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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

共 2011 条记录，以下是571-580 订阅

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2010 6th Iranian conference on Machine vision and image processing, MVIP 2010

2010 6th Iranian Conference on Machine Vision and Image Proc...

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2010 6th Iranian conference on Machine vision and image processing, MVIP 2010

ISBN: (纸本)9781424497065

the proceedings contain 55 papers. the topics discussed include: classification of subcellular location patterns in fluorescence microscope images based on modified threshold adjacency statistics;automatic extraction of positive cells in pathology images of meningioma based on the maximal entropy principle and HSV color space;a novel iris segmentation method based on balloon active contour;retinal vessel segmentation using color image morphology and local binary patterns;computer-aided detection of proliferative cells and mitosis index in immunohistichemically images of meningioma;full automatic micro calcification detection in mammogram images using artificial neural network and Gabor wavelets;lung nodule segmentation using active contour modeling;assessment and elimination of errors due to electrode displacements in elliptical and square models in EIT;and a new scheme of face image encoding through wireless fading channels using WBCT and block thresholding.

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Automated petroglyph image segmentation with interactive classifier fusion 12

Automated petroglyph image segmentation with interactive cla...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Seidl, Markus Breiteneder, Christian Media Computing Group St. Pölten University of Applied Sciences Austria Interactive Media Systems Group Vienna University of Technology Austria

ISBN: (纸本)9781450316606

the number of high quality images of rock panels containing petroglyphs grows steadily. Different time-consuming manual methods to determine and document the exact shapes and spatial locations of petroglyphs on a panel have been carried out over decades. the first step for classification and retrieval of petroglyphs is the segmentation of the images. In this paper, we present and evaluate an automated approach to segment petroglyph images. © 2012 ACM.

关键词： image segmentation

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Dementia Disease Progression Analysis from 2D Cellular Automata Architecture 24

Dementia Disease Progression Analysis from 2D Cellular Autom...

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15th indian conference on computer vision graphics and image processing

作者： Kesu, Siva Manohar Reddy Sinha, Neelam Ramasangu, Hariharan Inst Sci Campus Ctr Brain Res Indian Bangalore Karnataka India Relecura Bangalore Karnataka India

ISBN: (纸本)9798400710759

the detection of change in mild cognitive impairment (MCI) is vital between one visit and another visit for the early-stage detection of Alzheimer's disease (AD). It is associated with changes in the structural and functional aspects. Diagnosis of dementia progression is a challenging but essential step for the planning of early treatment. this paper proposes the cellular automata (CA) framework to identify the changes when the disease progresses from one visit to another. An updated rule using the conduction function of anisotropic diffusion has been proposed to assess the earliest signs of dementia manifestation and progression. the progression of dementia disease in multi-visit subjects for healthy and unhealthy patients has been analyzed by examining the entropy from evolved 2D CA images. the subjects below the reference line in healthy cases lead to the prognosis of the disease, whereas subjects above the reference line in unhealthy subjects are in improved condition. the proposed CA architecture shows the transitions and progressive patterns, thereby offering a method for tracking the evolution of dementia over time.

关键词： Dementia Functional Connectivity FMRI Cellular Automata Disease Progression

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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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Bilateral filtering for gray and color images

Bilateral filtering for gray and color images

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6th International conference on computer vision

作者： Tomasi, C Manduchi, R Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)8173192219

Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. the method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.

关键词： Color image processing

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Estimation of the area of mouth opening during speech production 12

Estimation of the area of mouth opening during speech produc...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Saha, Sudipan Pandey, Prem C. Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai-400076 Maharashtra India

ISBN: (纸本)9781450316606

Estimation of the area of mouth opening from a video of the speaker's face and processing of the simultaneously recorded speech signal can be used for estimation of vocal tract shape during speech production. A technique is presented to estimate the area of mouth opening, using a processing based on contrast enhancement with multi-threshold binarization and connected component detection for reducing the errors in estimating the vertical opening due to the presence of teeth and tongue in mouth opening. the method is tested on vowel utterances from 12 speakers. © 2012 ACM.

关键词： Speech

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Style-sheets Extraction from Existing Digital Contents by image processing for Web-based BML Contents Management System

Style-sheets Extraction from Existing Digital Contents by Im...

