Earticle

In this paper, a new switch median filter is presented for suppression of impulsive noise in image. The proposed filter is Modified Adaptive Center Weighted Median (MACWM) filter with an adjustable central weight obtained by partitioning the observation vector space. Dominant points of the proposed approach are partitioning of observation vector space using clustering method, training procedure using LMS algorithm then freezing weights in each block are applied to test image. The proposed method includes fuzzy clustering part for clustering the observed vector of each pixel into one of M mutually exclusive blocks. In the training phase, Least Mean Square (LMS) algorithm use to train center weight in each block then obtained weights used in testing phase. Final results shows better performance in the impulse noise reduction over standard images relative the median (MED) filter, the switching scheme I (SWM-I) filter, the signal dependent rank order mean (SD-ROM) filter, the tristate median (TSM) filter, the fast peer group filter (FPGF), the fuzzy median (FM) filter, the PFM filter and the adaptive center weighted median (ACWM) filter.

The continuous orthogonal polynomials, such as Zernike and pseudo-Zernike, are often used as an expansion of corneal height data. However, the use of continuous polynomials has some limitations due to the discretization. It is because that the integrals are usually approximated by discrete summations, and this process not only leads to numerical errors, but also severely affects some analytical properties such as rotation invariance, orthogonality, etc. To overcome these drawbacks, this paper presents a methodology for decomposing corneal height data into discrete orthogonal Tchebichef polynomials. Tchebichef polynomials, which are a product of angular functions and radial Tchebichef polynomials, are orthogonal in the discrete coordinate. Therefore, the approximation error caused by discretization can be avoided, and the analytical property can be well preserved. Examples of modeling corneal elevation are provided for simulation corneas, real normal corneas, and real abnormal corneas. The experimental results show that the proposed discrete Tchebichef polynomials have better surface representation capability than Zernike polynomials or pseudo-Zernike polynomials, and have more robust fitting for the level of noise found in current videokeratoscopes, so that they can be used as an alternative to fit the corneal surface.

In a multi script environment, an archive of documents having the text regions printed in different scripts is in practice. For automatic processing of such documents through Optical Character Recognition (OCR), it is necessary to identify different script regions of the document. In this paper, a novel texture-based approach is presented to identify the script type of the collection of documents printed in seven scripts, to categorize them for further processing. The South Indian documents printed in the seven scripts - Kannada, Tamil, Telugu, Malayalam, Urdu, Hindi and English are considered here The document images are decomposed through the Wavelet Packet Decomposition using the Haar basis function up to level two. Gray level co-occurrence matrix is constructed for the coefficient sub bands of the wavelet transform. The Haralick texture features are extracted from the co-occurrence matrix and then used in the identification of the script of a machine printed document. Experimentation conducted involved 2100 text images for learning and 1400 text images for testing. Script classification performance is analyzed using the K-nearest neighbor classifier. The average success rate is found to be 99.68%.

In this paper, it is considered the image compression scheme, in which a part of coded data extracted in the coding process is hidden into the other parts of coded data of own image, especially into the block address data of the best matching block within restricted blocks. The proposed scheme is able to be used in a fractal image coding in which the best matching domain block is searched, in a vector quantization in image coding in which the best matching vector is searched, and in motion compensation of moving picture in which the best matching motion vector is searched. We study each image coding method and consider the features of each coding method using the proposed scheme.

Superiority of poly-alphabetic sequences (PAS) for pulse compression radar over the binary and ternary sequences was established earlier. However, the enlarged alphabets in poly–alphabetic sequences deteriorate the noise and Doppler robustness at higher lengths in high resolution radar (HRR) systems. In this paper, poly-semantic sequences (PSS) with restricted alphabet {+1,-1} are considered and their performance is analyzed in order to achieve superior detection performance for high resolution radar system in presence of high density additive noise and Doppler shift. The poly-semantic sequences are optimized by employing modified Hamming scan algorithm called Hamming backtrack algorithm (HBT) by taking figure of merit as the measure of goodness. The detection capability of poly-semantic sequences is further improved through coincidence detection of the return signal. The simulation results show that the proposed sequences give improved robustness of noise and Doppler shift for HRR target detection compared to conventional pulse compression sequences.

Landslide is a type of mass movement that causes damage in many areas. The evolving remote sensing technology in producing high resolution images may help in landslide studies. However, the problem in detecting small size landslides is still challenging when suitable image resolution of the area being analyzed is not available. In this paper, a novel method based on elastic image registration, appropriate for the detection of small landslides will be presented. This method can be used to detect and quantify landslide movement with sub-pixel accuracy. It is based on the invocation of deformation operators which imitate the deformations expected to be observed when a landslide occurs. The similarity between two images is measured by a similarity function which takes into consideration grey level value correlation and geometric deformation. The geometric deformation term ensures that the minimum necessary deformation compatible with the two images is employed. An extra term, ensuring maximum overlap between the two images is also incorporated. There are two versions of this method. One using the correlation coefficient as a measure of similarity for the grey level value, and another one using mutual information. These methods are tested using known small scale landslides images of southern Italy taken from the Landsat 5 TM. The mutual information-based method gives more reliable results.

Adaptive Filtering is a widely researched topic in the present era of communications. When the received signal is continuously corrupted by noise where both the received signal and noise change continuously, then arises the need for adaptive filtering. The heart of the adaptive filter is the adaptive algorithm. This paper deals with cancellation of noise on speech signals using two algorithms-Least Mean Square (LMS) algorithm and Recursive Least Squares (RLS) algorithm with implementation in MATLAB. Comparisons of algorithms are based on SNR and tap weights of FIR filter. The algorithms chosen for implementation which provide efficient performances with less computational complexity.