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Conference

International Symposium on Image and Signal Processing and Analysis 

About: International Symposium on Image and Signal Processing and Analysis is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Image processing. Over the lifetime, 1163 publications have been published by the conference receiving 8227 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
24 Oct 2005
TL;DR: It is first shown using simple arguments that the so-called residual and stratified methods do yield an improvement over the basic multinomial resampling approach, and a central limit theorem is established for the case where resamplings is performed using the residual approach.
Abstract: This contribution is devoted to the comparison of various resampling approaches that have been proposed in the literature on particle filtering. It is first shown using simple arguments that the so-called residual and stratified methods do yield an improvement over the basic multinomial resampling approach. A simple counter-example showing that this property does not hold true for systematic resampling is given. Finally, some results on the large-sample behavior of the simple bootstrap filter algorithm are given. In particular, a central limit theorem is established for the case where resampling is performed using the residual approach.

692 citations

Proceedings ArticleDOI
18 Sep 2003
TL;DR: The purpose is to develop an automatic computerized screening system to recognize automatically the main components of the retina, an important features of background diabetic retinopathy and classify the normal, abnormal and unknown retinal image.
Abstract: The purpose is to develop an automatic computerized screening system to recognize automatically the main components of the retina, an important features of background diabetic retinopathy and classify the normal, abnormal and unknown retinal image. This paper has presented 4 main methods to succeed of retinal diagnosis. Firstly, the retinal images are preprocessed via adaptive, local, contrast enhancement Secondly, the main features of a retinal image were defined as the optic disc, and blood vessels. The optic discs were located by identifying the area with the highest variation in intensity of adjacent pixels. Blood vessels were identified by means of a multilayer perceptron neural network, for which the inputs were derived from a principal component analysis of the image and edge detection of the intensity. Next, the background diabetic retinopathy features are identified. Recursive region growing segmentation algorithms were applied to detect the hard exudates. The haemorrhages and microaneurysms were recognised by detecting all feature similar to the blood vessels and removed the vessels out. Finally, all information is accumulated and diagnosed for diabetic retinopathy or a normal retina. The diabetic retinopathy screening technique has been applied to the 484 normal retina images and 283 images with diabetic retinopathy. The sensitivity and specificity for the computerized screening program to classify the images were corrected 80.21% and 70.66% respectively. The computerized screening system has been developed to classify the normal and abnormalities of retinal images. The development of getting higher performance is in progress.

107 citations

Proceedings ArticleDOI
18 Sep 2003
TL;DR: An efficient algorithm which can automatically detect, localize and extract horizontally aligned text in images (and digital videos) with complex backgrounds is presented.
Abstract: Text detection in images or videos is an important step to achieve multimedia content retrieval. In this paper, an efficient algorithm which can automatically detect, localize and extract horizontally aligned text in images (and digital videos) with complex backgrounds is presented. The proposed approach is based on the application of a color reduction technique, a method for edge detection, and the localization of text regions using projection profile analyses and geometrical properties. The output of the algorithm are text boxes with a simplified background, ready to be fed into an OCR engine for subsequent character recognition. Our proposal is robust with respect to different font sizes, font colors, languages and background complexities. The performance of the approach is demonstrated by presenting promising experimental results for a set of images taken from different types of video sequences.

92 citations

Proceedings ArticleDOI
24 Oct 2005
TL;DR: Thirty local geometrical features extracted from 3D hitman face surfaces have been used to model the face for face recognition, with the most discriminating ones selected from a set of 86.
Abstract: Thirty local geometrical features extracted from 3D hitman face surfaces have been used to model the face for face recognition. They are the most discriminating ones selected from a set of 86. We have experimented with 420 3D-facial meshes (without texture) of 60 individuals. There are 7 images per subject including views presenting fight rotations and facial expressions. The HK algorithm, based in the signs of the mean and Gaussian curvatures, has been used for region segmentation. Experiments under controlled and non-controlled acquisition conditions, considering pose variations and facial expressions, have been achieved to analyze the robustness of the selected characteristics. Success recognition results of 82.0% and 90.16% were obtained when the images are frontal views with neutral expression using PCA and SVM, respectively. The recognition rates only decrease to 76.2% and 77.9% using PCA and SVM matching schemes respectively, under gesture and light face rotation.

77 citations

Proceedings ArticleDOI
01 Sep 2013
TL;DR: This database is to the authors' knowledge the first and only database which has diabetic retinopathy pathologies and major fundus structures annotated for every image from the database which makes it perfect for design and evaluation of currently available and new image processing algorithms for early detection of diabetic Retinopathy using color fundus images.
Abstract: Diabetic retinopathy is one of the leading disabling chronic diseases, and one of the leading causes of preventable blindness in the world Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs For automated screening programs to work robustly a representative fundus image database is required In this paper we give an overview of currently available databases and present a new diabetic retinopathy database Our database is to our knowledge the first and only database which has diabetic retinopathy pathologies and major fundus structures annotated for every image from the database which makes it perfect for design and evaluation of currently available and new image processing algorithms for early detection of diabetic retinopathy using color fundus images

59 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202299
202147
201965
201742
201556
2013147