Baptiste Magnier

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.

Texture removal by pixel classification using a rotating filter

In this paper, we present a new method for removing texture in images using a smoothing rotating filter. From this filter, a bank of smoothed images provides pixel signals able to classify a pixel as a texture pixel, a homogenous region pixel or an edge pixel. Then, we introduce a new method for anisotropic diffusion which controls accurately the diffusion near edge and corner points and diffuses isotropically inside textured regions. Several results applied on real images and a comparison with anisotropic diffusion methods show that our model is able to remove the texture and control the diffusion.

Color Image Stegananalysis Using Correlations between RGB Channels

Digital images, especially color images, are very widely used, as well as traded via Internet, e-mail and posting on websites. Images have a large size which allows embedding secret messages of large size, so they are a good medium for digital steganography. The main goal of steganalysis is to detect the presence of hidden messages in digital media. In this paper, we propose a steganalysis method based on color feature correlation and machine learning classification. Fusing features with features obtained from color-rich models allows to increase the detect ability of hidden messages in the color images. Our novel proposition uses the correlation between different channels of color images. Features are extracted from the channel correlation and co-occurrence correlation. In this work, all stego images are created with a range of different payload sizes using two steganography S-UNIWARD and WOW algorithms. To validate the proposed method, his efficiency is demonstrated by comparison with color rich model steg analysis.

Evolution of image regularization with PDEs toward a new anisotropic smoothing based on half kernels

This paper is dedicated to a new anisotropic diffusion approach for image regularization based on a gradient and two diffusion directions obtained from half Gaussian kernels. This approach results in smoothing an image while preserving edges. From an anisotropic edge detector, built of half Gaussian derivative kernels, we introduce a new smoothing method preserving structures which drives the diffusion function of the angle between the two edge directions and the gradient value. Due to the two directions diffusion used in the control function, our diffusion scheme enables to preserve edges and corners, contrary to other anisotropic diffusion methods. Moreover, parameters of the Gaussian kernel can be tuned to be sufficiently thin extracting precisely edges whereas its length allows detecting in contour orientations which leads to a coherent image regularization. Finally, we present some experimental results and discuss about the choice of the different parameters.

A quantitative error measure for the evaluation of roof edge detectors

Crest line extraction remains a hard task in image processing. Indeed, these roof edges represent narrow edges on the image surface and whatever undesirable pixel close to or on the crest line may disturb the detection. This communication presents a new crest line detection overall evaluation. Comparing the ground truth contour image and the candidate crest line image, the proposed algorithm is based upon a new criterion that take into account the list of ground truth, the recall and their associated spacial nearness. Doubtlessly, an efficient evaluation penalizes a misplaced edge point proportionally to the distance to the true contour. This quantitative performance evaluation proves its efficiency on several crest line images of different types, bringing a favorable indicator for tow closest edge images or a poor in the presence of a degraded/distorted candidate edge image.

Multi-scale crest line extraction based on half Gaussian Kernels

Crest line extraction has always been a challenging task in image processing and its applications. It is possible to detect ridges and valleys in images using second order filters. In order to estimate crest lines of variable widths, a multi-scale analysis of the image is required. In this paper we propose a new ridge/valley detection method in images based on the difference of rotating Gaussian semi filters adapted in a multi-scale process. Due to the directional filters, we obtain a new ridge/valley anisotropic detector enabling very precise detection of ridge/valley of varied widths. Moreover, as the detector filters compute the two directions of crest lines, even highly bended crest lines are correctly extracted. Numerical comparisons with other oriented Gaussian filters and results on real images validate the interest of this method.

Ridges and valleys detection in images using difference of rotating half smoothing filters

In this paper we propose a new ridge/valley detection method in images based on the difference of rotating Gaussian semi filters. The novelty of this approach resides in the mixing of ideas coming both from directional filters and DoG method. We obtain a new ridge/valley anisotropic DoG detector enabling very precise detection of ridge/valley points. Moreover, this detector performs correctly at crest lines even if highly bended, and is precise on junctions. This detector has been tested successfully on various image types presenting difficult problems for classical ridges/valleys detection methods.

Color Image Steganalysis Based On Steerable Gaussian Filters Bank

This article deals with color images steganalysis based on machine learning. The proposed approach enriches the features from the Color Rich Model by adding new features obtained by applying steerable Gaussian filters and then computing the co-occurrence of pixel pairs. Adding these new features to those obtained from Color-Rich Models allows us to increase the detectability of hidden messages in color images. The Gaussian filters are angled in different directions to precisely compute the tangent of the gradient vector. Then, the gradient magnitude and the derivative of this tangent direction are estimated. This refined method of estimation enables us to unearth the minor changes that have occurred in the image when a message is embedded. The efficiency of the proposed framework is demonstrated on three stenographic algorithms designed to hide messages in images: S-UNIWARD, WOW, and Synch-HILL. Each algorithm is tested using different payload sizes. The proposed approach is compared to three color image steganalysis methods based on computation features and Ensemble Classifier classification: the Spatial Color Rich Model, the CFA-aware Rich Model and the RGB Geometric Color Rich Model.

RSD-HoG: A New Image Descriptor

In this paper we propose a novel local image descriptor called RSD-HoG. For each pixel in a given support region around a key-point, we extract the rotation signal descriptor(RSD) by spinning a filter made of oriented anisotropic half-gaussian derivative convolution kernel. The obtained signal has extremums at different orientations of the filter. These characteristics are combined with a HoG technique, to obtain a novel descriptor RSD-HoG. The obtained descriptor has rich, discriminative set of local information related to the curvature of the image surface. With these rich set of features, our descriptor finds applications in various branches of computer vision. For evaluation, we have used the standard Oxford data set which has rotation, brightness, illumination, compression and viewpoint changes. Extensive experiments on these images demonstrates that our approach performs better than many state of the art descriptors such as SIFT, GLOH, DAISY and PCA-SIFT.

Edge detection: a review of dissimilarity evaluations and a proposed normalized measure

In digital images, edges characterize object boundaries, so edge detection remains a crucial stage in numerous applications. To achieve this task, many edge detectors have been designed, producing different results, with various qualities of segmentation. Indeed, optimizing the response obtained by these detectors has become a crucial issue, and effective contour assessment assists performance evaluation. In this paper, several referenced-based boundary detection evaluations are detailed, pointing out their advantages and disadvantages, theoretically and through concrete examples of image edges. Then, a new normalized supervised edge map quality measure is proposed, comparing a ground truth contour image, the candidate contour image and their associated spatial nearness. The effectiveness of the proposed distance measure is demonstrated theoretically and through several experiments, comparing the results with the methods detailed in the state-of-the-art. In summary, compared to other boundary detection assessments, this new method proved to be a more reliable edge map quality measure.