Abstract: In this paper, we present a novel approach to accurately
detect text regions including shop name in signboard images with
complex background for mobile system applications. The proposed
method is based on the combination of text detection using edge
profile and region segmentation using fuzzy c-means method. In the
first step, we perform an elaborate canny edge operator to extract all
possible object edges. Then, edge profile analysis with vertical and
horizontal direction is performed on these edge pixels to detect
potential text region existing shop name in a signboard. The edge
profile and geometrical characteristics of each object contour are
carefully examined to construct candidate text regions and classify the
main text region from background. Finally, the fuzzy c-means
algorithm is performed to segment and detected binarize text region.
Experimental results show that our proposed method is robust in text
detection with respect to different character size and color and can
provide reliable text binarization result.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
Abstract: Image coding based on clustering provides immediate
access to targeted features of interest in a high quality decoded
image. This approach is useful for intelligent devices, as well as for
multimedia content-based description standards. The result of image
clustering cannot be precise in some positions especially on pixels
with edge information which produce ambiguity among the clusters.
Even with a good enhancement operator based on PDE, the quality of
the decoded image will highly depend on the clustering process. In
this paper, we introduce an ambiguity cluster in image coding to
represent pixels with vagueness properties. The presence of such
cluster allows preserving some details inherent to edges as well for
uncertain pixels. It will also be very useful during the decoding phase
in which an anisotropic diffusion operator, such as Perona-Malik,
enhances the quality of the restored image. This work also offers a
comparative study to demonstrate the effectiveness of a fuzzy
clustering technique in detecting the ambiguity cluster without losing
lot of the essential image information. Several experiments have been
carried out to demonstrate the usefulness of ambiguity concept in
image compression. The coding results and the performance of the
proposed algorithms are discussed in terms of the peak signal-tonoise
ratio and the quantity of ambiguous pixels.