Sreeraj, M; Soumya, Varma(IEEE, February 17, 2014)
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Abstract:
Detection of Objects in Video is a highly
demanding area of research. The Background Subtraction
Algorithms can yield better results in Foreground Object
Detection. This work presents a Hybrid CodeBook based
Background Subtraction to extract the foreground ROI from the
background. Codebooks are used to store compressed
information by demanding lesser memory usage and high speedy
processing. This Hybrid method which uses Block-Based and
Pixel-Based Codebooks provide efficient detection results; the
high speed processing capability of block based background
subtraction as well as high Precision Rate of pixel based
background subtraction are exploited to yield an efficient
Background Subtraction System. The Block stage produces a
coarse foreground area, which is then refined by the Pixel stage.
The system’s performance is evaluated with different block sizes
and with different block descriptors like 2D-DCT, FFT etc. The
Experimental analysis based on statistical measurements yields
precision, recall, similarity and F measure of the hybrid system
as 88.74%, 91.09%, 81.66% and 89.90% respectively, and thus
proves the efficiency of the novel system.
Description:
Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the
Sreeraj, M; Sumam, Mary Idicula(Association for Computer Science and Telecommunica, May , 2011)
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Abstract:
This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach
Sreeraj, M; Sumam, Mary Idicula(IEEE, December 9, 2009)
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Abstract:
This paper presents an efficient Online Handwritten
character Recognition System for Malayalam Characters
(OHR-M) using Kohonen network. It would help in
recognizing Malayalam text entered using pen-like devices. It
will be more natural and efficient way for users to enter text
using a pen than keyboard and mouse. To identify the
difference between similar characters in Malayalam a novel
feature extraction method has been adopted-a combination of
context bitmap and normalized (x, y) coordinates. The system
reported an accuracy of 88.75% which is writer independent
with a recognition time of 15-32 milliseconds
Description:
2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009)
As the popularity of digital videos increases, a large number illegal videos are
being generated and getting published. Video copies are generated by performing various
sorts of transformations on the original video data. For effectively identifying such illegal
videos, the image features that are invariant to various transformations must be extracted for
performing similarity matching. An image feature can be its local feature or global feature.
Among them, local features are powerful and have been applied in a wide variety of computer vision aplications .This paper focuses on various recently proposed local detectors and descriptors that are invariant to a number of image transformations.
Description:
International Journal of Scientific & Engineering Research, Volume 4, Issue 9, september 2013