Michal Kawulok

Fast propagation-based skin regions segmentation in color images

This paper introduces a new method for skin regions segmentation which consists in spatial analysis of skin probability maps obtained using pixel-wise detectors. There are a number of methods which use various techniques of skin color modeling to classify every individual pixel or transform input color images into skin probability maps, but their performance is limited due to high variance and low specificity of the skin color. Detection precision can be enhanced based on spatial analysis of skin pixels, however this direction has been little explored so far. Our contribution lies in using the distance transform for propagating the “skinness” across the image in a combined domain of luminance, hue and skin probability. In the paper we explain theoretical advantages of the proposed method over alternative skin detectors that also perform spatial analysis. Finally, we present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition.

Support vector machines training data selection using a genetic algorithm

This paper presents a new method for selecting valuable training data for support vector machines (SVM) from large, noisy sets using a genetic algorithm (GA). SVM training data selection is a known, however not extensively investigated problem. The existing methods rely mainly on analyzing the geometric properties of the data or adapt a randomized selection, and to the best of our knowledge, GA-based approaches have not been applied for this purpose yet. Our work was inspired by the problems encountered when using SVM for skin segmentation. Due to a very large set size, the existing methods are too time-consuming, and random selection is not effective because of the set noisiness. In the work reported here we demonstrate how a GA can be used to optimize the training set, and we present extensive experimental results which confirm that the new method is highly effective for real-world data.

Wrist Localization in Color Images for Hand Gesture Recognition

In this paper we present an extensive study of a two-stage algorithm for wrist localization in color images, which is an important and challenging, yet not extensively studied, step in gesture recognition systems. In the first stage of the algorithm, a color hand image is subject to skin segmentation. Secondly, the wrist is localized in a corresponding binarized skin probability map. In our two-stage approach, the algorithms for both localization stages can be developed and compared separately. Here, we compare our propagation-based skin segmentation algorithm and real-time wrist localization algorithm with other state-of-the-art approaches based on our set of 414 color hand images using two independent sets of ground-truth data.

Energy-based blob analysis for improving precision of skin segmentation

This paper addresses a problem of precise skin segmentation necessary for sign language recognition purposes. The main contribution of the presented research is an adaptive skin model enhanced with a blob analysis algorithm which significantly reduces false positives and improves skin segmentation precision. Adaptive skin detector utilizes a statistical skin color model updated dynamically based on a face region defined by eye positions. Face geometry is used for face and eye detection in luminance channel prior to the model adaptation. Color-based skin detectors classify every pixel separately which results in high false positives for background pixels which color is similar to human skin. The proposed blob analysis technique verifies detected skin regions by taking into account pixel topology. The experiments for ECU database showed that with the proposed approach false positive rate was reduced from 15.6% to 6% compared with a statistical model in RGB, which can be regarded as a significant improvement.

Dynamic Skin Detection in Color Images for Sign Language Recognition

Skin detection is the first step of processing in many approaches to face and gesture recognition. This paper presents research aimed at detecting skin in digital images for Polish Sign Language recognition. There are many methods for detecting human skin, including parametric skin models defined in various color spaces and statistical approaches which require appropriate training. The presented method is based on statistical model updated dynamically for every image in which human faces can be detected. The detection is performed in luminance channel based on geometric properties of human faces. The experiments proved that effectiveness of this approach is higher than application of general skin detection models.

A memetic algorithm to select training data for support vector machines

In this paper we propose a new memetic algorithm (MASVM) for fast and efficient selection of a valuable training set for support vector machines (SVMs). This is a crucial step especially in case of large and noisy data sets, since the SVM training has high time and memory complexity. The majority of state-of-the-art methods exploit the data geometry analysis, both in the input and kernel space. Although evolutionary algorithms have been proven to be very efficient for this purpose, they have not been extensively studied so far. Here, we propose a new method employing an adaptive genetic algorithm enhanced by some refinement techniques. The refinements are based on utilizing a pool of the support vectors identified so far at various steps of the algorithm. Extensive experimental study performed on the well-known benchmark, real-world and artificial data sets clearly confirms the efficacy, robustness and convergence capabilities of the proposed approach, and shows that it is competitive compared with other state-of-the-art techniques.

Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs

Support vector machines (SVMs) are one of the most popular and powerful machine learning techniques, but suffer from a significant drawback of the high time and memory complexities of their training. This issue needs to be endured especially in the case of large and noisy datasets. In this paper, we propose a new adaptive memetic algorithm (PCA2MA) for selecting valuable SVM training data from the entire set. It helps improve the classifier score, and speeds up the classification process by decreasing the number of support vectors. In PCA2MA, a population of reduced training sets undergoes the evolution, which is complemented by the refinement procedures. We propose to exploit both a priori information about the training set-extracted using the data geometry analysis-and the knowledge attained dynamically during the PCA2MA execution to enhance the refined sets. Also, we introduce a new adaptation scheme to control the pivotal algorithm parameters on the fly, based on the current search state. Extensive experimental study performed on benchmark, real-world, and artificial datasets clearly confirms the efficacy and convergence capabilities of the proposed approach. We demonstrate that PCA2MA is highly competitive compared with other state-of-the-art techniques. HighlightsWe propose a new adaptive memetic algorithm to select SVM training data.We perform the PCA-based preprocessing to determine valuable training samples.We apply a new scheme to adapt the refined training set size and selection scheme.We evaluate the importance of particular components of our algorithm.We demonstrate the effectiveness and efficiency of the proposed memetic algorithm.

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.

Hand landmarks detection and localization in color images

This paper introduces a new method for detecting and localizing hand landmarks in 2D color images. Location of the hand landmarks is an important source of information for recognizing hand gestures, effectively exploited in a number of recent methods which operate from the depth maps. However, this problem has not yet been satisfactorily solved for 2D color images. Here, we propose to analyze the skin-presence masks, as well as the directional image of a hand using the distance transform and template matching. This makes it possible to detect the landmarks located both at the contour and inside the hand masks. Moreover, we performed an extensive experimental study to compare the proposed method with a number of state-of-the-art algorithms. The obtained quantitative and qualitative results clearly indicate that our approach outperforms other methods, which may help improve the existing gesture recognition systems.

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.