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检索条件"任意字段=15th Indian Conference on Computer Vision Graphics and Image Processing"

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On Improved CS-SS image Watermark Detection Over Radio Mobile Channel 16

On Improved CS-SS Image Watermark Detection Over Radio Mobil...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bose, Anirban Maity, Santi P. IIEST Dept Informat Technol Sibpur 711103 Howrah India

ISBN: (纸本)9781450347532

Recently compressed sensing or compressive sampling (CS), apart from its intrinsic applications of sub-sample signal reconstruction, is explored a lot in the design of bandwidth preserving-energy efficient wireless networks. At the same time, due to open nature of wireless channel, digital data (media) transmission needs their protection from unauthorized access and digital watermarking has been devised as one form of potential solution over the years. Among the various methods, spread spectrum (SS) watermarking is found to be efficient due to its improved robustness and imperceptibility. SS watermarking on digital images in presence of additive and multiplicative noise is studied a lot. To the best of knowledge, CS-SS watermarking in presence of both multiplicative (fading channel) and additive noise is not explored much in the existing literature. To address this problem, a wireless communication theoretic model is suggested here to develop an improved detection scheme on additive SS image watermark framework. System model considers sub sample (CS) transmission of the watermarked image over both non-fading and fading channel. then a diversity assisted weighted combining scheme for the improved watermark detection is developed. An optimization problem is formulated where the weight for the individual link is calculated through eigen filter approach to maximize the watermark detection probability for a fixed false alarm rate under the constraint of an embedding power (strength). A large set of simulation results validate the mathematical model of the diversity assisted compressive watermark detector.

关键词： Spread spectrum watermarking compressive transmission

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Color Restoration in Turbid Medium 16

Color Restoration in Turbid Medium

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Nimisha, T. M. Seemakurthy, Karthik Rajagopalan, A. N. Vedachalam, Narayanaswamy Raju, Ramesh IIT Madras Madras Tamil Nadu India Natl Inst Ocean Technol Madras Tamil Nadu India

ISBN: (纸本)9781450347532

Light scattering and color distortions are two major issues with underwater imaging. Scattering occurs due to turbidity of the medium and color distortions are caused by differential attenuation of wavelengths as a function of depth. As a result, underwater images taken in a turbid medium have low contrast, color cast, and color loss. the main objective of this work is color restoration of underwater images i.e, produce its equivalent image as seen outside of the water surface. As a first step, we account for low contrast by employing dark channel prior based dehazing. these images are then color corrected by learning a mapping function between a pair of color chart images, one taken inside water and another taken outside. the mapping thus learned is with respect to a reference distance from the water surface. We also propose a color modulation scheme that is applied prior to color mapping to accommodate the same mapping function for different depths as well. Color restoration results are given on several images to validate the efficacy of the proposed methodology.

关键词： Color mapping color modulation white balancing boosting dehazing

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A Rapid Non-Linear Diffusion Compressed Sensing parallel MR image Reconstruction 11

A Rapid Non-Linear Diffusion Compressed Sensing parallel MR ...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Joy, Ajin Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Trivandrum Kerala India

ISBN: (纸本)9781450366151

Optimization of the tradeoff between computation time and image quality is essential for reconstructing high-quality magnetic resonance image (MRI) from a limited number of acquired samples in a short time using compressed sensing (CS) algorithms. In this paper, we achieve this for the edge preserving non-linear diffusion reconstruction (NLDR) which eliminates the critical step-size tuning of the total variation (TV) based CS-MRI. Based on optimization of contrast parameter that controls noise and signal in sensitivity modulated channel images, we propose an a-switching NLDR technique for a faster approximation of reconstruction image without affecting the image quality. Proposed algorithm exploits the difference in the extent of undersampling artifacts in signal-background regions of the channel images to arrive at different estimates of contrast parameter, leading to an effective optimization of speed and quality. While maintaining better image quality as compared to conventional TV reconstruction, the switched NLDR also achieves 25-35% gain in convergence time over NLDR without switching. this makes the switched NLDR a better candidate for fast reconstruction over traditional TV and NLDR approaches. In the detailed numerical experiments, we have compared and optimized the tradeoff for various state-of-the-art choices of contrast parameter.

