Jolanta Kawulok

Skin Detection and Segmentation in Color Images

This chapter presents an overview of existing methods for human skin detection and segmentation. First of all, the skin color modeling schemes are outlined, and their limitations are discussed based on the presented experimental study. Then, we explain the techniques which were reported helpful in improving the efficacy of color-based classification, namely (1) textural features extraction, (2) model adaptation schemes, and (3) spatial analysis of the skin blobs. The chapter presents meaningful qualitative and quantitative results obtained during our study, which demonstrate the benefits of exploiting particular techniques for improving the skin detection outcome.

Self-adaptive algorithm for segmenting skin regions

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

Skin detection using spatial analysis with adaptive seed

This paper introduces a new method for adaptive skin detection in color images combined with spatial analysis of skin pixels. It has been reported in many works that adaptation of a skin color model to a particular image may decrease the false positives, however the false negatives are considerably high unless a local model is combined with the global one. Another possibility for improvement is to analyze spatial properties of the pixels classified as skin, but this operation strongly depends on the seed extraction technique. Our contribution lies in using a local dynamic skin model learned from the detected faces to extract seeds for the spatial analysis. We present an extensive experimental study confirming that our method outperforms alternative skin detection techniques.

CoMeta: Classification of Metagenomes Using k-mers

Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license.

Support Vector Machines in Biomedical and Biometrical Applications

In the chapter, a background review material concerning applications of the kernel methods in computational biology and biometry is illustrated by the case studies concerning the proteomic spectra analysis to find diagnostic biomarkers and performing case-control discrimination as well as the face recognition problem, which is situated among the most investigated biometric methods. These case studies, representing the state-of-the-art in applications of the support vector machines (SVM) in biomedical and biometrical applications, are the examples of a research work conducted by computer scientists, bioinformaticians, and biostatisticians from the Faculty of Automatic Control, Electronics and Computer Science at Silesian University of Technology in a collaboration with clinicists from the Institute of Oncology in Gliwice, Poland.

Textural Features for Scribble-Based Image Colorization

In this paper we propose how to exploit image textural features to improve scribble-based image colorization. The existing techniques work by propagating color from the user-added scribbles over the whole image. The color propagation paths are determined so as to minimize the luminance changes integrated along the path. In our method, at first linear discriminant analysis is performed over the scribble pixels to extract discriminative textural features (DTF). Our contribution to image colorization lies in using DTF for the path optimization instead of the luminance. The colorization results presented in the paper explain and confirm the method’s robustness compared with the alternative existing techniques.

Hybrid adaptation for detecting skin in color images

It has been reported in many works on skin detection and segmentation from color images that skin color models suffer from low specificity and high variance of the skin color, and this problem can be addressed by conforming the skin model to a presented scene. Here, we introduce a new hybrid adaptation system which combines two strategies, namely (i) adaptation from a detected facial region and (ii) a self-adaptive scheme that creates a local model based on the response obtained using the global one. As a result of this hybrid adaptation, we obtain a local skin color model and we use it to extract seeds for the geodesic distance transform that determines the boundaries of skin regions. The results of our extensive experimental study confirm that the proposed algorithm outperforms several state-of-the-art methods, as well as our earlier adaptive skin detectors.

An Improved Algorithm for Fast and Accurate Classification of Sequences

Understanding of biocenosis derived from environmental samples can help understanding the relationships between organisms and the environmental conditions of their occurrence. Therefore, the classification of DNA fragments that are selected from different places is an important issue in many studies. In this paper we report how to improve (in terms of speed and qualification accuracy) the algorithm of fast and accurate classification of sequences (FACS).

Environmental Metagenome Classification for Soil-based Forensic Analysis

Metagenome analysis makes it possible to extract essential information on the organisms that have left their traces in a given environmental sample. In some cases, it is sufficient to determine the origin of an environmental sample, rather than being able to accurately identify the organisms living there (which may be a challenging task). For example, in forensic soil analysis, it could be possible to confirm or exclude that a defendant was present in a certain place by comparing a soil sample acquired from his belongings against the samples derived from a variety of places (including the suspected place). In this paper, we present a method to identify the environmental origins of metagenomic reads by comparing them with entire metagenomic collections derived from reference samples. For this purpose, we exploit our CoMeta program, which allows for fast classification of metagenome samples, and we apply it to classify the extracted soil metagenomes to various collections of soil samples. The experimental results reported in this paper indicate that the proposed approach is effective, which allows us to outline the future research pathways to extend and improve our method.

Self-Adaptive Skin Segmentation in Color Images

In this paper, we present a new method for skin detection and segmentation, relying on spatial analysis of skin-tone pixels. Our contribution lies in introducing self-adaptive seeds, from which the skin probability is propagated using the distance transform. The seeds are determined from a local skin color model that is learned on-line from a presented image, without requiring any additional information. This is in contrast to the existing methods that need a skin sample for the adaptation, e.g., acquired using a face detector. In our experimental study, we obtained F-score of over 0.85 for the ECU benchmark, and this is highly competitive compared with several state-of-the-art methods.