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.

Useful for human visual perception, edge detection remains a crucial stage in numerous image processing applications. One of the most challenging goals in contour detection is to operate algorithms that can process visual information as humans require. To ensure that an edge detection technique is reliable, it needs to be rigorously assessed before being used in a computer vision tool. This assessment corresponds to a supervised evaluation process to quantify differences between a reference edge map and a candidate, computed by a performance measure/criterion. To achieve this task, a supervised evaluation computes a score between a ground truth edge map and a candidate image. This paper presents a survey of supervised edge detection evaluation methods. Considering a ground truth edge map, various methods have been developed to assess a desired contour. Several techniques are based on the number of false positive, false negative, true positive and/or true negative points. Other methods strongly penalize misplaced points when they are outside a window centered on a true or false point. In addition, many approaches compute the distance from the position where a contour point should be located. Most of these edge detection assessment methods will be detailed, highlighting their drawbacks using several examples. In this study, a new supervised edge map quality measure is proposed. The new measure provides an overall evaluation of the quality of a contour map by taking into account the number of false positives and false negatives, and the degrees of shifting. Numerous examples and experiments show the importance of penalizing false negative points differently than false positive pixels because some false points may not necessarily disturb the visibility of desired objects, whereas false negative points can significantly change the aspect of an object. Finally, an objective assessment is performed by varying the hysteresis thresholds on contours of real images obtained by filtering techniques. Theoretically, by varying the hysteresis thresholds of the thin edges obtained by filtering gradient computations, the minimum score of the measure corresponds to the best edge map, compared to the ground truth. Twenty-eight measures are compared using different edge detectors that are robust or not robust regarding noise. The scores of the different measures and different edge detectors are recorded and plotted as a function of the noise level in the original image. The plotted curve of a reliable edge detection measure must increase monotonously with the noise level and a reliable edge detector must be less penalized than a poor detector. In addition, the obtained edge map tied to the minimum score of a considered measure exposes the reliability of an edge detection evaluation measure if the edge map obtained is visually closer to the ground truth or not. Hence, experiments illustrate that the desired objects are not always completely visible using ill-suited evaluation measure.

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In recent years, information security has received a great deal of attention. To give an example, steganography techniques are used to communicate in a secret and invisible way. Digital color images have become a good medium for digital steganography because of their easy manipulation as carriers via Internet, e‐mails, or used on websites. The main goal of steganalysis is to detect the presence of hidden messages in a digital media. The proposed method is a further extension of the authors' previous work: steganalysis based on color feature correlation and machine learning classification. Fusing features with those obtained from color‐rich models allows increasing the detectability of hidden messages in the color images. Our new proposition uses two types of features, computed between color image channels. The first type of feature reflects local Euclidean transformations, and the second one reflects mirror transformations. These geometric measures are obtained by the sine and cosine of gradient angles between all the color channels. Features are extracted from co‐occurrence correlation matrices of measures. We demonstrate the efficiency of the proposed framework on three steganography algorithms designed to hide messages in images represented in the spatial domain: S‐UNIWARD, WOW, and Synch‐HILL. For each algorithm, we applied a range of different payload sizes. The efficiency of the proposed method is demonstrated by the comparison with the previous authors work and the spatial color‐rich model and color filter array‐aware features for steganalysis. Copyright © 2016 John Wiley & Sons, Ltd.

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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.

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.

Easy to use, oriented half kernels are reliable in image analysis. These thin filters, rotated in all the desired directions are useful to detect edges, or extract precisely their orientations, even concerning highly noisy images. Usually, the filtering process corresponds to convolutions with Gaussians and their derivatives. Other filters exist and can be implemented in order to build half kernels. However, functions used for the smoothing and derivative parts have not been studied in depth. The goal of this paper is to evaluate different types of half filters as a function of the noise level. The studied kernels have the same spatial support, enabling easier comparisons. To address the robustness of the studied filters against noise, the image quality is gradually worsened. Then, their performances are compared through objective evaluations of both segmentation and gradient direction.