High Performance Novel Skin Segmentation Algorithm for Images With Complex Background
Skin Segmentation is widely used in biometric applications such as face detection, face recognition, face tracking, and hand gesture recognition. However, several challenges such as nonlinear illumination, equipment effects, personal interferences, ethnicity variations, etc., are involved in detection process that result in the inefficiency of color based methods. Even though many ideas have already been proposed, the problem has not been satisfactorily solved yet. This paper introduces a technique that addresses some limitations of the previous works. The proposed algorithm consists of three main steps including initial seed generation of skin map, Otsu segmentation in color images, and finally a two-stage diffusion. The initial seed of skin pixels is provided based on the idea of ternary image as there are certain pixels in images which are associated to human complexion with very high probability. The Otsu segmentation is performed on several color channels in order to identify homogeneous regions. The result accompanying with the edge map of the image is utilized in two consecutive diffusion steps in order to annex initially unidentified skin pixels to the seed. Both quantitative and qualitative results demonstrate the effectiveness of the proposed system in compare with the state-of-the-art works.
Code (0)
등록된 구현이 없습니다.
Tasks
Face DetectionFace RecognitionGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionSegmentationVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many …
Lesion SegmentationMedical Image AnalysisSegmentationSkin Lesion SegmentationSegmenting Dermoscopic Images
We propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentatio…
Lesion SegmentationSegmentationAutomatic Skin Lesion Segmentation using Semi-supervised Learning Technique
Skin cancer is the most common of all cancers and each year million cases of skin cancer are treated. Treating and curing skin cancer is easy, if it is diagnosed and treated at an early stage. In this work we propose an …
ClusteringLesion SegmentationSegmentationSkin Lesion SegmentationSkin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
This work summarizes the results of the largest skin image analysis challenge in the world, hosted by the International Skin Imaging Collaboration (ISIC), a global partnership that has organized the world's largest publi…
AttributeLesion SegmentationSegmentationUnsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search
Segmentation of the pixels corresponding to human skin is an essential first step in multiple applications ranging from surveillance to heart-rate estimation from remote-photoplethysmography. However, the existing litera…
Domain AdaptationSegmentationSemantic SegmentationUnsupervised Domain Adaptation