Pub Date : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.38
F. Ayres, R. Rangayyan
Oriented feature detectors are fundamental tools in image understanding, as many images display relevant information in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, a performance analysis is presented of the following oriented feature detectors: the Gaussian second-derivative steerable filter, the quadrature-pair Gaussian second-derivative steerable filter, the real Gabor filter, the complex Gabor filter, and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of linear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features, as well as their accuracy in the estimation of the angle of the oriented features present in the image. It is shown that the Gabor filters yield the best detection performance and angular accuracy, whereas the steerable filters have the best performance in terms of computational speed.
{"title":"Performance Analysis of Oriented Feature Detectors","authors":"F. Ayres, R. Rangayyan","doi":"10.1109/SIBGRAPI.2005.38","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.38","url":null,"abstract":"Oriented feature detectors are fundamental tools in image understanding, as many images display relevant information in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, a performance analysis is presented of the following oriented feature detectors: the Gaussian second-derivative steerable filter, the quadrature-pair Gaussian second-derivative steerable filter, the real Gabor filter, the complex Gabor filter, and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of linear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features, as well as their accuracy in the estimation of the angle of the oriented features present in the image. It is shown that the Gabor filters yield the best detection performance and angular accuracy, whereas the steerable filters have the best performance in terms of computational speed.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116662","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.41
Luís Augusto Consularo, R. M. C. Junior
This paper presents a new method for segmentation and recognition of image objects based on structural pattern recognition. The input image is decomposed into regions through a quadtree algorithm. The decomposed image is represented by an attributed relational graph (ARG) named input graph. The objects to be recognized are also stored in an ARG named model graph. Object segmentation and recognition are accomplished by matching the input graph to the model graph. The possible inexact matches between the two graphs are cliques of the association graph between them. An objective function, to be optimized, is defined for each clique in order to measure how suitable is the match between the graphs. Therefore, recognition is modeled as an optimization procedure. A beam-search algorithm is used to optimize the objective function. Experimental results corroborating the proposed approach are presented.
{"title":"Quadtree-Based Inexact Graph Matching for Image Analysis","authors":"Luís Augusto Consularo, R. M. C. Junior","doi":"10.1109/SIBGRAPI.2005.41","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.41","url":null,"abstract":"This paper presents a new method for segmentation and recognition of image objects based on structural pattern recognition. The input image is decomposed into regions through a quadtree algorithm. The decomposed image is represented by an attributed relational graph (ARG) named input graph. The objects to be recognized are also stored in an ARG named model graph. Object segmentation and recognition are accomplished by matching the input graph to the model graph. The possible inexact matches between the two graphs are cliques of the association graph between them. An objective function, to be optimized, is defined for each clique in order to measure how suitable is the match between the graphs. Therefore, recognition is modeled as an optimization procedure. A beam-search algorithm is used to optimize the objective function. Experimental results corroborating the proposed approach are presented.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"16 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856826","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.27
T. Lewiner, M. Craizer, H. Lopes, S. Pesco, L. Velho, Esdras Medeiros
Among the mesh compression algorithms, different schemes compress better specific categories of model. In particular, geometry-driven approaches have shown outstanding performances on isosurfaces. It would be expected these algorithm to also encode well meshes reconstructed from the geometry, or optimized by a geometric re-meshing. GEncode is a new single-rate compression scheme that compresses the connectivity of these meshes at almost zero-cost. It improves existing geometry-driven schemes for general meshes on both geometry and connectivity compression. This scheme extends naturally to meshes of arbitrary dimensions in arbitrary ambient space, and deals gracefully with non-manifold meshes. Compression results for surfaces are competitive with existing schemes.
