Pub Date : 2004-03-28DOI: 10.1109/IAI.2004.1300971
C. Krishnamurthy, J.J. Rodriguez, R. Gillies
A novel and robust method for accurate segmentation of liver lesions is discussed. The initial contour for the snake is formed using edge and region information. The modified snake, guided by fuzzy edge information, deforms from this initial position, providing an accurate representation of the lesion boundary with few iterations and minimal user interaction. Results obtained from this algorithm are comparable to those obtained from manual segmentation by a trained radiologist.
{"title":"Snake-based liver lesion segmentation","authors":"C. Krishnamurthy, J.J. Rodriguez, R. Gillies","doi":"10.1109/IAI.2004.1300971","DOIUrl":"https://doi.org/10.1109/IAI.2004.1300971","url":null,"abstract":"A novel and robust method for accurate segmentation of liver lesions is discussed. The initial contour for the snake is formed using edge and region information. The modified snake, guided by fuzzy edge information, deforms from this initial position, providing an accurate representation of the lesion boundary with few iterations and minimal user interaction. Results obtained from this algorithm are comparable to those obtained from manual segmentation by a trained radiologist.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116063600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-03-28DOI: 10.1109/IAI.2004.1300934
S. Tominaga, N. Tanaka, T. Komada
The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.
{"title":"Imaging and rendering of oil paintings using a multi-band camera","authors":"S. Tominaga, N. Tanaka, T. Komada","doi":"10.1109/IAI.2004.1300934","DOIUrl":"https://doi.org/10.1109/IAI.2004.1300934","url":null,"abstract":"The paper proposes a method for the imaging and rendering of art paintings using a multi-band camera system. The surface shape of an art painting is considered as a rough plane rather than a three-dimensional curved surface. First. we present an algorithm for estimating the surface normal at each pixel point, based on a photometric stereo without using a rangefinder. Next, an algorithm is presented for estimating the spectral reflectance function from a set of pixel values acquired at different illumination directions. Then, the surface reflectance and normal data are used for estimating the light reflection properties. The Torrance-Sparrow model is used for model fitting and parameter estimation. Finally, an experiment using an oil painting is executed for demonstrating the feasibility of the proposed method.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132710518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-03-28DOI: 10.1109/IAI.2004.1300976
M. Nicolescu, G. Medioni
A main difficulty for estimating camera and scene geometry from a set of point correspondences is caused by the presence of false matches and independently moving objects. Given two images, after obtaining the matching points, they are usually filtered by an outlier rejection step before being used to solve for epipolar geometry and 3D structure estimation. In the presence of moving objects, image registration becomes a more challenging problem, as the matching and registration phases become interdependent. We propose a novel approach that decouples the above operations, allowing for explicit and separate handling of matching, outlier rejection, grouping, and recovery of camera and scene structure. The method is based on a voting-based computational framework for motion analysis; it determines an accurate representation, in terms of dense velocities, segmented motion regions and boundaries, by using only the smoothness of image motion, followed by the extraction of scene and camera 3D geometry.
{"title":"Voting-based grouping and interpretation of visual motion","authors":"M. Nicolescu, G. Medioni","doi":"10.1109/IAI.2004.1300976","DOIUrl":"https://doi.org/10.1109/IAI.2004.1300976","url":null,"abstract":"A main difficulty for estimating camera and scene geometry from a set of point correspondences is caused by the presence of false matches and independently moving objects. Given two images, after obtaining the matching points, they are usually filtered by an outlier rejection step before being used to solve for epipolar geometry and 3D structure estimation. In the presence of moving objects, image registration becomes a more challenging problem, as the matching and registration phases become interdependent. We propose a novel approach that decouples the above operations, allowing for explicit and separate handling of matching, outlier rejection, grouping, and recovery of camera and scene structure. The method is based on a voting-based computational framework for motion analysis; it determines an accurate representation, in terms of dense velocities, segmented motion regions and boundaries, by using only the smoothness of image motion, followed by the extraction of scene and camera 3D geometry.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130738058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-03-28DOI: 10.1109/IAI.2004.1300948
P. J. Myerscough, M. Nixon
We describe a robust moving feature detector that extracts feature points and feature velocities from a sequence of images. We develop a new approach based on phase congruency to include interpolation of feature orientation and improvements in robustness due to correlations in the image sequence. This new temporal phase congruency operator shows improved capabilities on a series of different real image types, as well as a noise analysis on synthetic images.
{"title":"Temporal phase congruency","authors":"P. J. Myerscough, M. Nixon","doi":"10.1109/IAI.2004.1300948","DOIUrl":"https://doi.org/10.1109/IAI.2004.1300948","url":null,"abstract":"We describe a robust moving feature detector that extracts feature points and feature velocities from a sequence of images. We develop a new approach based on phase congruency to include interpolation of feature orientation and improvements in robustness due to correlations in the image sequence. This new temporal phase congruency operator shows improved capabilities on a series of different real image types, as well as a noise analysis on synthetic images.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132347129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-03-28DOI: 10.1109/IAI.2004.1300954
S. Mozaffari, K. Faez, H. Kanan
We propose a new method for isolated handwritten Farsi/Arabic characters and numerals recognition using fractal codes. Fractal codes represent affine transformations which, when iteratively applied to the range-domain pairs in an arbitrary initial image, give results close to the given image. Each fractal code consists of six parameters, such as corresponding domain coordinates for each range block, brightness offset and an affine transformation, which are used as inputs for a multilayer perceptron neural network for learning and identifying an input. This method is robust to scale and frame size changes. Farsi's 32 characters are categorized to 8 different classes in which the characters are very similar to each other. There are ten digits in the Farsi/Arabic languages, but since two of them are not used in postal codes in Iran, only 8 more classes are needed for digits. According to experimental results, classification rates of 91.37% and 87.26% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.
{"title":"Recognition of isolated handwritten Farsi/Arabic alphanumeric using fractal codes","authors":"S. Mozaffari, K. Faez, H. Kanan","doi":"10.1109/IAI.2004.1300954","DOIUrl":"https://doi.org/10.1109/IAI.2004.1300954","url":null,"abstract":"We propose a new method for isolated handwritten Farsi/Arabic characters and numerals recognition using fractal codes. Fractal codes represent affine transformations which, when iteratively applied to the range-domain pairs in an arbitrary initial image, give results close to the given image. Each fractal code consists of six parameters, such as corresponding domain coordinates for each range block, brightness offset and an affine transformation, which are used as inputs for a multilayer perceptron neural network for learning and identifying an input. This method is robust to scale and frame size changes. Farsi's 32 characters are categorized to 8 different classes in which the characters are very similar to each other. There are ten digits in the Farsi/Arabic languages, but since two of them are not used in postal codes in Iran, only 8 more classes are needed for digits. According to experimental results, classification rates of 91.37% and 87.26% were obtained for digits and characters respectively on the test sets gathered from various people with different educational background and different ages.","PeriodicalId":326040,"journal":{"name":"6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2004-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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