Colonic perforation due to fecaloma. Apropos of 3 case reports

To explore the regional differences in neuronal cytoarchitecture of human dentate nucleus, we examined first the three-dimensional structure of this nucleus with a computerized reconstruction technique, after making serial sections of the brain in seven fetuses aged from 20 to 39 weeks of gestation (WG), an infant (1-month-old) and two adults (22- and 85-year-old). The surface was broadly smooth at 20-22 weeks, but primary gyri or fissures were noticed in the rostral half of the lateral surface, earliest in its dorsal region. A small cavity (the hilus nuclei dentati) was situated in the middle of the medial surface, with four distinct margins. A great progress in gyration was noted after 22 weeks: gyri were observed over the entire surface by 28-29 weeks. Gyri were thicker in the caudal half than the rostral half both in the lateral and the medial surfaces. At this stage, the rostral margin of the hilus was partially cut off and the hilus was elongated toward the rostral tip, but its relative size appeared to be grossly equal to that at 22 weeks. The hilus began to open wider and wider after 30 weeks. Subdivision of the human dentate nucleus into two different parts (the smaller microgyric rostral part and the larger macrogyric caudal part) was accomplished by 35 weeks. We have previously, using morphometric approaches, reported that a vulnerable (or critical) period may exist during 20-30 weeks in the fetal development of the dentate nucleus. It is possible that this special ten weeks of mid-gestation may be coincident with the time of extensive growth in gyration for this nucleus. It will be necessary to sample the neurons independently from at least two different parts, as described above, to design further microscopic studies on the regional differences or on other cytological investigations.     

Automatic interpretation and coding of face images using flexiblemodels

Face images are difficult to interpret because they are highly variable. Sources of variability include individual appearance, 3D pose, facial expression, and lighting. We describe a compact parametrized model of facial appearance which takes into account all these sources of variability. The model represents both shape and gray-level appearance, and is created by performing a statistical analysis over a training set of face images. A robust multiresolution search algorithm is used to fit the model to faces in new images. This allows the main facial features to be located, and a set of shape, and gray-level appearance parameters to be recovered. A good approximation to a given face can be reconstructed using less than 100 of these parameters. This representation can be used for tasks such as image coding, person identification, 3D pose recovery, gender recognition, and expression recognition. Experimental results are presented for a database of 690 face images obtained under widely varying conditions of 3D pose, lighting, and facial expression. The system performs well on all the tasks listed above     

Neural network methods for one-to-many multi-valued mapping problems

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems.     

Toward automatic simulation of aging effects on face images

The process of aging causes significant alterations in the facial appearance of individuals. When compared with other sources of variation in face images, appearance variation due to aging displays some unique characteristics. Changes in facial appearance due to aging can even affect discriminatory facial features, resulting in deterioration of the ability of humans and machines to identify aged individuals. We describe how the effects of aging on facial appearance can be explained using learned age transformations and present experimental results to show that reasonably accurate estimates of age can be made for unseen images. We also show that we can improve our results by taking into account the fact that different individuals age in different ways and by considering the effect of lifestyle. Our proposed framework can be used for simulating aging effects on new face images in order to predict how an individual might look like in the future or how he/she used to look in the past. The methodology presented has also been used for designing a face recognition system, robust to aging variation. In this context, the perceived age of the subjects in the training and test images is normalized before the training and classification procedure so that aging variation is eliminated. Experimental results demonstrate that, when age normalization is used, the performance of our face recognition system can be improved