Jose Luis Lisani

Shape preserving local histogram modification

A novel approach for shape preserving contrast enhancement is presented in this paper. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based both on the grey-values and spatial relations between pixels in the image, and following mathematical morphology, constitute the basic objects in the scene. We give examples for both grey-value and color images.

A Nonparametric Approach for Histogram Segmentation

In this work, we propose a method to segment a 1-D histogram without a priori assumptions about the underlying density function. Our approach considers a rigorous definition of an admissible segmentation, avoiding over and under segmentation problems. A fast algorithm leading to such a segmentation is proposed. The approach is tested both with synthetic and real data. An application to the segmentation of written documents is also presented. We shall see that this application requires the detection of very small histogram modes, which can be accurately detected with the proposed method

ON THE THEORY OF PLANAR SHAPE

One of the aims of computer vision in the past 30 years has been to recognize shapes by numerical algorithms. Now, what are the geometric features on which shape recognition can be based? In this paper, we review the mathematical arguments leading to a unique definition of planar shape elements. This definition is derived from the invariance requirement to not less than five classes of perturbations, namely noise, affine distortion, contrast changes, occlusion, and background. This leads to a single possibility: shape elements as the normalized, affine smoothed pieces of level lines of the image. As a main possible application, we show the existence of a generic image comparison technique able to find all shape elements common to two images.

Shape preserving local contrast enhancement

A novel approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based on the image grey-values and spatial relations between its pixels. Following mathematical morphology, these constitute the basic objects in the scene. We give examples for both grey-valued and color images.

Simplest Color Balance

In this paper we present the simplest possible color balance algorithm. The assumption underlying this algorithm is that the highest values of R, G, B observed in the image must correspond to white, and the lowest values to obscurity. The algorithm simply stretches, as much as it can, the values of the three channels Red, Green, Blue (R, G, B), so that they occupy the maximal possible range [0, 255] by applying an affine transform ax+b to each channel. Since many images contain a few aberrant pixels that already occupy the 0 and 255 values, the proposed method saturates a small percentage of the pixels with the highest values to 255 and a small percentage of the pixels with the lowest values to 0, before applying the affine transform. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article.

Affine Invariant Mathematical Morphology Applied to A Generic Shape Recognition Algorithm

We design a generic contrast and affine invariant planar shape recognition algorithm. By generic, we mean an algorithm which delivers a list of all shapes two digital images have in common, up to any affine transform or contrast change. We define as“shape elements” all pieces of level lines of the image. Their number can be drastically reduced by using affine and contrast invariant smoothing Matheron operators, which we describe as alternate affine erosions-dilations. We then discuss an efficient local encoding of the shape elements. We finally show experiments. Applications aimed at include image registration, image indexing, optical flow.

Color image segmentation using acceptable histogram segmentation

In this paper, a new method for the segmentation of color images is presented. This method searches for an acceptable segmentation of 1D-histograms, according to a “monotone” hypothesis. The algorithm uses recurrence to localize all the modes in the histogram. The algorithm is applied on the hue, saturation and intensity histograms of the image. As a result, an optimal and accurately segmented image is obtained. In contrast to previous state of the art methods uses exclusively the image color histogram to perform segmentation and no spatial information at all.

Patch-Based Video Denoising With Optical Flow Estimation

A novel image sequence denoising algorithm is presented. The proposed approach takes advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal average. The use of motion compensation by regularized optical flow methods permits robust patch comparison in a spatiotemporal volume. The use of principal component analysis ensures the correct preservation of fine texture and details. An extensive comparison with the state-of-the-art methods illustrates the superior performance of the proposed approach, with improved texture and detail reconstruction.

Color and Contrast Enhancement by Controlled Piecewise Affine Histogram Equalization

This paper presents a simple contrast enhancement algorithm based on histogram equalization (HE). The proposed algorithm performs a piecewise affine transform of the i ntensity levels of a digital image such that the new histogram function will be approximately uniform (as with HE), but where the stretching of the range is locally controlled to avoid brutal noise enhancement. We call this algorithm Piecewise Affine Equalization (PAE). Several experiments show that, in general, the new algorithm improves HE results. Source Code The proposed algorithm has been implemented in ANSI C. The source code, the code documentation, and the online demo are accessible from the web page of this article 1 .

Directional Filters for Color Cartoon+Texture Image and Video Decomposition

The decomposition of an image in a geometrical and a textural part has shown to be a challenging problem with several applications. Since the theoretical breakthrough of Y. Meyer, many variational methods and minimization techniques have been proposed for this task. This paper uses a different approach based on low/high-pass filtering with directional filters. This approach modifies the algorithm proposed in Buades et al. (IEEE Trans Image Process 19(8):1978–1986, 2010) improving its performance near image discontinuities while keeping the simplicity and rapidity of a linear model. Comparisons with variational methods illustrate the flexibility of the proposed algorithm. We illustrate how the proposed method is the only one dealing correctly with frame-by-frame video processing.