关键词： parallel MRI nonlinear diffusion contrast parameter

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Extended Multi-spectral Face Recognition Across Two Different Age Groups: An Empirical Study 16

Extended Multi-spectral Face Recognition Across Two Differen...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Vetrekar, N. T. Raghavendra, R. Gaonkar, A. A. Naik, G. M. Gad, R. S. Goa Univ Taleigao Goa India NTNU Trondheim Norway

ISBN: (纸本)9781450347532

Face recognition has attained a greater importance in biometric authentication due to its non-intrusive property of identifying individuals at varying stand-off distance. Face recognition based on multi-spectral imaging has recently gained prime importance due to its ability to capture spatial and spectral information across the spectrum. Our first contribution in this paper is to use extended multi-spectral face recognition in two different age groups. the second contribution is to show empirically the performance of face recognition for two age groups. thus, in this paper, we developed a multi-spectral imaging sensor to capture facial database for two different age groups (<= 15years and >= 20years) at nine different spectral bands covering 530nm to 1000nm range. We then collected a new facial images corresponding to two different age groups comprises of 168 individuals. Extensive experimental evaluation is performed independently on two different age group databases using four different state-of-the-art face recognition algorithms. We evaluate the verification and identification rate across individual spectral bands and fused spectral band for two age groups. the obtained evaluation results shows higher recognition rate for age groups >= 20years than <= 15years, which indicates the variation in face recognition across the different age groups.

关键词： Face Recognition Facial Age Groups Multi-spectral Imaging Feature Extraction Collaborative Representation

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image Co-segmentation using Graph Convolution Neural Network 11

Image Co-segmentation using Graph Convolution Neural Network

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Banerjee, Sayan Hati, Avik Chaudhuri, Subhasis Velmurugan, Rajbabu Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

image co-segmentation is jointly segmenting two or more images sharing common foreground objects. In this paper, we propose a novel graph convolution neural network (graph CNN) based end-to-end model for performing co-segmentation. At the beginning, each input image is over-segmented into a set of superpixels. Next, a weighted graph is formed using the over-segmented images exploiting spatial adjacency and both intra-image and inter-image feature similarities among the image superpixels (nodes). Subsequently, the proposed network, consisting of graph convolution layers followed by node classification layers, classifies each superpixel either into the common foreground or its complement. During training, along with the co-segmentation network, an additional network is introduced to exploit the corresponding semantic labels, and the two networks share the same weights in graph convolution layers. the whole model is learned in an end-to-end fashion using a novel cost function comprised of a superpixel wise binary cross entropy and a multi-label cross entropy. We also use empirical class probabilities in the loss function to deal with class imbalance. Experimental results reflect that the proposed technique is very competitive with the state-of-the-art methods on two challenging datasets, Internet and Pascal-VOC.

关键词： Co-segmentation graph superpixel graph CNN convolution filter

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Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks 24

Revised Regularization for Efficient Continual Learning thro...

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15th indian conference on computer vision graphics and image processing

作者： Palit, Sanchar Banerjee, Biplab Chaudhuri, Subhasis Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9798400710759

We propose a Bayesian neural network-based continual learning algorithm using Variational Inference, aiming to overcome several drawbacks of existing methods. Specifically, in continual learning scenarios, storing network parameters at each step to retain knowledge poses challenges. this is compounded by the crucial need to mitigate catastrophic forgetting, particularly given the limited access to past datasets, which complicates maintaining correspondence between network parameters and datasets across all sessions. Current methods using Variational Inference with KL divergence risk catastrophic forgetting during uncertain node updates and coupled disruptions in certain nodes. To address these challenges, we propose the following strategies. To reduce the storage of the dense layer parameters, we propose a parameter distribution learning method that significantly reduces the storage requirements. In the continual learning framework employing variational inference, our study introduces a regularization term that specifically targets the dynamics and population of the mean and variance of the parameters. this term aims to retain the benefits of KL divergence while addressing related challenges. To ensure proper correspondence between network parameters and the data, our method introduces an importance-weighted Evidence Lower Bound term to capture data and parameter correlations. this enables storage of common and distinctive parameter hyperspace bases. the proposed method partitions the parameter space into common and distinctive subspaces, with conditions for effective backward and forward knowledge transfer, elucidating the network-parameter dataset correspondence. the experimental results demonstrate the effectiveness of our method across diverse datasets and various combinations of sequential datasets, yielding superior performance compared to existing approaches.

