## 1 Introduction
Halos are unresolved images with reverse contrast, and shading-off is a contrast-decreasing effect from the edge of the specimen towards the center of it [10]. DIC microscopy is also a popular method for improving the contrast of unstained specimens. DIC is absent of the halo effect found in phase contrast microscopy producing pseudo-relief images that can be understood as the derivative of the optical path length defined as product of the index of refraction times the specimen thickness. In dark field microscopy, the specimen is illuminated with a hollow cone of light, which is too wide to enter the objective lens. Dark field is a suitable modality for diatom visualization that can be a substitute of phase contrast or DIC in many cases, although both are out of the scope of this work. Scanning electron microscopy is another suitable modality, especially in taxonomy for revealing structural details [11], which is also out of the scope of this paper. On the other hand, most of the diatom databases that are publicly available use brightfield microscopy. Other difficulties attached to microscopy are: images partially focused and multiple orientations (views). Both are related to the projection of 3D objects into 2D images. The main challenges faced by automatic identification and classification methods are due not only to the high number of species to recognize, but to the great similarities between them and even the presence of polymorphisms within species. In some cases, analysis is simply unfeasible due to the huge amount of information and images involved. Advances in digital microscopy and image analysis systems offer a potentially advantageous solution compared to manual methods of counting and classification. Before going further, it is worth pointing out the difference between classification and identification in terms of biology and pattern recognition terminology. In biology, it may be said that the term identification answers the question: 'What is the name of the taxon in front of me?'. However,classification answers questions of the sort: How is this taxon related to other taxa? According to the pattern recognition terminology, a class is a set of objects that within a given context is recognized as similar. Such a class has usually a unique name, the class name. The individual objects within a class have a label that refers to this name. Additionally, classification is the assignment of a class name to an object by evaluating a trained classifier for that object. Therefore, in this study, our objects or classes are the diatoms, and we will classify them to give them a label with their taxon name. Henceforth, a diatom taxa will be referred to as a class. A good review of morphometric methods for shape analysis and landmark-based analysis used in diatom research is presented in [PERSON] et al. [12]. Another attempt to describe the morphology and geometry of diatoms is the work of [PERSON] et al. [13], who developed a system that allows the segmentation and feature extraction from diatom contours, although it does not provide textural information about the frustule. As mentioned by [PERSON] et al. [12], two areas of study may be identified with regard to the methods used in diatom research, namely shape analysis and pattern recognition. We will focus on pattern recognition or machine learning methods, but notice that the traditional machine learning methods are based on a previous definition of a set of features describing the objects to be classified that may or may not have a biologically-meaningful interpretation. Methods for diatom detection and identification have been studied in [PERSON] et al., 1979 [14], [PERSON] et al., 1996 [15], [PERSON], 1998 [16], [PERSON] 2001 [17]. [PERSON] et al. [15] have proposed some diatom identification methods based on coherent optics and holography. However, such work did not have any impact on diatom research mainly due to the fact that the identification system was too specialized and probably too expensive to be used by a diatomist community [18]. [PERSON] et al.