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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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Document image Segmentation as a Spectral Partitioning Problem

Document Image Segmentation as a Spectral Partitioning Probl...

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6th indian conference on computer vision, graphics and image processing

作者： Dasigi, Praveen Jain, Raman Jawahar, C. V. IIIT Hyderabad Ctr Visual Informat Technol Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9781424442195

State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. these solutions need to be adapted frequently and sometimes fail to perform well for complex scripts. this calls for a generalized solution that achieves a one shot segmentation that is globally optimal. this paper describes one such solution based on the optimization problem of spectral partitioning which makes the decision of proper segmentation based on the Spectral properties of the pairwise similarity matrix. the solution described in the paper is shown to be general, global and closed form. the claims have been demonstrated on 142 page images from a Telugu book, in a script set in both poetry and prose layouts. this particular class of scripts has been proved to be challenging for the existing state of the art algorithms, where the proposed solution achieves significant results.

关键词： Iterative methods

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Assisting the Autistic with improved Facial Expression Recognition from mixed expressions

Assisting the Autistic with improved Facial Expression Recog...

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4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Ahmed, Washef Chanda, Kunal Mitra, Soma Mazumdar, Debasis Ctr Dev Adv Comp Adv Signal Proc Grp Kolkata India

ISBN: (纸本)9781479915880

People suffering from autism have difficulty with recognizing other people's emotions and are therefore unable to react to it. Although there have been attempts aimed at developing a system for analyzing facial expressions for persons suffering from autism, very little has been explored for capturing one or more expressions from mixed expressions which are a mixture of two closely related expressions. this is essential for psychotherapeutic tool for analysis during counseling. this paper presents the idea of improving the recognition accuracy of one or more of the six prototypic expressions namely happiness, surprise, fear, disgust, sadness and anger from the mixture of two facial expressions. For this purpose a motion gradient based optical flow for muscle movement is computed between frames of a given video sequence. the computed optical flow is further used to generate feature vector as the signature of six basic prototypic expressions. Decision Tree generated rule base is used for clustering the feature vectors obtained in the video sequence and the result of clustering is used for recognition of expressions. the relative intensity of expressions for a given face present in a frame is measured. With the introduction of Component Based Analysis which is basically computing the feature vectors on the proposed regions of interest on a face, considerable improvement has been noticed regarding recognition of one or more expressions. the results have been validated against human judgement.

关键词： Human computer Interaction Facial Expression Analysis mixed expressions Component Based Analysis quantification of score human psycho-visual judgement social interactions autism

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GraphiCon 2005 - International conference on computer graphics and vision, Proceedings

GraphiCon 2005 - International Conference on Computer Graphi...

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15th International conference on computer graphics and vision, GraphiCon 2005

the proceedings contain 78 papers. the topics discussed include: thematic processing of the earth remote sensing multispectral data;detection and localization of differences on the images of printed circuit boards;efficient application of optical objects in light simulation software;robin hood's algorithm for time-critical level of detail;AMLESAC: a new maximum likelihood robust estimator;ImagiCAD: experimental image based modeling system;ImagiCAD: experimental image based modeling system;adaptive multiresolution filter banks for image and audio processing;prospects of LGS: application and development;reconstruction of shapes based on normals Analysis;crest line extration from point clouds;real-time visualization of large vector HP-GL based maps;and deforming rheological objects using an adaptive mesh.

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A Fast Method for Very Low Bit-Rate Coding of Hyperspectral images 11

A Fast Method for Very Low Bit-Rate Coding of Hyperspectral ...

引用

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

作者： Kumar, Saurabh Chaudhuri, Subhasis Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781450366151

In this work, we propose a computationally efficient compressive sensing based approach for very low bit rate lossy coding of hyperspectral (HS) image data by exploiting the redundancy inherent in this imaging modality. We divide the HS datacube into subsets of adjacent bands, each of which is encoded into a coded snapshot using a random code matrix. these coded snapshot images are encoded using the wavelet-based SPIHT compression technique. the decompression from the coded snapshots at the receiver is done using the orthogonal matching pursuit with the help of an overcomplete dictionary learned on a general purpose training dataset. We provide ample experimental results and performance comparisons to substantiate the usefulness of the proposed method. In the proposed technique the encoder is free from any decoder and it offers a significant saving in computation and yet yields a much higher compression quality.

关键词： image coding

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How Much Can A Gaussian Smoother Denoise? 16

How Much Can A Gaussian Smoother Denoise?

