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

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Zernike Moment and Mutual Information Based Methods for Multimodal image Registration 3rd

Zernike Moment and Mutual Information Based Methods for Mult...

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3rd International conference on computer vision and image processing (CVIP)

作者： Kashyap, Suraj Kumar Jat, Dinesh Bhuyan, M. K. Vishwakarma, Amit Gadde, Prathik Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India Indiana Univ Purdue Univ Sch Informat & Comp 535 West Michigan St Indianapolis IN 46202 USA

ISBN: (纸本)9789813292918;9789813292901

image registration enables joint operations between images obtained from diverse sources. However, there have been limited advances in the registration of multichannel images. The accuracy of registration is a significant concern for medical applications, among others. Two methods, PCA-ZM and CED-ZM, have been proposed for registration based on Zernike moment and enhanced mutual information. Edge detection by Zernike moment and identification of common features in multichannel images are used as a foundation to improve accuracy over single-channel registrations. Single-channel registration accuracy for MRI and SPECT brain images is found to surpass the methods compared against. PCA-ZM demonstrates good accuracy for MR-MR registration, while CED-ZM has good accuracy for MR-SPECT registration. These measures improve upon accurate registration for images, especially where many modalities are available, such as in medical diagnosis.

关键词： Multimodal Multichannel MRI SPECT Medical image registration Zernike moment Enhanced mutual information Principal component analysis Canny edge detector Gradient information Edge overlap

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A Quantitative Comparison of the Role of Parameter Selection for Regularization in GRAPPA-Based Autocalibrating Parallel MRI 3rd

A Quantitative Comparison of the Role of Parameter Selection...

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3rd International conference on computer vision and image processing (CVIP)

作者： Mathew, Raji Susan Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Med Image Comp & Signal Proc Lab Trivandrum Kerala India

ISBN: (纸本)9789813290884;9789813290877

The suitability of regularized reconstruction in autocalibrating parallel magnetic resonance imaging (MRI) is quantitatively analyzed based on the choice of the regularization parameter. In this study, L-curve and generalized cross-validation (GCV) are adopted for parameter selection. The results show that: (1) Presence of well-defined L-corner does not guarantee an artifact-free reconstruction, (2) Sharp L-corners are not always observed in GRAPPA calibration, (3) Parameter values based on L-curves always exceed those based on GCV, and (4) Use of a predetermined number of filters based on the local signal power can result in a compromise between noise and artifacts as well as better visual perception. It is concluded that appropriate use of regularized solutions facilitates minimization of noise build-up in the reconstruction process, without enhancing the effects of aliasing artifacts.

关键词： L-curve GCV g-factor GRAPPA Regularization

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Efficient Sparse to Dense Stereo Matching Technique 3rd

Efficient Sparse to Dense Stereo Matching Technique

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3rd International conference on computer vision and image processing (CVIP)

作者： Bhandari, Piyush Wu, Meiqing Aslam, Nazia Lam, Siew-Kei Kolekar, Maheshkumar Indian Inst Technol Patna Bihar India Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9789813290884;9789813290877

Acquiring accurate dense depth maps with low computational complexity is crucial for real-time applications that require 3D reconstruction. The current sensors capable of generating dense maps are expensive and bulky, while compact low-cost sensors can only generate the sparse map measurements reliably. To overcome this predicament, we propose an efficient stereo analysis algorithm that constructs a dense disparity map from the sparse measurements. Our approach generates a dense disparity map with low computational complexity using local methods. The algorithm has much less computation time than the existing dense stereo matching techniques and has a high visual accuracy. Experiments results performed on KITTI and Middlebury datasets show that our algorithm has much less running time while providing accurate disparity maps.

关键词： Depth estimation Sparse to dense Stereo matching Interpolation

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Integrated Semi-Supervised Model for Learning and Classification 3rd

Integrated Semi-Supervised Model for Learning and Classifica...

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3rd International conference on computer vision and image processing (CVIP)

作者： Bhalla, Vandna Chaudhury, Santanu Indian Inst Technol Delhi Delhi India CEERI PILANI Pilani Rajasthan India

ISBN: (纸本)9789813290884;9789813290877

Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.

