The Experts below are selected from a list of 4032 Experts worldwide ranked by ideXlab platform
Paul Scheunders - One of the best experts on this subject based on the ideXlab platform.
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a multivalued image wavelet representation based on Multiscale fundamental forms
IEEE Transactions on Image Processing, 2002Co-Authors: Paul ScheundersAbstract:A new wavelet representation for multivalued images is presented. The idea for this representation is based on the first fundamental form that provides a local measure for the contrast of a multivalued image. In this paper, this concept is extended toward Multiscale fundamental forms using the dyadic wavelet transform of Mallat (1992). The Multiscale fundamental forms provide a local measure for the contrast of a multivalued image at different scales. The representation allows for a Multiscale Edge description of multivalued images. A variety of applications is presented, including multispectral image fusion, color image enhancement and multivalued image noise filtering. In an experimental section, the presented techniques are compared to single valued and/or single scale algorithms that were previously described in the literature. The techniques, based on the new representation are demonstrated to outperform the others.
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Multiscale fundamental forms a multimodal image wavelet representation
International Conference on Image Analysis and Processing, 2001Co-Authors: Paul ScheundersAbstract:A new wavelet representation for multimodal images is presented. The idea for this representation is based on the first fundamental that provides a local measure for the contrast of a multimodal image. This concept is extended towards Multiscale fundamental forms using the dyadic wavelet transform of Mallat. The Multiscale fundamental forms provide a local measure for the contrast of a multimodal image at different scales. The representation allows for a Multiscale Edge description of multimodal images. Two applications are presented: multispectral image fusion and colour image noise filtering. In an experimental section, the presented techniques are compared to single valued and/or single scale algorithms that were previously described in the literature. The techniques based on the new representation are demonstrated to outperform the others.
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Multiscale Edge representation applied to image fusion
Wavelet applications in signal and image processing VIII SPIE Annual Meeting 2000 San Diego USA July 30-August 4 2000, 2000Co-Authors: Paul ScheundersAbstract:In this paper the fusion of multimodal images into one greylevel image is aimed at. A multiresolution technique, based on the wavelet Multiscale Edge representation is applied. The fusion consists of retaining only the modulus maxima of the wavelet coefficients from the different bands and combining them. After reconstruction, a synthetic image is obtained that contains the Edge information from all bands simultaneously. Noise reduction is applied by removing the noise-related modulus maxima. In several experiments on test images and multispectral satellite images, we demonstrate that the proposed technique outperforms mapping techniques, as PCA and SOM and other wavelet-based fusion techniques.
Moncef Gabbouj - One of the best experts on this subject based on the ideXlab platform.
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a generic shape texture descriptor over Multiscale Edge field 2 d walking ant histogram
IEEE Transactions on Image Processing, 2008Co-Authors: Serkan Kiranyaz, M Ferreira, Moncef GabboujAbstract:A novel shape descriptor, which can be extracted from the major object Edges automatically and used for the multimedia content-based retrieval in multimedia databases, is presented. By adopting a Multiscale approach over the Edge field where the scale represents the amount of simplification, the most relevant Edge segments, referred to as subsegments, which eventually represent the major object boundaries, are extracted from a scale-map. Similar to the process of a walking ant with a limited line of sight over the boundary of a particular object, we traverse through each subsegment and describe a certain line of sight, whether it is a continuous branch or a corner, using individual 2-D histograms. Furthermore, the proposed method can also be tuned to be an efficient texture descriptor, which achieves a superior performance especially for directional textures. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed shape descriptor in the context of multimedia indexing and retrieval.
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automatic object extraction over Multiscale Edge field for multimedia retrieval
IEEE Transactions on Image Processing, 2006Co-Authors: Serkan Kiranyaz, M Ferreira, Moncef GabboujAbstract:In this work, we focus on automatic extraction of object boundaries from Canny Edge field for the purpose of content-based indexing and retrieval over image and video databases. A Multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major Edges. At each stage of the simplification, Edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural Edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture
Edurne Barrenechea - One of the best experts on this subject based on the ideXlab platform.
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Multiscale Edge detection based on gaussian smoothing and Edge tracking
Knowledge Based Systems, 2013Co-Authors: Carlos Lopezmolina, B De Baets, Humberto Bustince, Jos Antonio Sanz, Edurne BarrenecheaAbstract:The human vision is usually considered a Multiscale, hierarchical knowlEdge extraction system. Inspired by this fact, Multiscale techniques for computer vision perform a sequential analysis, driven by different interpretations of the concept of scale. In the case of Edge detection, the scale usually relates to the size of the region where the intensity changes are measured or to the size of the regularization filter applied before Edge extraction. Multiscale Edge detection methods constitute an effort to combine the spatial accuracy of fine-scale methods with the ability to deal with spurious responses inherent to coarse-scale methods. In this work we introduce a Multiscale method for Edge detection based on increasing Gaussian smoothing, the Sobel operators and coarse-to-fine Edge tracking. We include visual examples and quantitative evaluations illustrating the benefits of our proposal.
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Multiscale Edge detection based on the sobel method
Intelligent Systems Design and Applications, 2011Co-Authors: Carlos Lopezmolina, Humberto Bustince, Edurne Barrenechea, Aranzazu Jurio, B De BaetsAbstract:The Multiscale techniques for Edge detection represent an effort to combine the spatial accuracy of small-scale methods with the ability to deal with spurious responses inherent to the large scale ones. In this work we introduce a Multiscale extension of the Sobel method for Edge detection based on Gaussian smoothing and fine-to-coarse Edge tracking. We include examples illustrating the procedure and its results, as well as some quantitative measurements of the improvement obtained with the Multiscale approach with respect to the original one.
Yang Houjun - One of the best experts on this subject based on the ideXlab platform.
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new method of contour initialization in gvf snake model
Journal of Computer Applications, 2006Co-Authors: Yang HoujunAbstract:A new method of contour initializaiton was proposed by analyzing the shortcomings of the huge waste of time and the failure of extracting some true contours in Gradient Vector Flow(GVF) Snake model.The Multiscale Edge detection based on wavelet transform could exactly distinguish all kinds of contours.The new contour initialization method based on wavelet transform could approach to the target boundaries.The experiment results show that this method reserves all the advantages of GVF Snake model and leads to the small searching range,decreased amount of GVF iteration and improvement of convergence speed.
Su Zhang - One of the best experts on this subject based on the ideXlab platform.
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a Multiscale Edge detection algorithm based on wavelet domain vector hidden markov tree model
Pattern Recognition, 2004Co-Authors: Junxi Sun, Yazhu Chen, Su ZhangAbstract:Abstract The wavelet analysis is an efficient tool for the detection of image Edges. Based on the wavelet analysis, we present an unsupervised learning algorithm to detect image Edges in this paper. A wavelet domain vector hidden Markov tree (WD-VHMT) is employed in our algorithm to model the statistical properties of Multiscale and multidirectional (subband) wavelet coefficients of an image. With this model, each wavelet coefficient is viewed as an observation of its hidden state and the hidden state indicates if the wavelet coefficient belongs to an Edge. The WD-VHMT model can be learned by an expectation–maximization algorithm. After the model is learned, we employ an extended Viterbi algorithm to uncover the hidden state sequences according to the maximum a posterior estimation. The experiment results of the Edge detection for several images are provided to evaluate our algorithm.
