Skin Detection and Segmentation in Color Images

Gałuszka

2015

Multimed Tools Appl

Self Cite

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.

Section: Overview Of Vision-based Gesture Recognitionmentioning

confidence: 99%

Section: Gesture Recognition Processmentioning

confidence: 99%

Hand landmarks detection and localization in color images

Grzejszczak

Gałuszka

2015

Multimed Tools Appl

Self Cite

“…This problem has been widely studied, and a large number of skin color models were introduced over the years. The main difference between them lies in their learning and generalization capabilities, but given a sufficiently large training set, their effectiveness is similar, and it is limited due to high variance and low specificity of human skin color [2]. Basically, skin and non-skin pixels overlap in color spaces; hence, they cannot be separated relying exclusively on their color.…”

Section: Overview Of Skin Detection and Segmentation Techniquesmentioning

confidence: 99%

“…Detection and segmentation of human skin regions [1,2] in color images is an active research topic, which receives considerable attention from image and signal processing community. Skin detection consists in taking a binary decision whether an image, its region, or a particular pixel presents the human skin.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive algorithm for segmenting skin regions

EURASIP J. Adv. Signal Process.

Nalepa

et al. 2014

Self Cite

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.

“…There exist a number of robust skin detection and segmentation techniques [15][16][17][18][19]. A thorough survey on current state-of-the-art skin detection approaches has been published recently [20]. Then, the image, either the skin mask or the original one, is normalized (line 6).…”

Section: Parallel Hand Shape Classification Algorithmmentioning

confidence: 99%

Fast and Accurate Hand Shape Classification

Nalepa

Communications in Computer and Information Science

2014

Self Cite

Abstract. The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.