Abstract: This paper presents a comparative analysis of a new
unsupervised PCA-based technique for steel plates texture segmentation
towards defect detection. The proposed scheme called Variance
Based Component Analysis or VBCA employs PCA for feature
extraction, applies a feature reduction algorithm based on variance of
eigenpictures and classifies the pixels as defective and normal. While
the classic PCA uses a clusterer like Kmeans for pixel clustering,
VBCA employs thresholding and some post processing operations to
label pixels as defective and normal. The experimental results show
that proposed algorithm called VBCA is 12.46% more accurate and
78.85% faster than the classic PCA.
Abstract: Textures are replications, symmetries and
combinations of various basic patterns, usually with some random
variation one of the gray-level statistics. This article proposes a
new approach to Segment texture images. The proposed approach
proceeds in 2 stages. First, in this method, local texture information
of a pixel is obtained by fuzzy texture unit and global texture
information of an image is obtained by fuzzy texture spectrum.
The purpose of this paper is to demonstrate the usefulness of fuzzy
texture spectrum for texture Segmentation.
The 2nd Stage of the method is devoted to a decision process,
applying a global analysis followed by a fine segmentation,
which is only focused on ambiguous points. The above Proposed
approach was applied to brain image to identify the components
of brain in turn, used to locate the brain tumor and its Growth
rate.
Abstract: In this paper, a robust digital image watermarking
scheme for copyright protection applications using the singular value
decomposition (SVD) is proposed. In this scheme, an entropy
masking model has been applied on the host image for the texture
segmentation. Moreover, the local luminance and textures of the host
image are considered for watermark embedding procedure to
increase the robustness of the watermarking scheme. In contrast to all
existing SVD-based watermarking systems that have been designed
to embed visual watermarks, our system uses a pseudo-random
sequence as a watermark. We have tested the performance of our
method using a wide variety of image processing attacks on different
test images. A comparison is made between the results of our
proposed algorithm with those of a wavelet-based method to
demonstrate the superior performance of our algorithm.