Acne vulgaris is a common skin disease that is found in all humans. There are various types of acnes according to their severity. In this research, an application was developed to segment and find out the acne on patients image. In this paper an acne detection method based on image processing techniques were processed. It is mobile based and hence very accessible even in remote areas and it is completely noninvasive to patient's skin. An image of the infected area of the skin is provided as an input to prototype by patient. Different Image processing techniques are performed on this image and corresponding disease detected is displayed at the output. This proposed system is highly beneficial in rural areas where access to dermatologists is limited.
Abstract:The Systems that rely on Face Recognition (FR) biometric have acquired great importance ever since terrorist threats injected weakness among the implemented security systems. Rest biometrics as iris or Fingerprints recognition is not trustworthy in such situations whereas FR is considered as a better compromise. In Image processing, Occlusion refers to facade of the face image which can be due to hair, moustache, sunglasses, or wrapping of facial image by scarf or other accessories. Efforts on FR appears in controlled environment have been in the picture for past several years; however identification under uncontrolled condition like partial occlusion is typically quite a matter of concern. Based on review of literature and its analysis so far, a classification made in this paper to solve the challenges in recognition of face in the presence of partial occlusion. The methods used are INPAINTING based methods that make use of Exemplar-based Inpainting, Feature-Extraction, and Fast Weighted-Principal component analysis (FW-PCA),etc. The presented approach in this paper describes the removal of Occlusion from images or restore the occluded part of image using Exemplar-based Image Inpainting technique, feature extraction and FW-PCA(Restoration) combinations.
Sign Language is one of the most common approaches of communication usually used by people having hearing and speech impairment. These languages consist of well-defined set of gestures or pattern and sequence of actions that conveys meaningful words and sentences. The paper presents different algorithms and techniques for automation of single hand gesture detection and recognition using vision based methods. The paper uses basic structure of hand and properties like centroid for detecting the pattern formed by the fingers and thumb and assigning code bits i.e. converting each gesture into a set of 5 digits representation and motion is detected using movement of centroid in each frame. The paper uses techniques like K-means Clustering or Thresholding for background elimination; Convex Hull or a proposed algorithm for peak detection and text to speech API for conversion of words/sentences corresponding to gestures to speech. Combinations of different techniques like thresholding and convex hull or Clustering and proposed algorithm is implemented and results are compared.
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