Fig 1 - uploaded by Gerardo Reyes Salgado
Content may be subject to copyright.
Source publication
Summary We present the results for the study and classification of urine sediments and coproparasitoscopic specimens using neural networks. This method has the additional advantage of taking into account the internal geometry of certain structures and to classify them according to certain parameters such as fractal dimension and entropies. In this...
Context in source publication
Context 1
... studied. A general classification of sediments is: 1) epithelial cells (renal tubular, transitional, scamous); 2) crystals (triple phosphate, Uric Acid, Calcium Carbonate and oxalate); 3) red blood cells (non- glomerular, crenated and intact); 4) casts (hyaline, granular, red blood cell, white blood cell, waxy); 5) yeast and bacteria, 6) others. Fig. 1 shows some examples: a) eritrocytes, b) uric acid crystal, c) cast. ...
Citations
... This means that fractal parameters can be calculated by three means. Over the image, with the projected structure, in which case, changes in such parameters with respect to the projection over the plane will be evaluated; another option is through the computer-generated moiré structure; and finally it can be calculated by using complexity considerations, that in our case will be calculating Renyi and Tsallis entropies in a way similar to the one used in a previous work [26]. In this case clusters are formed when particular portions of the face are taken, which can also be useful for making corrections in the image caused by lack of illumination or focus issues in the original image. ...
We show a new method for face recognition which combines the projection of structures with different characteristics (fringes, bars or grids, dots or speckle) over the face. These projections will then allow the creation of a computer-generated moiré pattern over which different kinds of fractal and complex geometry parameters are then measured. Such parameters will then be used as inputs for a neuro-symbolic hybrid system. Here, we analyze the incidence of some parameters on the efficience for the face recognition method.
