In this paper, we present a method for binary image comparison. For binary images, intensity information is poor and shape extraction is often difficult. Therefore binary images have to be compared without using feature extraction. Due to the fact that different scene patterns can be present in the images, we propose a modified Hausdorff distance (HD) locally measured in an adaptive way. The resulting set of measures is richer than a single global measure. The local HD measures result in a local-dissimilarity map (LDMap) including the dissimilarity spatial layout. A classification of the images in function of their similarity is carried out on the LDMaps using a support vector machine. The proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency. For the first approach, conspicuous features must be captured in the signature of each image in order to be as discriminating as possible in some user defined way [6]. The choice of the signature attributes is not always easy and depends on the processed images [5,1]. In case that there are several patterns in the images, a segmentation is often necessary to compare locally the attributes. For binary images, object shapes cannot always be precisely identified, so it is difficult to find the features related to shapes. Moreover, the texture attribute is also difficult to extract (binary images are not always * Corresponding author. textured), and the color attribute is poor (only black and white colors). Thus, the second approach, a straight image comparison, seems adapted in the case of binary images. In our work, the measure is windowed and the window size is adjusted so as to measure exclusively the local dissimilarity. The obtained result is composed of a set of local measures covering the image. The following state of the art gives a general presentation of dissimilarity measures and tends to choose the Hausdorff distance (HD). The computation of windowed measure all over the images is time consuming, nevertheless, for the HD, the algorithm comes down to a formula based on the distance transform (DT) of each image which accelerates the computation.The paper is organized as follows: firstly we present an overview of the image retrieval in Section 1.1 and then focus on the HD in Section 1.2. Secondly the notion of local HD is introduced and its properties are exposed in Sections 2 and 3. This allows us to determine automatically the size of the local HD's window in function of the dissimilarity, as shown in Section 4. Then it is shown how the algorithm may be reduced to a formula based on the DT. The map of local HD defined without parameters is presented in Sections 4 and 5. Finally, qualitative results and an application to image classification are presented in Section 6 before concluding.Please cite this article as:Ḃaudrier, et al., Binary-image comparison with local-dissimilarity quantification, Pattern Recognition (2007),

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This paper presents a quantification method which can be used to quantify the evolution of a brain tumor with time. From two segmented volumes, a Local Distance Volume (LDV) based on Hausdorff Distance is computed to show the true physical local distances between them. In the case of tracking a tumor volume during a therapeutic treatment, local variations can thus be shown by the LDV in particular where the tumor has regressed and where it has grown. This information can help radiologists to adapt the current treatment.

This article focuses on the classical problem of the control of information loss during the digitization step. The properties proposed in the literature rely on smoothness hypotheses that are not satised by the curves including angular points. The notion of turn introduced by Milnor in the article On the Total Curvature of Knots generalizes the notion of integral curvature to continuous curves. Thanks to the turn, we are able to dene the local turn-boundedness. This promising property of curves does not require smoothness hypotheses and shares several properties with the par(r)-regularity, in particular well-composed digitizations. Besides, the local turn-boundedness enables to constrain spatially the continuous curve as a function of its digitization.