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

作者： Gubbi, Sagar Venkatesh Gupta, Ashutosh Seelamantula, Chandra Sekhar Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India Indian Space Res Org Ahmadabad 380058 Gujarat India Indian Inst Sci Dept Elect Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

Recently, a suite of increasingly sophisticated methods have been developed to suppress additive noise from images. Most of these methods take advantage of sparsity of the underlying signal in a specific transform domain to achieve good visual or quantitative results. these methods apply relatively complex statistical modelling techniques to bifurcate the noise from the signal. In this paper, we demonstrate that a spatially adaptive Gaussian smoother could be a very effective solution to the image denoising problem. To derive the optimal parameter estimates for the Gaussian smoothening kernel, we derive and deploy a surrogate of the mean squared error (MSE) risk similar to the Stein's estimator for Gaussian distributed noise. However, unlike the Stein's estimator or its counterparts for other noise distributions, the proposed generic risk estimator (GenRE) uses only first- and second-order moments of the noise distribution and is agnostic to the exact form of the noise distribution. By locally adapting the parameters of the Gaussian smoother, we obtain a denoising function that has a denoising performance (quantified by the peak signal-to-noise ratio (PSNR)) that is competitive to far more sophisticated methods reported in the literature. To avail the parallelism offered by the proposed method, we also provide a graphics processing unit (GPU) based implementation.

关键词： image denoising Gaussian smoothing Stein's lemma generic risk estimation spatially varying smoothers graphics processing unit (GPU)

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Multi-slice Tomographic Reconstruction: To Couple or Not to Couple 16

Multi-slice Tomographic Reconstruction: To Couple or Not to ...

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

作者： Gopal, Preeti Chandran, Sharat Svalbe, Imants Rajwade, Ajit Indian Inst Technol Dept Comp ViGIL Bombay Maharashtra India IITB Monash Res Acad Bombay Maharashtra India Monash Univ Dept Phys & Astron Clayton Vic Australia

ISBN: (纸本)9781450347532

Recent work in tomography focuses on algorithms that enable faster and more accurate reconstruction from as few measurements as possible. We review the advantage of jointly reconstructing multiple slices and show that joint reconstruction may suffer in the presence of adjacent dissimilar slices. this gives rise to the need to detect similarity or dissimilarity of unknown images before performing joint reconstruction. We propose a method to detect 'similar' slices directly from their tomographic measurements and juxtapose these similar slices. Since the images themselves are not available by definition, we compute similarity between slices based on image moments;these in turn are estimated in a novel way from Radon projection moments. A segmented least squares algorithm is then designed to couple only similar slices. Our results confirm the benefit of this method for tomographic reconstruction.

关键词： Compressive sensing Radon projection moments image moments tomographic reconstruction

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Contextual Self-Attention Based UNet Architecture for Fluid Segmentation in Retinal OCT B-scans 24

Contextual Self-Attention Based UNet Architecture for Fluid ...

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15th indian conference on computer vision graphics and image processing

作者： Kalita, Himashree Dandapat, Samarendra Bora, Prabin Kumar Indian Inst Technol Guwahati Elect & Elect Engn North Guwahati Assam India

ISBN: (纸本)9798400710759

Retinal edema caused by fluid buildups has links to vision threatening retinal diseases. Presently, spectral-domain optical coherence tomography (SD-OCT) is the imaging method of choice for evaluating retinal health and tracking treatment progress. Automated methods for fluid segmentation in retinal OCT B-scans are critical for ocular disease diagnosis. the segmentation task in retinal OCT B-scans with pathological manifestations is challenging as the retinal layered structure varies significantly with the severity of the underlying disease condition. In this paper, we present a Contextual Self-Attention based U-Net (CoSAUNet) architecture for fluid segmentation in pathological OCT B-scans. the proposed model replaces the basic convolutional layers in the standard U-Net architecture with contextual self-attention layers. the contextual self-attention mechanism integrates contextual information mining among neighboring keys and conventional self-attention into a unified architecture to learn the long-range feature dependencies. the robustness of the proposed CoSAUNet model is evaluated through a 3-fold cross-validation setup on the RETOUCH and the AROI datasets, yielding average Dice scores (DS) of 88.16% and 76.67%, respectively. Furthermore, the CoSAUNet performs comparably to the state-of-the-art methods while using only half of the parameters.

关键词： Optical coherence tomography retinal fluid segmentation deep learning semantic segmentation self-attention mechanism

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Deep image Inpainting with Region Prediction at Hierarchical Scales 16

Deep Image Inpainting with Region Prediction at Hierarchical...

