Bertrand Augereau

A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the images structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.

A fast multi-scale edge detection algorithm

In this paper we present a new explicit numerical scheme to approximate the solution of the linear diffusion filtering. This scheme is fast, stable, easy to program, applicable to arbitrary dimensions, and preserves the discontinuities of the objects. Experimental results support the efficiency of the proposed approach for the multi-scale detection of edges in greyscale, and color images.

Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion

In this paper, we present the first participation of a consortium of French laboratories, IRIM, to the TRECVID 2008 BBC Rushes Summarization task. Our approach resorts to video skimming. We propose two methods to reduce redundancy, as rushes include several takes of scenes. We also take into account low and mid-level semantic features in an ad-hoc fusion method in order to retain only significant content

Multivariate orthogonal polynomials to extract singular points

In fluid motion analysis, the extraction of singularity is an important step. This points are crucial for the analysis of physic phenomenas. For instance in meteorology these singularities might represent the center of depression.The objective of this paper is to present an original method for extraction of singularities in a vector field. We study the affine model of the motion to extract potential singularities. The originality of our method reside in the computation of the affine model by projection of the vector field onto multivariate orthogonal polynomials basis. We use a one degree basis so this method is enough computationally efficient to be included in a multiscale scheme. We have tested this method on synthetic and experimental vector field. It provides significant results. Moreover this technique is robust to noise.

Human activities discrimination with motion approximation in polynomial bases

In this paper we propose a new descriptor for human motion analysis. We try to recognize human motion represented by sequence of vector fields which are obtained with optical flow. Our method is based on an efficient measure of the similarity between two vector fields. This measure depends on the approximation of vector fields using projection onto a polynomial basis. From this similarity measure, we can retrieve a vector field within video sequences. As we consider a human motion as a sequence of vector fields, we use a simple algorithm in order to recognize motion. The results of the motion recognition system are shown for five boxing motions.

A fast multiscale edge detection algorithm based on a new edge preserving PDE resolution scheme

In this communication we present a new explicit numerical scheme to approximate the solution of the linear diffusion filtering. It allows to introduce a new edge preserving scheme which is fast, stable, easy to program and applicable to any dimensions. Our diffusion scheme is then put into a simple and original multi-scale edge detection algorithm. Some experimental results of the proposed approach for the multi-scale detection of edges in grey scale images are presented, as well as a comparison with other diffusion filtering schemes.

Invariant feature extraction for 3D texture analysis using the autocorrelation function

Abstract A new method for invariant feature extraction on textured images undergoing affine transformations is presented. This is performed by transformation of the autocorrelation function (ACF) followed by determination of an invariant criterion which is the sum of the coefficients of the discrete correlation matrix. Experimental results support the effectiveness of the proposed approach.

Cooperation of the partial differential equation methods and the wavelet transform for the segmentation of multivalued images

In this work, the wavelet transform (WT) and two partial differential equations (PDEs)-based segmentation methods are merged together towards an efficient segmentation paradigm that integrates level-set functions and wavelet-based singularity detection to object extraction from multivalued images. To this end, different interfaces of the image regions are characterized using a wavelet-based multiscale multistructure tensor that is capable of identifying edges in spite of the presence of noise. With this wavelet-based multistructure tensor, the edge structures of a vector-valued image can be studied at different scales. This multiresolution edge-detection approach allows to reconstruct the accumulated orientational information of the multispectral image. Detected edges are then modeled by level-set functions. A functional is defined on these level sets whose minimizers define the optimal classification of objects. In a second step, the cooperation of PDE and WT is used for pioneering active contour segmentation method. For that purpose, foveal wavelets [S. Mallat, Foveal orthonormal wavelets for singularities, Technical Report, Ecole Polytechnique, 2000], known by their high capability to precisely characterize the holder regularity of singularities, are used to detect the image contours. These wavelets are capable of accurately characterizing edges of noisy images. The obtained foveal coefficients are used to guide the curve flow in an active contour segmentation process. Therefore a foveal-wavelet-based snake approach is formulated. The proposed approach is capable of driving the snake curve to the real edges of different regions in a noisy image. Promising experimental results illustrate the potential of the cooperation of the PDE and the WT in the segmentation of multivalued images.

Bandelet-Based Anisotropic Diffusion

Visual tasks often require a hierarchical representation of images in scales ranging from coarse to fine. A variety of linear and nonlinear smoothing techniques, such as Gaussian smoothing, anisotropic diffusion, regularization, wavelet thresholding etc... have been proposed. In this work, we propose a geometrical multiscale anisotropic diffusion based on the geometrical flow for denoising multivalued images. The geometrical flow is determined by the Bandelet transform of the image being processed. Consequently, the image is segmented into a quadtree where each square regroups pixels sharing the same geometrical flow direction. The motivation of this work is to introduce a new multiscale multistructure bandelet-based diffusion tensor to adjust the anisotropic diffusion toward the direction of the optimal geometrical flow. Therefore, multiple dyadic squares in the quadtree have multiple structure tensors. Hence, a more accurate geometrically driven noise suppression is obtained where the homogeneity of different image regions is well maintained.

A PDE-based method for ringing artifact removal on grayscale and color JPEG2000 images

As successor to JPEG, JPEG2000 aims to realize a very low bitrate. Although JPEG2000s rate distortion has been improved by approximately 30% against JPEG, as on most images compressed using overlapping transforms-based algorithms, one can notice visible spurious oscillations, or ringing artifacts, around the edges of a JPEG2000-compressed image. In this paper, we propose a partial differential equations, or PDE-based image enhancement technique, in order to remove these ringing artifacts, while keeping the images edges. Results are provided on grayscale and color images, showing a visible enhancement, confirmed by the PSNR increase.