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6th International conference on computer graphics, Imaging and Visualization (CGIV 2009)

作者： Morimoto, Hidekazu Meing, Fanfan Takano, Shigeru Okada, Yoshihiro Kyushu Univ Grad Sch Informat Sci & Elect Engn Nishi Ku Fukuoka 8190395 Japan

ISBN: (纸本)9780769537894

this paper proposes a web-based BML contents management system. BML is the short form of Broadcast Markup Language which is a script language for data broadcasting contents included in digital TV broadcasting services of Japan. the scripting style of BML is very similar to that of HTML and it also supports style-sheets and JavaScript. However, in BML, there are many restrictions about the display area size, font sizes, color types and so on because the BML-browser of TV is not flexible as compared with the Web-browser. Although there are a couple of dedicated software packages for BML contents creation, they are very expensive and difficult to use. So, BML contents creation is not easy for the end-user. To make it easier to create BML contents, the authors have been developing a web-based BML contents management system. this paper explains fundamental functionalities provided by the proposed BML contents management system. Already many digital contents were created and stored and we can easily obtain their images by capturing the screen snapshot. So, this paper also proposes style-sheets extraction method for BML contents from such already existing digital contents by image processing techniques.

关键词： BML Style sheets Contentsmanagement system image processing

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Improved Leaf image Classification Using Topological Features and CNN with Attention Module 6

Improved Leaf Image Classification Using Topological Feature...

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6th International conference on Signal and image processing, ICSIP 2021

作者： Zhang, Yanping Wuhan University of Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9780738133737

Leaf image recognition is a fine-grained image classification task in the computer vision domain that is fundamental yet challenging both in biology and computer vision research communities. With the advent of the Convolutional Neural Network (CNN), encouraging accuracy has been achieved on leaf image recognition. However, the current CNN-based methods mainly capture the leaf properties of geometric and statistical distribution aspects but ignore the topological features. In this paper, we integrated the topological descriptor Persistence image (PI) with the classic CNN model VGG16 to improve the accuracy of leaf image classification. A pretrained model Pi-net was adopted as the PI extractors, and an attention module was introduced to fuse the topological features and the features learned by CNN. Experiments on species dataset Flavia, Swedish, and Folio, and a more fine-grained cultivar dataset Cherry demonstrated the effectiveness of the proposed method. Our method achieved an accuracy of 99.85% on the Flavia dataset, 99.92% on the Swedish dataset, 99.64% on the Folio dataset, and 68.95% on the Cherry image dataset. Besides, the huge accuracy improvement on the Cherry dataset also demonstrated the advantage of our method in fine-grained leaf image classification. © 2021 IEEE.

关键词： image classification

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No Prompting Frozen Foundation Models: Interactive Medical Volume Segmentation using Continual Test Time Adaptation of Compact Models 24

No Prompting Frozen Foundation Models: Interactive Medical V...

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15th indian conference on computer vision graphics and image processing

作者： Borkar, Kushal Reen, Abhilaksh Singh Jawahar, C. V. Arora, Chetan Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Delhi Delhi India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Automated segmentation of medical image volumes promises to reduce costly medical experts' time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. this paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72x. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64x. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

关键词： Medical image Analysis image Segmentation Human-computer Interaction Continual Learning

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Automobile Cluster Pointer Defect Detection System using Adaptive Intensity Adjustment 6

Automobile Cluster Pointer Defect Detection System using Ada...

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6th IEEE International conference on Industrial Engineering and Applications (ICIEA)

作者： Trakulwaranont, Donnaphat Cooharojananone, Nagul Kruachottikul, Pravee Pitak, Panupong Gongsri, Nanthachot Aitphawin, Sermpong Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand Solitech Innovat & Technol Co Ltd Bangkok Thailand Thai Nippon Seiki Co Ltd Bangkok Thailand

ISBN: (纸本)9781728108513

In the manufacturing, inspection process always facing the problem of time and consuming number of staff consuming. Because companies want to maintain high quality of their products. Introducing a technology such as autonomous machine vision and computer-based algorithms would solve a problem. Typically, this detection requires human to inspect the pointer, however it takes time and sometimes the errors are caused by human eyes judgement. So, we can apply image processing and robotics to reduce number of staff, human error, time and cost in this inspection. therefore, this paper presents an automatic system to detect, analyze and measure size of the defect on automobile cluster pointer. We introduced an adaptive intensity adjustment algorithm to solve a variation of intensity from taking image of various color of cluster pointer by a fix aperture camera. In the experimental result, our defection size measurement passed a bias test. then, our system was tested in the real production line over 66,160 pieces within two months period. the result shown that the accuracy was 99.4%.

关键词： industrial 4.0 automatic inspection system automobile pointer inspection image processing machine vision

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