14,789 research outputs found
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
Fair comparison of skin detection approaches on publicly available datasets
Skin detection is the process of discriminating skin and non-skin regions in
a digital image and it is widely used in several applications ranging from hand
gesture analysis to track body parts and face detection. Skin detection is a
challenging problem which has drawn extensive attention from the research
community, nevertheless a fair comparison among approaches is very difficult
due to the lack of a common benchmark and a unified testing protocol. In this
work, we investigate the most recent researches in this field and we propose a
fair comparison among approaches using several different datasets. The major
contributions of this work are an exhaustive literature review of skin color
detection approaches, a framework to evaluate and combine different skin
detector approaches, whose source code is made freely available for future
research, and an extensive experimental comparison among several recent methods
which have also been used to define an ensemble that works well in many
different problems. Experiments are carried out in 10 different datasets
including more than 10000 labelled images: experimental results confirm that
the best method here proposed obtains a very good performance with respect to
other stand-alone approaches, without requiring ad hoc parameter tuning. A
MATLAB version of the framework for testing and of the methods proposed in this
paper will be freely available from https://github.com/LorisNann
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