Transactions on Mass Data Analysis of Images and Signals Vol. 8, vol. 1 (2017) 17-30 © 2017, ibai-publishing, ISSN: 1868-6451, ISBN: 978-3-942952-55-2 Online ISSN 2509-9353 Verification of Hypotheses generated by Case-Based Reasoning Object Matching Petra Perner Institute of Computer Vision and Applied Computer Sciences, IBaI PSF 301114, 04251 Leipzig, Germany pperner@ibai-institut.de, www.ibai-institut.de Abstract. Case-based reasoning object-matching is the methods at choice when the objects can be identified by case models. The result of the matching process will be many hypotheses for the true shape of the objects. These hypothesizes must be verified in a hypothesis-verification process. We review in this paper what has been done so far and present our hypothesis-verification rules. The rules get evaluated and the results are shown in the images and the achievements are discussed. We consider two different hypothesis-verification rules, one is based on set-theory and the other one is based on statistical measures. Finally, we describe the results achieved so far and give an outlook about further work. Keywords: Case-Based Reasoning Object-Matching, Hypothesis-Test Verification, Set Theory, Statistical Measures 1 Introduction Case-based object-matching [1] is the methods at choice when the objects can be identified by case models. These case models can be learnt from the raw data by case mining [2]. For the case-matching procedure, we need a proper similarity measure that depends of the case model description. In our case, the case models are object contours such as round, ellipse-like, or more fuzzy-like geometric figures. The chosen similarity measure in this work is the cosine-similarity measure [3]. The properties of this similarity measure have been described in detail in [3]. The case matcher takes the case models and matches them against the objects in the image. In case the similarity measure is high the found contour will be marked in the image. Often the matcher does not bring out only one contour for an object, instead of the matcher fires several times at slightly different spatial positions in the image for the same object. These multiple matches must be evaluated after the matching in a hypothesis verification procedure.