{"title":"GEncode: Geometry-Driven Compression in Arbitrary Dimension and Co-Dimension","authors":"T. Lewiner, M. Craizer, H. Lopes, S. Pesco, L. Velho, Esdras Medeiros","doi":"10.1109/SIBGRAPI.2005.27","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.27","url":null,"abstract":"Among the mesh compression algorithms, different schemes compress better specific categories of model. In particular, geometry-driven approaches have shown outstanding performances on isosurfaces. It would be expected these algorithm to also encode well meshes reconstructed from the geometry, or optimized by a geometric re-meshing. GEncode is a new single-rate compression scheme that compresses the connectivity of these meshes at almost zero-cost. It improves existing geometry-driven schemes for general meshes on both geometry and connectivity compression. This scheme extends naturally to meshes of arbitrary dimensions in arbitrary ambient space, and deals gracefully with non-manifold meshes. Compression results for surfaces are competitive with existing schemes.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115865999","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.47
J. Torreão, João L. Fernandes
The limited depth of field causes scene points at various distances from a camera to be imaged with different amounts of defocus. If images captured under different aperture settings are available, the defocus measure can be estimated and used for 3D scene reconstruction. Usually, defocusing is modeled by gaussian convolution over local image patches, but the estimation of a defocus measure based on that is hampered by the spurious high-frequencies introduced by windowing. Here we show that this can be ameliorated by the use of unnormalized gaussians, which allow defocus estimation from the zero-frequency Fourier component of the image patches, thus avoiding spurious high frequencies. As our main contribution, we also show that the modified shape from defocus approach can be extended to shape estimation from single shading inputs. This is done by simulating an aperture change, via gaussian convolution, in order to generate the second image required for defocus estimation. As proven here, the gaussian-blurred image carries an explicit depth-dependent blur component - which is missing from an ideal shading input -, and thus allows depth estimation as in the multi-image case.
{"title":"Single-Image Shape from Defocus","authors":"J. Torreão, João L. Fernandes","doi":"10.1109/SIBGRAPI.2005.47","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.47","url":null,"abstract":"The limited depth of field causes scene points at various distances from a camera to be imaged with different amounts of defocus. If images captured under different aperture settings are available, the defocus measure can be estimated and used for 3D scene reconstruction. Usually, defocusing is modeled by gaussian convolution over local image patches, but the estimation of a defocus measure based on that is hampered by the spurious high-frequencies introduced by windowing. Here we show that this can be ameliorated by the use of unnormalized gaussians, which allow defocus estimation from the zero-frequency Fourier component of the image patches, thus avoiding spurious high frequencies. As our main contribution, we also show that the modified shape from defocus approach can be extended to shape estimation from single shading inputs. This is done by simulating an aperture change, via gaussian convolution, in order to generate the second image required for defocus estimation. As proven here, the gaussian-blurred image carries an explicit depth-dependent blur component - which is missing from an ideal shading input -, and thus allows depth estimation as in the multi-image case.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363174","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.50
A. Machado
This article presents a new method for discovering hidden patterns in high-dimensional dataset resulting from image registration. It is based on true factor analysis, a statistical model that aims to find clusters of correlated variables. Applied to medical imaging, factor analysis can potentially identify regions that have anatomic significance and lend insight to knowledge discovery and morphometric investigations related to pathologies. Existent factor analytic methods require the computation of the sample covariance matrix and are thus limited to low-dimensional variable spaces. The proposed algorithm is able to compute the coefficients of the model without the need of the covariance matrix, expanding its spectrum of applications. The method’s efficiency and effectiveness is demonstrated in a study of volumetric variability related to the Alzheimer’s disease.
{"title":"True Factor Analysis in Medical Imaging: Dealing with High-Dimensional Spaces","authors":"A. Machado","doi":"10.1109/SIBGRAPI.2005.50","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.50","url":null,"abstract":"This article presents a new method for discovering hidden patterns in high-dimensional dataset resulting from image registration. It is based on true factor analysis, a statistical model that aims to find clusters of correlated variables. Applied to medical imaging, factor analysis can potentially identify regions that have anatomic significance and lend insight to knowledge discovery and morphometric investigations related to pathologies. Existent factor analytic methods require the computation of the sample covariance matrix and are thus limited to low-dimensional variable spaces. The proposed algorithm is able to compute the coefficients of the model without the need of the covariance matrix, expanding its spectrum of applications. The method’s efficiency and effectiveness is demonstrated in a study of volumetric variability related to the Alzheimer’s disease.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134009823","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.42
H. I. A. Bustos, H. Y. Kim
Maximum entropy (MENT) is a well-known image reconstruction algorithm. If only a small amount of acquisition data is available, this algorithm converges to a noisy and blurry image. We propose an improvement to this algorithm that consists on applying alternately the MENT reconstruction and the robust anisotropic diffusion (RAD). We have tested this idea for the reconstruction from full-angle parallel acquisition data, but the idea can be applied to any data acquisition scenario. The new technique has yielded surprisingly clear images with sharp edges even using extremely small amount of projection data.