关键词： Semi-supervised Self-training Clonal data Integrated

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Joint Bit Allocation for 3D Video with Nonlinear Depth Distortion-An SSIM-Based Approach 3rd

Joint Bit Allocation for 3D Video with Nonlinear Depth Disto...

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3rd International conference on computer vision and image processing (CVIP)

作者： Harshalatha, Y. Biswas, Prabir Kumar Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9789813292918;9789813292901

Perceptual quality improvement approach for 3D video through bit allocation is presented in this paper. Bit allocation between texture video and depth map plays an important role in deciding quality of synthesized views at the decoder end. To have better visual quality, structural similarity (SSIM) index is used as a distortion metric in rate distortion optimization (RDO) of the 3D video. In this paper, we used the nonlinear relationship of depth distortion with synthesis distortion in computing rate distortion cost resulting in bettermode decision. Using the same depthmapRDO in bit allocation algorithm, more accurate results are obtained when compared to the linear relation of depth distortion with synthesis distortion.

关键词： 3D video Perceptual quality Bit allocation

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A Modified FCM-Based Brain Lesion Segmentation Scheme for Medical images 3rd

A Modified FCM-Based Brain Lesion Segmentation Scheme for Me...

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3rd International conference on computer vision and image processing (CVIP)

作者： Gautam, Anjali Sadhya, Debanjan Raman, Balasubramanian Indian Inst Technol Roorkee Roorkee 247667 Uttarakhand India

ISBN: (纸本)9789813292918;9789813292901

Segmentation of brain lesion from medical images is a critical problem in the present day. In this work, we have proposed a new distance metric for fuzzy clustering based classification of different brain regions via acquiring accurate lesion structures. The modified distance metric segments the images into different regions by calculating the distances between the cluster centers and object elements, and subsequently classify them via fuzzy clustering. The proposed method can effectively remove noise from the images, which results in a better homogeneous classification of the image. Our method can also accurately segment stroke lesion where the results are near to the ground truth of the stroke lesion. The performance of our method is evaluated on both magnetic resonance images (MRI) and computed tomography (CT) images of brain. The obtained results indicate that our method performs better than the standard fuzzy c-means (FCM), spatial FCM (SFCM), kernelized FCM methods (KFCM), and adaptively regularized kernel-based FCM (ARKFCM) schemes.

关键词： Brain stroke Hemorrhage Segmentation Fuzzy c-means (FCM) Metric

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Comparison of Reconstruction Methods for Multi-compartmental Model in Diffusion Tensor Imaging 3rd

Comparison of Reconstruction Methods for Multi-compartmental...

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3rd International conference on computer vision and image processing (CVIP)

作者： Shakya, Snehlata Kumar, Sanjeev Indian Inst Technol Roorkee Roorkee Uttarakhand India

ISBN: (纸本)9789813292918;9789813292901

Diffusion tensor imaging (DTI) is one of the magnetic resonance techniques to describe the anisotropic diffusion in terms of its orientation. DTI gives the direction of white matter fibers in a single direction. However, multi-fiber heterogeneity can be present at several places of the human brain. Recently, a multi-compartmental model (which uses noncentral Wishart distributions) was proposed to improve the state of the art of solving this multi-fiber heterogeneity. In this model, nonnegative least square (NNLS) method was used for solving the inverse problem which is based on L-2 norm minimization. In this paper, results are obtained with the least absolute shrinkage and selection operator (L-1 regularization). In particular, we study the performance of NNLS and nonnegative lasso methods and shown that the later method outperforms for several cases.

关键词： Crossing fibres DTI Lasso Least absolute shrinkage MRI

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6th International Symposium on Signal processing and Intelligent Recognition Systems, SIRS 2020

6th International Symposium on Signal Processing and Intelli...

