6,499 research outputs found
Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques
One of the most important steps of document image processing is binarization.
The computational requirements of locally adaptive binarization techniques make
them unsuitable for devices with limited computing facilities. In this paper,
we have presented a computationally efficient implementation of convolution
based locally adaptive binarization techniques keeping the performance
comparable to the original implementation. The computational complexity has
been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the
image size. Experiments over benchmark datasets show that the computation time
has been reduced by 5 to 15 times depending on the window size while memory
consumption remains the same with respect to the state-of-the-art algorithmic
implementation
Image Enhancement with Statistical Estimation
Contrast enhancement is an important area of research for the image analysis.
Over the decade, the researcher worked on this domain to develop an efficient
and adequate algorithm. The proposed method will enhance the contrast of image
using Binarization method with the help of Maximum Likelihood Estimation (MLE).
The paper aims to enhance the image contrast of bimodal and multi-modal images.
The proposed methodology use to collect mathematical information retrieves from
the image. In this paper, we are using binarization method that generates the
desired histogram by separating image nodes. It generates the enhanced image
using histogram specification with binarization method. The proposed method has
showed an improvement in the image contrast enhancement compare with the other
image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images
Cell segmentation in microscopy is a challenging problem, since cells are
often asymmetric and densely packed. This becomes particularly challenging for
extremely large images, since manual intervention and processing time can make
segmentation intractable. In this paper, we present an efficient and highly
parallel formulation for symmetric three-dimensional (3D) contour evolution
that extends previous work on fast two-dimensional active contours. We provide
a formulation for optimization on 3D images, as well as a strategy for
accelerating computation on consumer graphics hardware. The proposed software
takes advantage of Monte-Carlo sampling schemes in order to speed up
convergence and reduce thread divergence. Experimental results show that this
method provides superior performance for large 2D and 3D cell segmentation
tasks when compared to existing methods on large 3D brain images
Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method
Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2
- …