Групповой состав дизельного топлива, как фактор определяющий эффективность действия депрессорных присадок

In this paper, we present a content-based image retrieval system designed to retrieve mammographies from large medical image database. The system is developed based on breast density, according to the four categories defined by the American College of Radiology, and is integrated to the database of the Image Retrieval in Medical Applications (IRMA) project, that provides images with classification ground truth. Two-dimensional principal component analysis is used in breast density texture characterization, in order to effectively represent texture and allow for dimensionality reduction. A support vector machine is used to perform the retrieval process. Average precision rates are in the range from 83% to 97% considering a data set of 5024 images. The results indicate the potential of the system as the first stage of a computer-aided diagnosis framework

Evolutionary cellular system for invariant pattern recognition

O reconhecimento de padrões tem provocado um grande interesse nas últimas décadas. Como consequência, numerosas aplicações foram desenvolvidas. Entre as mais importantes podem-se citar as seguintes: ajuda ao diagnóstico médico, análise automática de sinais, inspeção automática de produtos industriais, sistemas de vigilância automática, busca automática de informação digitalizada, etc. A complexidade de um sistema de reconhecimento de padrões é alta, devido ao fato de que padrões reais se apresentam com grande variedade, sofrendo transformações e deformações não-lineares. Este trabalho tem como objetivo desenvolver um sistema celular evolutivo, para reconhecimento de padrões invariantes à rotação, baseado em mecanismos fundamentais de Autómatos Celulares, os quais foram usados com sucesso para modelagem e simulação de problemas complexos. O modelo proposto neste trabalho extrai eficientemente características globais invariantes à rotação de padrões, a partir das interações locais das células.Pattern recognition has provoked a great interest in the last decades. As a consequence, numerous engineering applications have been developed, such as medicai diagnosis aiding, automatic signal analysis, industrial inspection, automatic monitoring systems, automatic digital information searching, etc. The complexity of a pattern recognition system is high because real patterns present in fornis of large extent of varieties, suffering from linear transformations even nonlinear deformations. The objective of this work is to develop a evolutive cellular system for rotational invariant pattern recognition based on the fundamental mechanism of cellular automata, which has been successfully used to model and simulate complex problems. The proposed model extracts efficiently global features invariant to pattern rotation by local interactions among cellulars

Evolutionary cellular system for invariant pattern recognition

O reconhecimento de padrões tem provocado um grande interesse nas últimas décadas. Como consequência, numerosas aplicações foram desenvolvidas. Entre as mais importantes podem-se citar as seguintes: ajuda ao diagnóstico médico, análise automática de sinais, inspeção automática de produtos industriais, sistemas de vigilância automática, busca automática de informação digitalizada, etc. A complexidade de um sistema de reconhecimento de padrões é alta, devido ao fato de que padrões reais se apresentam com grande variedade, sofrendo transformações e deformações não-lineares. Este trabalho tem como objetivo desenvolver um sistema celular evolutivo, para reconhecimento de padrões invariantes à rotação, baseado em mecanismos fundamentais de Autómatos Celulares, os quais foram usados com sucesso para modelagem e simulação de problemas complexos. O modelo proposto neste trabalho extrai eficientemente características globais invariantes à rotação de padrões, a partir das interações locais das células.Pattern recognition has provoked a great interest in the last decades. As a consequence, numerous engineering applications have been developed, such as medicai diagnosis aiding, automatic signal analysis, industrial inspection, automatic monitoring systems, automatic digital information searching, etc. The complexity of a pattern recognition system is high because real patterns present in fornis of large extent of varieties, suffering from linear transformations even nonlinear deformations. The objective of this work is to develop a evolutive cellular system for rotational invariant pattern recognition based on the fundamental mechanism of cellular automata, which has been successfully used to model and simulate complex problems. The proposed model extracts efficiently global features invariant to pattern rotation by local interactions among cellulars

Abnormal event detection in video using motion and appearance information

This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2018.Trabajo de investigació

An interactive video content-based retrieval system

International audienceThe actual generation of video search engines offers low-level abstractions of the data while users seek for high-level semantics. The main challenge in video retrieval remains bridging the semantic gap. Thus, the effectiveness of video retrieval is based on the result of the interaction between query selection and a goal-oriented human user. The system exploits the human capability for rapidly scanning imagery augmenting it with an active learning loop, which tries to always present the most relevant material based on the current information. We describe in this paper, a machine learning system for interactive video retrieval. The core of this system is a kernel-based SVM classifier. The video retrieval uses the core as an active learning classifier. We perform an experiment against the 2005 NIST TRECVID benchmark in the high-level task

Brazilian license plate detection using histogram of oriented gradients and sliding windows.

Due to the increasingly need for automatic traffic monitoring, vehicle license plate detection is of high interest to perform automatic toll collection, traffic law enforcement, parking lot access control, among others. In this paper, a sliding window approach based on Histogram of Oriented Gradients (HOG) features is used for Brazilian license plate detection. This approach consists in scanning the whole image in a multiscale fashion such that the license plate is located precisely. The main contribution of this work consists in a deep study of the best setup for HOG descriptors on the detection of Brazilian license plates, in which HOG have never been applied before. We also demonstrate the reliability of this method ensured by a recall higher than 98% (with a precision higher than 78%) in a publicly available data set

Algorithms for hierarchical segmentation based on the Felzenszwalb-Huttenlocher dissimilarity

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. Guimarães et al. proposed in 2012 a hierarchical graph-based image segmentation method relying on a criterion popularized by Felzenszwalb and Huttenlocher in 2004, hence hierarchizing the popular Felzenszwalb-Huttenlocher method. However, Guimarães et al. did not provide an algorithm to compute the proposed hierarchy. We propose a series of algorithms to compute the result of this hierarchical graph-based image segmentation method. For an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than four hour

Hierarchical segmentation from a non-increasing edge observation attribute

International audienceHierarchical image segmentation provides region-oriented scale-spaces: sets of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph-based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. It computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should be merged according to the dissimilarity. We provide an explicit definition of the (edge-) observation attribute and Boolean criterion which are at the basis of this method and show that they are not increasing. Then, we propose an algorithm to compute all the scales for which the criterion holds true. Finally, we propose new methods to regularize the observation attribute and criterion and to set up the observation scale value of each edge of a graph, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy. Assessments on Pascal VOC 2010 and 2012 show that these strategies lead to better segmentation results than the ones obtained with the original HGB method

Efficient algorithms for hierarchical graph-based segmentation relying on the Felzenszwalb-Huttenlocher dissimilarity

International audienceHierarchical image segmentation provides a region-oriented scale-space, {\em i.e.}, a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb-Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimar\~aes {\em et al.} proposed in 2012 a method for hierarchizing the popular Felzenszwalb-Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This article is devoted to provide a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 $\times$ 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than four hours