Julio C. Jacobo

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain optimal results by operating in a diametrically opposite way, i.e., a so-called “anti-Bayesian” manner. Indeed, we shall show the completely counter-intuitive result that by working with a very few (sometimes as small as two) points distant from the mean, one can obtain remarkable classification accuracies. Further, if these points are determined by the Order Statistics of the distributions, the accuracy of our method, referred to as Classification by Moments of Order Statistics (CMOS), attains the optimal Bayes’ bound! This claim, which is totally counter-intuitive, has been proven for many uni-dimensional, and some multi-dimensional distributions within the exponential family, and the theoretical results have been verified by rigorous experimental testing. Apart from the fact that these results are quite fascinating and pioneering in their own right, they also give a theoretical foundation for the families of Border Identification (BI) algorithms reported in the literature.

Detection and tracking of coronal mass ejections based on supervised segmentation and level set

Coronal mass ejection (CME) events refer to the appearance of a new, discrete, white-light feature (with outward velocity) in a coronagraph. The huge amount of data provided by the pertinent instruments onboard the Solar and Heliospheric Observatory (SOHO) and, most recently, the Solar Terrestrial Relations Observatory (STEREO) makes the human-based detection of such events excessively time consuming. Although several algorithms have been proposed to address this issue, there is still lack of universal consensus about their reliability. This work presents a novel method for the detection and tracking of CMEs as recorded by the LASCO instruments onboard SOHO. The algorithm we developed is based on level set and region competition methods, the CMEs texture being characterized by their co-occurrence matrix. The texture information is introduced in the region competition motion equations, and in order to evolve the curve, a fast level set implementation is used.

A decision step for Shape Context matching

This work presents a novel contribution in the field of shape recognition, in general, and in the Shape Context technique, in particular. We propose to address the problem of deciding if two shape context descriptors match or not using an a contrario approach. Its key advantage is to provide a measure of the quality of each match, which is a powerful tool for later recognition processes. We tested the proposed combination of Shape Context and the a contrario framework in character recognition from license plate images.

Randomized Face Recognition on Partially Occluded Images

In this work we propose a new method for face recognition that successfully handles occluded faces. We propose an innovative improvement that allows to detect and discard occluded zones of the face, thus making recognition more robust in the presence of occlusion. We provide experimental results that show that the proposed method performs well in practice.

Multiple clues for license plate detection and recognition

This paper addresses a license plate detection and recognition (LPR) task on still images of trucks. The main contribution of our LPR system is the fusion of different segmentation algorithms used to improve the license plate detection. We also compare the performance of two kinds of classifiers for optical character recognition (OCR): one based on the a contrario framework using the shape contexts as features and the other based on a SVM classifier using the intensity pixel values as features.

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.

Improvement in SAR Image Classification using Adaptive Stack Filters

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters is evaluated for the classification of synthetic aperture radar (SAR) images, which are affected by speckle noise. With this aim it was carried out experiment in which simulated and real images are generated and then filtered with a stack filter trained with one of them. The results of their maximum likelihood classification are evaluated and then are compared with the results of classifying the images without previous filtering.

Assessment of SAR image filtering using adaptive stack filters

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is then filtered by a Boolean function, which characterizes the filter. Adaptive stack filters can be designed to be optimal; they are computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work we study the performance of adaptive stack filters when they are applied to Synthetic Aperture Radar (SAR) images. This is done by evaluating the quality of the filtered images through the use of suitable image quality indexes and by measuring the classification accuracy of the resulting images.

SAR Image Segmentation Using Level Sets and Region Competition under the

Synthetic Aperture Radar (SAR) images are dificult to segment due to their characteristic noise, called speckle , which is multiplicative, non-gaussian and has a low signal to noise ratio. In this work we use the

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