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6th International Symposium on Advances in Signal processing and Intelligent Recognition Systems, SIRS 2020

ISBN: (纸本)9789811604249

The proceedings contain 27 papers. The special focus in this conference is on Signal processing and Intelligent Recognition Systems. The topics include: Improved Long-Short Term Memory U-Net for image Segmentation;Extraction of Parcel Boundary from UAV images Using Deep Learning Techniques;Grad-CAM-Based Classification of Chest X-Ray images of Pneumonia Patients;periocular Recognition Under Unconstrained image Capture Distances;acoustic Prediction of Elephants for Localization and Movement Tracking Using Sensors and Distance Metrics;community Detection Algorithms in Complex Networks: A Survey;a Systematic Review on the Influence of User Personality in Rumor and Misinformation Propagation Through Social Networks;HRIDAI: A Tale of Two Categories of ECGs;channel-Aware Decision Fusion with Rao Test for Multisensor Fusion;detection of Breast Cancer from Mammogram images Using Deep Transfer Learning;Comprehending the Dynamics of EEG Generated Under Various Odorant Stimulation on the Brain;Robust Beamforming Against DOA Mismatch with Null Widening for Moving Interferences;6G Ultra-Low Latency Communication in Future Mobile XR Applications;applying Neural Style Transfer to Spectrograms of Environmental Audio;identification of indian English by Speakers of Multiple Native Languages;Robust and Imperceptible Digital image Watermarking Based on DWT-DCT-Schur;comparative Study of Maturation Profiles of Neural Cells in Different Species with the Help of computer vision and Deep Learning;deep Learning Algorithms to Detect and Localize Acute Intracranial Hemorrhages;Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity;a Leak Detection in Water Pipelines Using Discrete Wavelet Decomposition and Artificial Neural Network;supervised Feature Learning for Music Recommendation.

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Remote Multimodal Biometric Authentication using Visual Cryptography 3rd

Remote Multimodal Biometric Authentication using Visual Cryp...

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3rd International conference on computer vision and image processing (CVIP)

作者： Kaur, Harkeerat Khanna, Pritee PDPM Indian Inst Informat Technol Design & Mfg Jabalpur Madhya Pradesh India

ISBN: (纸本)9789813292918;9789813292901

This work proposes an architecture for multimodal biometric recognition systems where user, recognition system, and template database are remotely located over a network. As the number of biometrics are limited and once lost they are compromised forever, it becomes imperative to design systems that optimize recognition rates and also address security and privacy issues for biometric-enabled authentication schemes. The proposed architecture provides revocability to multimodal biometric templates and secures their storage and transmission over a remote network with the help of visual cryptography technique. The proposed architecture gives a good matching performance and also fulfills four template protection criteria, i.e., security, diversity, revocability, and performance. Various attack scenarios such as phishing, replay, database, man-in-middle, and attack via record multiplicity are also addressed.

关键词： Multimodal biometric security Remote authentication Revocability Visual cryptography

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Indoor-Outdoor Scene Classification with Residual Convolutional Neural Network 3rd

Indoor-Outdoor Scene Classification with Residual Convolutio...

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3rd International conference on computer vision and image processing (CVIP)

作者： Kumari, Seema Jha, Ranjeet Ranjan Bhavsar, Arnav Nigam, Aditya IIT Mandi MAS Lab Sch Comp & Elect Engn Kamand India

ISBN: (纸本)9789813292918;9789813292901

In this paper, we demonstrate the effectiveness of a customized ResNet to address the problem of indoor-outdoor scene classification both for color images as well as depth images. Such an approach can serve as an initial step in a scene classification/retrieval pipeline or a single-image depth estimation task. The classification framework is developed based on Residual Convolutional Neural Network (ResNet-18) to classify any random scene as indoor or outdoor. We also demonstrate the invariance of the classification performance with respect to different weather conditions of outdoor scenes (which one can commonly encounter). The performance of our classification strategy is analyzed on different varieties of publicly available datasets of indoor and outdoor scenes that also have corresponding depth maps. The suggested approach achieves almost an ideal performance in many scenarios, for both color and depth images, across datasets. We also show positive comparisons with other state-of-the-art methods.

关键词： Scene classification RCNN Scene analysis Depth information image retrieval

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