A Learning Framework for Morphological Operators using Counter-Harmonic Mean

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1

A class of adaptive directional image smoothing filters

Cataloged from PDF version of article.The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates aline segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters. Copyright (C) 1996 Pattern Recognition Society

Adaptive polyphase subband decomposition structures for image compression

Cataloged from PDF version of article.Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented

Linear/nonlinear adaptive polyphase subband decomposition structures for image compression

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral bands of the original data. However, this approach leads to various artifacts in images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure vary according to the nature of the signal. This leads to higher compression ratios for images containing subtitles compared to fixed filter banks. Simulation examples are presented

Polyphase adaptive filter banks for subband decomposition

Subband decomposition is widely used in signal processing applications including image and speech compression. In most practical cases, the goal is to obtain subband signals that are suitable for data compression. In this paper, we present Perfect Reconstruction (PR) polyphase filter bank structures in which the filters adapt to the changing input conditions. This leads to higher compression results for images containing sharp edges, text, and subtitles

Gibbs random field model based weight selection for the 2-D adaptive weighted median filter

Cataloged from PDF version of article.A generalized filtering method based on the minimization of the energy of the Gibbs model is described. The well-known linear and median filters are all special cases of this method. It is shown that, with the selection of appropriate energy functions, the method can be successfully used to adapt the weights of the adaptive weighted median filter to preserve different textures within the image while eliminating the noise. The newly developed adaptive weighted median filter is based on a 3 x 3 square neighborhood structure. The weights of the pixels are adapted according to the clique energies within this neighborhood structure. The assigned energies to 2- or 3-pixel cliques are based on the local statistics within a larger estimation window. It is shown that the proposed filter performance is better compared to some well-known similar filters like the standard, separable, weighted and some adaptive weighted median filters

Analyse d'images : Filtrage et segmentation

Ouvrage publié avec l'aide du Ministère des affaires étrangères, direction de la coopération scientifique et technique. AVERTISSEMENT Le livre publié en 1995 chez MASSON (EAN13 : 9782225849237) est épuisé. Cette version pdf est une version élaborée à partie de la version préliminaire transmise à l'éditeur. La mise en page est légèrement différente de celle du livre. Malheureusement quelques figures de l'annexe C ont été perdues.International audienceL'analyse d'image touche à l'heure actuelle de nombreux domaines, avec des objectifs aussi variés que l'aide au diagnostic pour les images médicales, la vision artificielle en robotique ou l'analyse des ressources terrestres à partir des images prises par satellite. Le but du traitement de ces images est à la fois simple dans son concept et difficile dans sa réalisation. Simple en effet, puisqu'il s'agit de reconnaître des objets que notre système visuel perçoit rapidement, du moins pour la majorité d'entre eux. Difficile cependant, car dans la grande quantité d'informations contenues dans l'image, il faut extraire des éléments pertinents pour l'application visée et ceci indépendamment de la qualité de l'image. L'analyse d'image s'est donc dotée d'outils et de méthodes puissants issus de domaines aussi variés que les mathématiques, le traitement du signal, ou l'informatique. Cet ouvrage présente un des aspects les plus importants du traitement des images : la " segmentation ". Il récapitule d'abord les grandeurs observables et calculables sur une image et les algorithmes de manipulation des structures de données associées. Il détaille ensuite les traitements préliminaires, tels le filtrage du bruit et les deux types d'approche de la segmentation, l'extraction des contours et celle des régions. Chacune fait l'objet d'une étude théorique et de nombreux résultats illustrent les performances. Une des originalités de l'ouvrage est l'étude comparative des différentes techniques appliquées sur un même corpus d'images réelles