关键词： Continual Learning Variational Inference Evidence Lower Bound Bayesian neural network

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Exploiting image processing locality in cache pre-fetching 5

Exploiting image processing locality in cache pre-fetching

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5th International conference on High Performance Computing

作者： Cucchiara, R Piccardi, M Univ Ferrara Dipartimento Ingn I-44100 Ferrara Italy

ISBN: (纸本)0818691948

Emerging trends in computer design attempt to include specific solutions for handling images also in general-purpose computers, because of the current spread of multimedia, image processing and computer graphics applications. In this context, this paper proposes hardware pre-fetching techniques specific for caching images: the main issue we state is that most algorithms working opt images exhibit a 2D spatial locality that is not taken into account in current cache organization and data access strategies. To this aim we propose an adaptive local pre-fetching for the image data type;this technique, mirroring the two-dimensional spatial locality of image processing algorithms, results to be more efficient than other approaches, such as sequential pre-fetching and adaptive pre-fetching. Performance is evaluated on different classes of image processing algorithms, namely raster-scan and propagative algorithms, common in computer vision and multimedia applications.

关键词： General purpose computers

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Multi-Scale Transformer-CNN Network for Brain Tumor Segmentation and Survival Prediction 24

Multi-Scale Transformer-CNN Network for Brain Tumor Segmenta...

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15th indian conference on computer vision graphics and image processing

作者： Mazumdar, Indrajit Mukhopadhyay, Jayanta Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9798400710759

Accurately segmenting brain tumors and predicting survival is crucial for diagnostic and personalized treatment plans. Recently, convolutional neural networks (CNNs) based on U-Net architecture have been extensively used to segment brain tumors. It is essential to capture both local and global dependencies to segment brain tumors accurately. therefore, several studies have considered U-Net variants by combining a CNN and a Transformer. However, the skip connections in these Transformer-based U-Net variants do not incorporate the features from multiple scales available at the encoder. To address this issue, we propose a new 3D U-Net variant called Multi-Scale Transformer-CNN Network (MTC-Net) that incorporates a Multi-Scale Transformer Convolution (MTC) block into the skip connections. the MTC block extracts local multi-scale features from the CNN encoder blocks and global features using Swin Transformer blocks to handle the wide variability in tumor size and enhance the feature representation capability. the segmentation mask predicted by the proposed MTC-Net is subsequently used to predict a patient's overall survival time. Most studies on overall survival prediction have used handcrafted radiomic features that lack the ability to fully model complex tumor patterns. In contrast, deep features are specifically adapted to brain tumors. We observed that instead of using only a CNN, combining it with a Transformer produces deep features that provide more accurate predictions. For this purpose, we extract deep features using our Transformer-based network MTC-Net to construct a regression model for predicting overall survival. Comprehensive experimentation on the BraTS 2020 and 2021 benchmark datasets proved the efficacy of the proposed components. MTC-Net outperformed the CNN- and Transformer-based state-of-the-art segmentation networks. Moreover, our approach outperformed the state-of-the-art survival prediction systems.

关键词： Brain tumor segmentation Deep learning CNN Transformer Survival prediction Machine learning

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Reduction of Variance of Observations on Pelvic Structures in CBCT images using Novel Mean-Shift and Mutual Information based image Registration 16

Reduction of Variance of Observations on Pelvic Structures i...

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Malladi, Sai Phani Kumar Veduruparthi, Bijju Kranthi Mukherjee, Jayanta Das, Partha Pratim Chakrabarti, Saswat Mallick, Indranil Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur GS Sanyal Sch Telecommun Kharagpur W Bengal India Tata Med Ctr Dept Radiat Oncol Kolkata India

ISBN: (纸本)9781450347532

In this paper, Cone-Beam Computed Tomography(CBCT) image data of colorectal cancer patients are considered for registering standard reference locations of bony structures in pelvic region. A solution is provided in this paper to automatically compute and resolve irregularities involved in locating bony structures in the pelvic region. A new algorithm is proposed to automatically locate the lowest 3D coordinates of the Pubic Symphysis (pb) and the Coccyx on a daily basis. the irregularities involved are reduced to minimum by registering CBCT images. the conventional three dimensional mutual information (MI) based registration and a novel mean shift based mutual information techniques are compared. the variations in the position of pelvic region are also compared for unregistered and registered CBCT images. the proposed algorithm, tested on CBCT image data of 25 patients, each taken over a span of 27 days consecutively, provide promising results. the variations in the locations of coccyx, pb, and the distance between them were found to be reduced due to registration of 3D CBCT images.

关键词： Cone-Beam Computed Tomography 3D Registration Mutual Information Pelvic structure

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A Framework to assess Sun Salutation videos 16

A Framework to assess Sun Salutation videos

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10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Jain, Hiteshi Harit, Gaurav Indian Inst Technol Jodhpur Dept Comp Sci & Engn Jodhpur 342011 Rajasthan India

ISBN: (纸本)9781450347532

there are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. the metric of grace and consistency are then laid down in terms of posture transition times. the assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency.

关键词： Sun Salutation HMM STIP grace consistency

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