Skin detection has gained popularity and importance in the computer vision community. It is an essential step for important vision tasks such as the detection, tracking and recognition of face, segmentation of hand for gesture analysis, person identification, as well as video surveillance and filtering of objectionable web images. All these applications are based on the assumption that the regions of the human skin are already located. In the recent past, numerous techniques for skin colour modeling and recognition have been proposed. The aims of this paper are to compile the published pixel-based skin colour detection techniques to describe their key concepts and try to find out and summarize their advantages, disadvantages and characteristic features.
Skin colour is an important visual cue for face detection, face recogmtlon, hand segmentation for gesture analysis and filtering of objectionable images. In this paper, the adaptive skin color detection model is proposed, based on two bivariate normal distribution models of the skin chromatic subspace, and on image segmentation using an automatic and adaptive multi-thresholding technique. Experimental results on images presenting a wide range of variations in lighting condition and background demonstrate the efficiency of the proposed skin-segmentation algorithm.
Referring to the existing model that considers the image boundary as the image background,
the model is still not able to produce an optimum detection. This paper is introducing
the combination features at the boundary known as boundary components affinity that is
capable to produce an optimum measure on the image background. It consists of contrast,
spatial location, force interaction and boundary ratio that contribute to a novel boundary
connectivity measure. The integrated features are capable to produce clearer background
with minimum unwanted foreground patches compared to the ground truth. The extracted
boundary features are integrated as the boundary components affinity. These features were
used for measuring the image background through its boundary connectivity to obtain the
final salient object detection. Using the verified datasets, the performance of the proposed
model was measured and compared with the 4 state-of-art models. In addition, the model
performance was tested on the close contrast images. The detection performance was
compared and analysed based on the precision, recall, true positive rate, false positive
rate, F Measure and Mean Absolute Error (MAE). The model had successfully reduced
the MAE by maximum of 9.4%.
Non-invasive imaging modalities for wound assessment have become increasingly popular over the past
two decades. The wounds can be developed superficially or from within deep tissues, depending on the
nature of the dominant risk factors. Developing a reproducible quantitative method to assess woundhealing
status has demonstrated to be a convoluted task. Advances in High-Frequency Ultrasound (HFU)
skin scanners have expanded their application as they are cost-effective and reproducible diagnostic tools
in dermatology, including for the measurement of skin thickness, the assessment of skin tumours, the
estimation of the volume of melanoma and non-melanoma skin cancers, the visualisation of skin structure
and the monitoring of the healing of acute and chronic wounds. Previous studies have revealed that HFU
images carry dominant parameters and depict the phenomena occurring within deep tissue layers during
the wound-healing process. However, the investigations have mostly focussed on the validation of HFU
images, and few studies have utilised HFU imaging in quantitative assessment of wound generation and
healing. This paper is an introductory review of the
important studies proposed by the researchers in
the context of wound assessment. The principles
of dermasonography are briefly explained,
followed by a review of the relevant literature that
investigated the wound-healing process and tissue
structures within the wound using HFU imaging.