Philippe Montesinos

Adaptive total variation denoising based on difference curvature

Image denoising methods based on gradient dependent regularizers such as Rudin et al.s total variation (TV) model often suffer the staircase effect and the loss of fine details. In order to overcome such drawbacks, this paper presents an adaptive total variation method based on a new edge indicator, named difference curvature, which can effectively distinguish between edges and ramps. With adaptive regularization and fidelity terms, the new model has the following properties: at object edges, the regularization term is approximate to the TV norm in order to preserve the edges, and the weight of the fidelity term is large in order to preserve details; in flat and ramp regions, the regularization term is approximate to the L2 norm in order to avoid the staircase effect, and the weight of the fidelity term is small in order to strongly remove the noise. Comparative results on both synthetic and natural images demonstrate that the new method can avoid the staircase effect and better preserve fine details.

Differential invariants for color images

We present a new method for matching points in stereoscopic, uncalibrated color images. Our approach consists of characterizing points of interest using differential invariants. We define additional invariants of first order, exploiting color information. We show that this contribution makes the characterization sufficient for first order. In addition, we make our description robust to usual transformations of image. We present a robust generalization of a gray level corner detector to the case of color images. We also propose a simple and efficient scheme for matching these points, using our characterization. Finally, we present matching results and the epipolar geometry obtained on complex scenes, which clearly show the pertinence of our approach. We are able to match points robustly and rapidly, using only first order derivatives.

Thin nets and crest lines: application to satellite data and medical images

We describe a new approach for extracting crest lines and thin nets. The key point of our approach is to model thin nets as the crest lines of the image surface. Crest lines are the lines where one of the two principal curvatures is locally extremal. We define these lines using first, second and third derivatives of the image. We compute the image derivatives using recursive filters approximating the Gaussian filter and its derivatives. Using an adapted scale factor, we apply this approach to the extraction of roads in satellite data and blood vessels in medical images. We also apply this method to the extraction of the crest lines in depth maps of human faces.

Matching color uncalibrated images using differential invariants

Abstract This paper presents a new method for matching points of interest in stereoscopic, uncalibrated color images. It consists in characterizing color points using differential invariants. We define additional first order invariants, using color information, and we show that the first order is sufficient to make the characterization accurate. The characterization thus obtained is invariant to orthogonal image transformations. In addition, we make it robust enough for affine illumination transformations. We go on to present a generalization of a gray-level corner detector to the case of color images. Thirdly, we propose a robust and fast incremental technique for matching points of interest in uncalibrated cases, which works robustly and rapidly whatever the number of points to be matched. Our matching scheme is evaluated using stereo color images consisting of many points, with viewpoint and illumination variations. The results obtained clearly show the relevance of our approach.

Ramp preserving Perona-Malik model

The Perona-Malik (PM) model, a classical anisotropic diffusion, can preserve edges while removing the noise. However, the defect of traditional PM model is tending to contain the staircase effect in the processed image. For this reason, a novel ramp preserving Perona-Malik (RPPM) model based on a new edge indicator is presented. In the RPPM model, the diffusion coefficient of the PM model is adaptively determined to introduce following property: isotropic diffusion in flat and ramp regions to prevent the staircase effect and anisotropic diffusion in edge regions to preserve edges. Comparative results on synthetic and real image denoising demonstrate that our model can preserve important structures, such as edges and ramps, while avoiding the speckles and the complex numerical implementation arising from high-order partial differential equations (PDEs).

Thin Nets and Crest Lines

In this paper, we describe a new approach for extractingthin netsin gray-level images. The key point of our approach is to model thin nets as crest lines of the image surface. Crest lines are lines where the magnitude maximum curvature is a local maximum in the corresponding principal direction. We define these lines using first, second, and third derivatives of the image. The image derivatives are computed using recursive filters approximating the Gaussian filter and its derivatives. Using an adaptive scale factor, we apply this approach to the extraction of roads in satellite data, blood vessels in medical images, and actual crest lines in depth maps of human faces.

Perceptual organization of thin networks with active contour functions applied to medical and aerial images

This paper describes a new method of perceptual organization applied to the extraction of thin networks on aerial and medical images. The key point of our approach is to consider perceptual grouping as a problem of optimization. First the quality of a grouping is defined with a class of functions inspired by the energy functions used for active contours optimization (involving curvature, co-circularity, grey levels, and orientation). Such functions can be computed recursively, and optimized from a local to a global level with an algorithm related to dynamic programming. This is followed by a selection procedure which rates and extracts principal groupings. The validity of our approach is presented with synthetic images, aerial and medical data.

A Fast Matching Method for Color Uncalibrated Images using Differential Invariants.

In this paper we present a new method for point matching in stereoscopic color images. Our approach consists rst in characterizing points of interest using di erential invariants. Then we de ne additional rst order invariants using color information, which make suÆcient the characterization till rst order. In addition, we make our description robust to important image transformations like rotation, range of viewpoint and linear illumination variations. Second, we propose a new incremental technique for point matching using our characterization, which works robustly and rapidly whatever the number of points to be matched. Our stereo matching scheme is evaluated using stereo color images, with viewpoint and illumination variations. The very good results obtained clearly show the pertinence of our approach. Our color characterization produces a high rate of good matches, even though only rst order derivatives are used. Results on images holding many points show that our matching process is robust and rapidly implemented even if the points to be matched are numerous. It is a great asset, when matching a high set of points is necessary for example to realize dense depth maps between images. 1 Key word : Color Images, Di erential Invariants, Stereo Matching, Transfer Methods. This work was supported by a CCETT grant 96-ME-24. BMVC 1998 doi:10.5244/C.12.37 368 British Machine Vision Conference

A New Perceptual Edge Detector in Color Images

In this paper we propose a new perceptual edge detector based on anisotropic linear filtering and local maximization. The novelty of this approach resides in the mixing of ideas coming both from perceptual grouping and directional recursive linear filtering. We obtain new edge operators enabling very precise detection of edge points which are involved in large structures. This detector has been tested successfully on various image types presenting difficult problems for classical edge detection methods.

Thin Nets Extraction Using a Multi-scale Approach

Thin nets are the lines where the grey level function is locally extremum in a given direction. Recently, we have shown that it is possible to characterize the thin nets using differential properties of the image surface. However, the method failed when these structures present different widths. In this paper we show that the extraction process of the thin nets, having different width, requires a multi-scale analysis of the image. To design the fusion process of the multi-scale information, we will study the behavior of the differential properties of the image surface, in particular the curvatures, in scale space. We illustrate the efficiency of the proposed multi-scale approach by extracting roads and blood vessels of different widths in satellite and medical images.