{"title":"Reconstruction-Diffusion: An Improved Maximum Entropy Reconstruction Algorithm Based on the Robust Anisotropic Diffusion","authors":"H. I. A. Bustos, H. Y. Kim","doi":"10.1109/SIBGRAPI.2005.42","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.42","url":null,"abstract":"Maximum entropy (MENT) is a well-known image reconstruction algorithm. If only a small amount of acquisition data is available, this algorithm converges to a noisy and blurry image. We propose an improvement to this algorithm that consists on applying alternately the MENT reconstruction and the robust anisotropic diffusion (RAD). We have tested this idea for the reconstruction from full-angle parallel acquisition data, but the idea can be applied to any data acquisition scenario. The new technique has yielded surprisingly clear images with sharp edges even using extremely small amount of projection data.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134062061","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.43
D. Acevedo, Ana M. C. Ruedin
We present a lossless compressor for multispectral images that exploits interband correlations. Each band is divided into blocks, to which a wavelet transform is applied. The wavelet coefficients are predicted by means of a linear combination of coefficients belonging to the same orientation and spatial location. The prediction errors are then encoded with an entropy - based coder. Our original contributions are i) the inclusion, among the candidates for prediction, of coefficients of the same location from other spectral bands, ii) the calculation of weights tuned to the landscape being processed, iii) a fast block classification and a different band-ordering for each landscape. Our compressor reduces the size of an image to about a fourth of its original size. Our method is equivalent to LOCO-I, on 3 of the images tested it was superior. It is superior to other lossless compressors: WinZip, JPEG2000 and PNG.
{"title":"Reduction of Interband Correlation for Landsat Image Compression","authors":"D. Acevedo, Ana M. C. Ruedin","doi":"10.1109/SIBGRAPI.2005.43","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.43","url":null,"abstract":"We present a lossless compressor for multispectral images that exploits interband correlations. Each band is divided into blocks, to which a wavelet transform is applied. The wavelet coefficients are predicted by means of a linear combination of coefficients belonging to the same orientation and spatial location. The prediction errors are then encoded with an entropy - based coder. Our original contributions are i) the inclusion, among the candidates for prediction, of coefficients of the same location from other spectral bands, ii) the calculation of weights tuned to the landscape being processed, iii) a fast block classification and a different band-ordering for each landscape. Our compressor reduces the size of an image to about a fourth of its original size. Our method is equivalent to LOCO-I, on 3 of the images tested it was superior. It is superior to other lossless compressors: WinZip, JPEG2000 and PNG.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132846046","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.34
A. Falcão, P. A. Miranda, A. Rocha, F. Bergo
The notion of "strength of connectedness" between pixels has been successfully used in image segmentation. We present an extension to these works, which can considerably increase the efficiency of object definition tasks. A set of pixels is said a ê-connected component with respect to a seed pixel when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we found that seed competition with different thresholds can reduce the number of seeds and the need for user interaction during segmentation. We also propose automatic and user-friendly interactive methods for determining the thresholds. The improvements are demonstrated through several segmentation experiments involving medical images.
