Francisco A. Pujol

Computing the Principal Local Binary Patterns for face recognition using data mining tools

Local Binary Patterns are considered as one of the texture descriptors with better results; they employ a statistical feature extraction by means of the binarization of the neighborhood of every image pixel with a local threshold determined by the central pixel. The idea of using Local Binary Patterns for face description is motivated by the fact that faces can be seen as a composition of micro-patterns which are properly described by this operator and, consequently, it has become a very popular technique in recent years. In this work, we show a method to calculate the most important or Principal Local Binary Patterns for recognizing faces. To do this, the attribute evaluator algorithm of the data mining tool Weka is used. Furthermore, since we assume that each face region has a different influence on the recognition process, we have designed a 9-region mask and obtained a set of optimized weights for this mask by means of the data mining tool RapidMiner. Our proposal was tested with the FERET database and obtained a recognition rate varying between 90% and 94% when using only 9 uniform Principal Local Binary Patterns, for a database of 843 individuals; thus, we have reduced both the dimension of the feature vectors needed for completing the recognition tasks and the processing time required to compare all the faces in the database.

Use of mathematical morphology in real‐time path planning

If an autonomous vehicle is working in an image‐based system which needs real‐time answers and whose response is critical, it will be very important to reduce computation times and, as we know, this could be performed by increasing the system parallelism. Since morphological filtering is the origin of several applications in computer vision, in this paper we are going to describe some new features to implement morphological operations by using digital signal processors. After that, an application to path planning is proposed. The standard shortest path planning problem determines a collision‐free path of shortest distance between two distinct locations in an environment scattered with obstacles. Consequently, a path planning algorithm which uses morphological operations and a DSP to process images is then described.

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates.

Using Gaussian Processes in Bayesian Robot Programming

In this paper, we present an adaptation of Gaussian Processes for learning a joint probabilistic distribution using Bayesian Programming. More specifically, a robot navigation problem will be showed as a case of study. In addition, Gaussian Processes will be compared with one of the most popular techniques for machine learning: Neural Networks. Finally, we will discuss about the accuracy of these methods and will conclude proposing some future lines for this research.

A BCD-based architecture for fast coordinate rotation

Although radix 10 based arithmetic has been gaining renewed importance over the last few years, decimal systems are not efficient enough and techniques are still under development. In this paper, an improvement of the CORDIC (coordinate rotation digital computer) method for decimal representation is proposed and applied to produce fast rotations. The algorithm uses BCD operands as inputs, combining the advantages of both decimal and binary systems. The result is a reduction of 50% in the number of iterations if compared with the original Decimal CORDIC method. Finally, we present a hardware architecture useful to produce BCD coordinates rotations accurately and fast, and different experiments demonstrating the advantages of the new method are shown. A reduction of 75% in a single stage delay is obtained, whereas the circuit area just increases in about 5%.

On searching for an optimal threshold for morphological image segmentation

Segmentation of images represents the first step in many of the tasks that pattern recognition or computer vision has to deal with. Therefore, the main goal of our paper is to describe a new method for image segmentation, taking into account some Mathematical Morphology operations and an adaptively updated threshold, what we call Morphological Gradient Threshold, to obtain the optimal segmentation. The key factor in our work is the calculation of the distance between the segmented image and the ideal segmentation. Experimental results show that the optimal threshold is obtained when the Morphological Gradient Threshold is around the 70% of the maximum value of the gradient. This threshold could be computed, for any new image captured by the vision system, using a properly designed binary metrics.

EZW-based image compression with omission and restoration of wavelet subbands

It is well known that multimedia applications provide the user with information through different methods (text, data, graphics, images, audio, video, etc.) which must be digitally represented, transmitted, stored and processed. Due to the fact that there is an increasing interest in developing high definition systems, multimedia applications are demanding, among others, higher bandwidth resources and more memory requirements in embedded devices. Therefore, it is essential to use compression techniques to reduce the time requirements of these new applications. This work aims to design an EZW-based image compression model, which makes use of the omission and restoration of wavelet subbands, providing high compression rates, good quality standards and low computation time requirements. The results obtained show that our method satisfies these assumptions and can be integrated in new multi-media devices.

Applying distance histograms for robust object recognition

Purpose – One of the main goals of vision systems is to recognize objects in real world to perform appropriate actions. This implies the ability of handling objects and, moreover, to know the relations between these objects and their environment in what we call scenes. Most of the time, navigation in unknown environments is difficult due to a lack of easily identifiable landmarks. Hence, in this work, some geometric features to identify objects are considered. Firstly, a Markov random field segmentation approach is implemented. Then, the key factor for the recognition is the calculation of the so‐called distance histograms, which relate the distances between the border points to the mass center for each object in a scene.Design/methodology/approach – This work, first discusses the features to be analyzed in order to create a reliable database for a proper recognition of the objects in a scene. Then, a robust classification system is designed and finally some experiments are completed to show that the reco...

Agent-based recognition of facial expressions

Description of a system to detect facial expressions using an agent-based approach is presented. The system utilizes interaction between Matlab-based image filters and a JADE-based agent implementation. The system is demonstrated using a feature recognition example. The system however has a much wider applicability, especially as Matlab is used extensively in other scientific/numerical computing applications.

Colour Image Compression Based on the Embedded Zerotree Wavelet

In recent years, some of the most emerging applications in multimedia data processing are wireless/mobile multimedia systems and streaming content over the Internet. Both applications require flexible image data compression for storage or transmission proposals. Wavelet-based image compression schemes, such as the Embedded Zerotree Wavelet, obtain excellent results for these proposals and have been object of intensive research. In this work we propose an EZW-based compression method for colour images, based on the omission and restoration of wavelet subbands; our method achieves high compression rates and low computation times, combining, therefore, the advantages from both DCT and wavelet based compression algorithms.