Classifying image analysis techniques from their output

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

Characterization of dynamic speckle sequences using principal component analysis and image descriptors

Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological or physical process taking place in the sample. Very recently, it has been shown that principal component analysis is able to split the original data set in a collection of classes. These classes can be related with the dynamic of the observed phenomena. At the same time, statistical descriptors of biospeckle images have been used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, principal component analysis requires longer computation time but the results contain more information related with spatial-temporal pattern that can be identified with physical process. This contribution merges both descriptions and uses principal component analysis as a pre-processing tool to obtain a collection of filtered images where a simpler statistical descriptor can be calculated. The method has been applied to slow-varying biological and industrial processes

Low spatial frequency filtering modulates early brain processing of affective complex pictures

Recent research on affective processing has suggested that low spatial frequency information of fearful faces provide rapid emotional cues to the amygdala, whereas high spatial frequencies convey fine-grained information to the fusiform gyrus, regardless of emotional expression. In the present experiment, we examined the effects of low (LSF, <15 cycles/image width) and high spatial frequency filtering (HSF, >25 cycles/image width) on brain processing of complex pictures depicting pleasant, unpleasant, and neutral scenes. Event-related potentials (ERP), percentage of recognized stimuli and response times were recorded in 19 healthy volunteers. Behavioral results indicated faster reaction times in response to unpleasant LSF than to unpleasant HSF pictures. Unpleasant LSF pictures and pleasant unfiltered pictures also elicited significant enhancements of P1 amplitudes at occipital electrodes as compared to neutral LSF and unfiltered pictures, respectively; whereas no significant effects of affective modulation were found for HSF pictures. Moreover, mean ERP amplitudes in the time between 200 and 500ms post-stimulus were significantly greater for affective (pleasant and unpleasant) than for neutral unfiltered pictures; whereas no significant affective modulation was found for HSF or LSF pictures at those latencies. The fact that affective LSF pictures elicited an enhancement of brain responses at early, but not at later latencies, suggests the existence of a rapid and preattentive neural mechanism for the processing of motivationally relevant stimuli, which could be driven by LSF cues. Our findings confirm thus previous results showing differences on brain processing of affective LSF and HSF faces, and extend these results to more complex and social affective pictures.