{"title":"Object Detection by K-Connected Seed Competition","authors":"A. Falcão, P. A. Miranda, A. Rocha, F. Bergo","doi":"10.1109/SIBGRAPI.2005.34","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.34","url":null,"abstract":"The notion of \"strength of connectedness\" between pixels has been successfully used in image segmentation. We present an extension to these works, which can considerably increase the efficiency of object definition tasks. A set of pixels is said a ê-connected component with respect to a seed pixel when the strength of connectedness of any pixel in that set with respect to the seed is higher than or equal to a threshold. While the previous approaches either assume no competition with a single threshold for all seeds or eliminate the threshold for seed competition, we found that seed competition with different thresholds can reduce the number of seeds and the need for user interaction during segmentation. We also propose automatic and user-friendly interactive methods for determining the thresholds. The improvements are demonstrated through several segmentation experiments involving medical images.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133092889","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.17
Marcus A. C. Farias, C. Scheidegger, J. Comba, L. Velho
Point-based modeling and rendering is an active area of research in Computer Graphics. The concept of points with attributes (e.g. normals) is usually referred to as surfels, and many algorithms have been devised to their effi- cient manipulation and rendering. Key to the efficiency of many methods is the use of partitioning schemes, and usually axis-aligned structures such as octrees and KD-trees are preferred, instead of more general BSP-trees. In this work we introduce a data structure called Constrained BSP-tree (CBSP-tree) that can be seen as an intermediate structure between KD-trees and BSP-trees. The CBSP-tree is characterized by allowing arbitrary cuts as long as the complexity of its cells remains bounded, allowing better approximation of curved regions. We discuss algorithms to build CBSP-trees using the flexibility that the structure offers, and present a modified algorithm for boolean operations that uses a new inside-outside object classification. Results show that CBSP-trees generate fewer cells than axis-aligned structures.
{"title":"Boolean Operations on Surfel-Bounded Objects Using Constrained BSP-Trees","authors":"Marcus A. C. Farias, C. Scheidegger, J. Comba, L. Velho","doi":"10.1109/SIBGRAPI.2005.17","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.17","url":null,"abstract":"Point-based modeling and rendering is an active area of research in Computer Graphics. The concept of points with attributes (e.g. normals) is usually referred to as surfels, and many algorithms have been devised to their effi- cient manipulation and rendering. Key to the efficiency of many methods is the use of partitioning schemes, and usually axis-aligned structures such as octrees and KD-trees are preferred, instead of more general BSP-trees. In this work we introduce a data structure called Constrained BSP-tree (CBSP-tree) that can be seen as an intermediate structure between KD-trees and BSP-trees. The CBSP-tree is characterized by allowing arbitrary cuts as long as the complexity of its cells remains bounded, allowing better approximation of curved regions. We discuss algorithms to build CBSP-trees using the flexibility that the structure offers, and present a modified algorithm for boolean operations that uses a new inside-outside object classification. Results show that CBSP-trees generate fewer cells than axis-aligned structures.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117080945","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 : 2005-10-09DOI: 10.1109/SIBGRAPI.2005.22
Leandro A. F. Fernandes, Manuel M. Oliveira, Roberto da Silva, G. Crespo
We present a new method for computing the dimensions of boxes from single perspective projection images in real time. Given a picture of a box, acquired with a camera whose intrinsic parameters are known, the dimensions of the box are computed from the extracted box silhouette and the projection of two parallel laser beams on one of its visible faces. We also present a statistical model for background removal that works with a moving camera, and an efficient voting scheme for identifying approximately collinear segments in the context of a Hough Transform. We demonstrate the proposed approach and algorithms by building a prototype of a scanner for computing box dimensions and using it to automatically compute the dimensions of real boxes. The paper also presents some statistics over measurements obtained with our scanner prototype.
{"title":"Computing Box Dimensions from Single Perspective Images in Real Time","authors":"Leandro A. F. Fernandes, Manuel M. Oliveira, Roberto da Silva, G. Crespo","doi":"10.1109/SIBGRAPI.2005.22","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2005.22","url":null,"abstract":"We present a new method for computing the dimensions of boxes from single perspective projection images in real time. Given a picture of a box, acquired with a camera whose intrinsic parameters are known, the dimensions of the box are computed from the extracted box silhouette and the projection of two parallel laser beams on one of its visible faces. We also present a statistical model for background removal that works with a moving camera, and an efficient voting scheme for identifying approximately collinear segments in the context of a Hough Transform. We demonstrate the proposed approach and algorithms by building a prototype of a scanner for computing box dimensions and using it to automatically compute the dimensions of real boxes. The paper also presents some statistics over measurements obtained with our scanner prototype.","PeriodicalId":193103,"journal":{"name":"XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699097","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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