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

作者： Chakraborty, Souradeep Kundu, Jogendra Nath Babu, R. Venkatesh Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781450347532

In this paper, we propose a CNN based method for image inpainting, which utilizes the inpaintings generated at different hierarchical resolutions. Firstly, we begin with the prediction of the missing image region with larger contextual information at the lowest resolution using deconv layers. Next, we refine the predicted region at greater hierarchical scales by imposing gradually reduced contextual information surrounding the predicted region by training different CNNs. thus, our method not only utilizes information from different hierarchical resolutions but also intelligently leverages the context information at different hierarchy to produce better inpainted image. the individual models are trained jointly, using loss functions placed at intermediate layers. Finally, the CNN generated image region is sharpened using the unsharp masking operation, followed by intensity matching with the contextual region, to produce visually consistent and appealing inpaintings with more prominent edges. Comparison of our method with well-known inpainting methods, on the Caltech 101 objects dataset, demonstrates the quantitative and qualitative strengths of our method over the others.

关键词： image inpainting context hierarchical scales image sharpening intensity matching

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A Fast Identity-Independent Expression Recognition System for Robust Cartoonification using Smart Devices 16

A Fast Identity-Independent Expression Recognition System fo...

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

作者： Agarwal, Gorisha Garg, Ronak Garg, Divya Prasad, Bikash Dutta, Tanima Gupta, Hari Prabhat Indian Inst Technol BHU Varanasi Varanasi Uttar Pradesh India

ISBN: (纸本)9781450347532

Facial expressions convey rich information about emotions, intentions and other internal states of a person. Automatic facial expression and cartoonification systems are aiming towards the application of computer vision systems in human computer interaction, emotion analysis, medical care, virtual learning and even entertainment. In this paper, we propose an identity-independent robust system to detect human expression and generate their corresponding cartoonified images in real-time using smart-devices. Identity-independent expression recognition system enhances the facial features of query face image using its intra-class variation image and classifies using support vector machines. the method is robust to variation in identity and illumination of the query face image. Along with the basic expressions, like angry, happy and sad, we have also successfully detected the emotional states of sleepy and pain. the experimental results on JAFFE, CK+, PICS, Yalefaces, and Senthil databases show the effectiveness of the system.

关键词： Emotion recognition facial expressions Cartoonification Human computer interaction Augumented reality

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Pre- and Post-processing Strategies for Generic Slice-wise Segmentation of Tomographic 3D Datasets Utilizing U-Net Deep Learning Models Trained for Specific Diagnostic Domains 15

Pre- and Post-processing Strategies for Generic Slice-wise S...

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15th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP) / 15th International conference on computer vision theory and Applications (VISAPP)

作者： Zwettler, Gerald A. Backfrieder, Werner Holmes, David R., III Mayo Clin Dept Physiol & Biomed Engn Biomed Analyt & Computat Engn Lab Coll Med Rochester MN 55905 USA Univ Appl Sci Upper Austria Dept Software Engn Res Grp Adv Informat Syst & Technol AIST Softwarepk 11 A-4232 Hagenberg Austria Univ Appl Sci Upper Austria Dept Software Engn Med Informat Softwarepk 11 A-4232 Hagenberg Austria

ISBN: (纸本)9789897584022

An automated and generally applicable method for segmentation is still in focus of medical image processing research. Since a few years artificial inteligence methods show promising results, especially with widely available scalable Deep Learning libraries. In this work, a five layer hybrid U-net is developed for slice-by-slice segmentation of liver data sets. Training data is taken from the Medical Segmentation Decathlon database, providing 131 fully segmented volumes. A slice-oriented segmentation model is implemented utilizing deep learning algorithms with adaptions for variable parenchyma shape along the stacking direction and similarities between adjacent slices. Both are transformed for coronal and sagittal views. the implementation is on a GPU rack with TensorFlow and Keras. For a quantitative measure of segmentation accuracy, standardized volume and surface metrics are used. Results DSC=97.59, JI=95.29 and NSD=99.37 show proper segmentation comparable to 3D U-Nets and other state of the art. the development of a 2D-slice oriented segmentation is justified by short training time and less complexity and therefore massively reduced memory consumption. this work manifests the high potential of AI methods for general use in medical segmentation as fully- or semi-automated tool supervised by the expert user.

关键词： Deep Learning U-Net Model-based Segmentation in Medicine Computed Tomography

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