Son Lam Phung

Skin segmentation using color pixel classification: analysis and comparison

This work presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.

A novel skin color model in YCbCr color space and its application to human face detection

This paper presents a new human skin color model in YCbCr color space and its application to human face detection. Skin colors are modeled by a set of three Gaussian clusters, each of which is characterized by a centroid and a covariance matrix. The centroids and covariance matrices are estimated from large set of training samples after a k-means clustering process. Pixels in a color input image can be classified into skin or non-skin based on the Mahalanobis distances to the three clusters. Efficient post-processing techniques namely noise removal, shape criteria, elliptic curve fitting and face/non-face classification are proposed in order to further refine skin segmentation results for the purpose of face detection.

A Pyramidal Neural Network For Visual Pattern Recognition

In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient backpropagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM)

Adaptive skin segmentation in color images

A new skin segmentation technique for color images is proposed. The proposed technique uses a human skin color model that is based on the Bayesian decision theory and developed using a large training set of skin colors and nonskin colors. The proposed technique is novel and unique in that texture characteristics of the human skin are used to select appropriate skin color thresholds for skin segmentation. Two homogeneity measures for skin regions that take into account both global and local image features are also proposed. Experimental results showed that the proposed technique can achieve good skin segmentation performance (false detection rate of 4.5% and false rejection rate of 4.0%).

A universal and robust human skin color model using neural networks

We propose a new image classification technique that utilizes neural networks to classify skin and non-skin pixels in color images. The aim is to develop a universal and robust model of the human skin color that caters for all human races. The ability to detecting solid skin regions in color images by the model is extremely useful in applications such as face detection and recognition, and human gesture analysis. Experimental results show that the neural network classifiers can consistently achieve up to 90% accuracy in skin color detection.

Skin segmentation using color and edge information

An algorithm for segmenting skin regions in color images using color and edge information is presented. Skin colored regions are first detected using a Bayesian model of the human skin color. These regions are further segmented into skin region candidates that satisfy the homogeneity property of the human skin. We show that Bayesian skin color model outperforms many other models such as the piece-wise linear models, Gaussian models and model based on multilayer perceptrons. Experimental results indicate that the proposed segmentation algorithm reduces false detection caused by background pixels having skin colors, and more significantly it is capable of separating true skin regions from falsely detected regions.

Learning pattern classification tasks with imbalanced data sets

This chapter is concerned with the class imbalance problem, which has been recognised as a crucial problem in machine learning and data mining. The problem occurs when there are significantly fewer training instances of one class compared to another class.

Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

The ground-penetrating radar (GPR) has been widely used in many applications. However, the processing and interpretation of the acquired signals remain challenging tasks since an experienced user is required to manage the entire operation. In this paper, we present an automatic classification system to assess railway-ballast conditions. It is based on the extraction of magnitude spectra at salient frequencies and their classification using support vector machines. The system is evaluated on real-world railway GPR data. The experimental results show that the proposed method efficiently represents the GPR signal using a small number of coefficients and achieves a high classification rate when distinguishing GPR signals reflected by ballasts of different conditions.

Two-Stage Fuzzy Fusion With Applications to Through-the-Wall Radar Imaging

A two-stage fuzzy image fusion approach, which combines multiple radar images of the same scene, is proposed to produce a more informative image. In this approach, two different image fusion methods are first applied. Then, a fuzzy logic fusion method is applied to the outputs of the first fusion stage. The performance of the proposed approach is evaluated on through-the-wall radar images obtained using different polarizations. Experimental results show that the proposed approach enhances image quality by producing outputs with high target intensity values and low clutter.

On the analysis of background subtraction techniques using Gaussian Mixture Models

In this paper, we conduct an investigation into background subtraction techniques using Gaussian Mixture Models (GMM) in the presence of large illumination changes and background variations. We show that the techniques used to date suffer from the trade-off imposed by the use of a common learning rate to update both the mean and variance of the component densities, which leads to a degeneracy of the variance and creates “saturated pixels”. To address this problem, we propose a simple yet effective technique that differentiates between the two learning rates, and imposes a constraint on the variance so as to avoid the degeneracy problem. Experimental results are presented which show that, compared to existing techniques, the proposed algorithm provides more robust segmentation in the presence of illumination variations and abrupt changes in background distribution.