction Surface texture Rough surfaces Surface roughness Redundancy

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