Sloven Dubois

A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

This paper presents four spatio-temporal wavelet decompositions for characterizing dynamic textures. The main goal of this work is to compare the influence of spatial and temporal variables in the wavelet decomposition scheme. Its novelty is to establish a comparison between the only existing method [11] and three other spatio-temporal decompositions. The four decomposition schemes are presented and successfully applied on a large dynamic texture database. Construction of feature descriptors are tackled as well their relevance, and performances of the methods are discussed. Finally, future prospects are exposed.

Decomposition of Dynamic Textures Using Morphological Component Analysis

The research context of this paper is dynamic texture analysis and characterization. Many dynamic textures can be modeled as large scale propagating wavefronts and local oscillating phenomena. After introducing a formal model for dynamic textures, the morphological component analysis (MCA) approach with a well-chosen dictionary is used to retrieve the components of dynamic textures. We define two new strategies for adaptive thresholding in the MCA framework, which greatly reduce the computation time when applied on videos. Tests on real image sequences illustrate the efficiency of the proposed method. An application to global motion estimation is proposed and future prospects are finally exposed.

A 3D discrete curvelet based method for segmenting dynamic textures

This paper presents a new approach for segmenting a video sequence containing dynamic textures. The proposed method is based on a 2D+T curvelet transform and an octree hierarchical representation. The curvelet transform enables to outline spatio-temporal structures of a given scale and orientation. The octree structure based on motion coherence enables a better spatio-temporal segmentation than a direct application of the 2D+T curvelet transform. Our segmentation method is successfully applied on video sequences of dynamic textures. Future prospects are finally exposed.

Ancient documents denoising and decomposition using aujol and chambolle algorithm

With the improvement of printing technology since the 15th century, there is a huge amount of printed documents published and distributed. These documents are degraded by the time and require to be preprocessed before being submitted to image indexing strategy, in order to enhance the quality of images. This paper proposes a new pre-processing that permits to denoise these documents, by using a Aujol and Chambolle algorithm. Aujol and Chambolle algorithm allows to extract meaningful components from image. In this case, we can extract shapes, textures and noise. Some examples of specific processings applied on each layer are illustrated in this paper.

Decomposition of Dynamic Textures Using Morphological Component Analysis: A New Adaptative Strategy

The research context of this work is dynamic texture analysis and characterization. Many dynamic textures can be modeled as a large scale propagating wave and local oscillating phenomena. The Morphological Component Analysis algorithm is used to retrieve these components using a well chosen dictionary. We define a new strategy for adaptive thresholding in the Morphological Component Analysis framework, which greatly reduces the computation time when applied on videos. Tests on synthetic and real image sequences illustrate the efficiency of the proposed method and future prospects are finally exposed.

Morphological Component Analysis for Decomposing Dynamic Textures

The research context of this work is dynamic texture analysis and characterization. A dynamic texture can be described as a time-varying phenomenon with a certain repetitiveness in both space and time.

Segmenting and indexing old documents using a letter extraction

This paper presents a new method to extract areas of interest in drop caps and particularly the most important shape: Letter itself. This method relies on a combination of a Aujol and Chambolle algorithm and a Segmentation using a Zipf Law and can be enhanced as a three-step process: 1) Decomposition in layers 2) Segmentation using a Zipf Law 3) Selection of the connected components.