Preface Linear filters have been used as the primary tools in signal and image processing. They exhibit many desirable properties, such as the superposition, which makes them easy to design and implement. Furthermore, they are the best solution in additive Gaussian noise suppression. However, when the noise is not additive or when there exist system non-linearities linear filters are not adequa...

This paper considers the problem of finding facial features in frontal color images. For a given image, our method at first detects skin regions on it using color segmentation in a 2D plane of selected color space (HSV, YES, YCrCb or normalized RGB), and after that generates face candidates. Then, we apply methods of fuzzy mathematical morphology to obtain face candidates in order to find a par...

In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathe...

In many domains of information processing, both vagueness, or imprecision, and bipolarity, encompassing positive and negative parts of information, are core features of the information to be modeled and processed. This led to the development of the concept of bipolar fuzzy sets, and of associated models and tools. Here we propose to extend these tools by defining algebraic relations between bip...

We present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively d...

In many domains, it is important to be able to deal with bipolar information [5]. Positive information represents what is granted to be possible (for instance because it has already been observed or experienced), while negative information represents what is impossible (or forbidden, or surely false). This domain has recently motivated work in several directions. In particular, fuzzy and possib...

Mobile robots must represent and reason about spatial knowledge acquired from sensor data which are inherently approximate and uncertain. While techniques based on fuzzy sets are increasingly used in this domain, the use of these techniques often rests on intuitive grounds. In this paper, we show that fuzzy mathematical morphology, a theory often used in image processing but mostly ignored in t...

The object of this paper is to elaborate an automatic system in order to detect suspect zones or tumors in medical image especially brain ones. The purpose is to define with precision the existence of different kinds of lesions using a hybrid system which combines Fuzzy Neural Networks and Expert System. After getting regions features by regions growing or mathematical morphology, the fuzzy neu...

Spatial directional relations, like "north of," play an important role in the modeling of the environment by an autonomous robot. We propose an approach to represent spatial relations grounded in fuzzy set theory and fuzzy mathematical morphology. We show how this approach can be applied to robot maps, and suggest that these relations can be used for self-localization and for reasoning about th...