Article history: Received 30 September 2007 Received in revised form 4 June 2008 Available online 23 May 2009

palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

Three new methods are developed to generate neutral spatial models for pattern recognition on raster data. The first method employs Genetic Programming (GP), the second Sequential Gaussian Simulation (SGS), and the third Conditional Pixel Swapping (CPS) in order to produce sets of ”neutral images” that provide a probabilistic assessment of how unlikely an observed spatial pattern on a target im...

This paper describes a face recognition method based on combining two complementary matching algorithms, one is a single-image matching algorithm and the other is a sequential-image matching algorithm. The sequential-image matching algorithm distinguishes each person by using the derived features from a set of sequential images of the same person, and the derived features meaningfully correspon...

This paper describes a knowledge based system for automatic parallelization of a wide class of sequential numeric codes operating on vectors and dense matrices and for execution on distributed memory message passing multiprocessors Its main feature is a fast and powerful pattern recognition tool that locally identi es frequently occurring computations and programming concepts in the source code...

Analyzing information is recently becoming much more important than ever, as it is produced massively in every area. In the past years, data streams became more and more important and so were algorithms that can mine hidden patterns out of those non static data bases. Those algorithms can also be used to simulate processes and to find important information step by step. The translation of an En...

The need for the study of dynamic and evolutionary settings made time a major dimension when it comes to data analytics. From business to health applications, being able to understand temporal patterns of customers or patients can determine the ability to adapt to future changes, optimizing processes and support other decisions. In this context, different approaches to Temporal Pattern Mining h...

Analyzing multimedia data is a challenging problem due to the quantity and complexity of such data. Mining for frequently recurring patterns is a task often ran to help discovering the underlying structure hidden in the data. In this article, we propose audio data symbolization and sequential pattern mining methods to extract patterns from audio streams. Experiments show that this task is hard ...

An increasingly relevant set of tasks, such as the discovery of biclusters with order-preserving properties, can be mapped as a sequential pattern mining problem on data with item-indexable properties. An item-indexable database, typically observed in biomedical domains, does not allow item repetitions per sequence and is commonly dense. Although multiple methods have been proposed for the effi...