As rapidly the computer technology is being developed the fractals and fractal based analysis have received special popularity. Space photogrammetric snap-shots fixed in a number of electromagnetic radiation spectral ranges have their own special attributes as compared with color images in general. The aspects of photogrammetric images segmentation based on multifractal analysis are studied in this paper in order to extract the edges of the developed object optimally. The aim of the study is to research the way of fractal analysis based on pointwise Hölder exponent of photogrammetric images fixed in a number of spectrum ranges by iconic means of remote sensing. 

Segmentation of Arabic text is a major challenge that shall be addressed by any recognition system. The cursive nature of Arabic writing makes it necessary to handle the segmentation issue at various levels. Arabic text line can be viewed as a sequence of words which in turn can be viewed as a sequence of sub-words. Sub-words have the frequently encountered intrinsic property of sharing the same vertical space which makes vertical projection based segmentation technique inefficient. In this paper, the task of segmenting handwritten Arabic text at sub-word level is taken up. The proposed algorithm is based on pulling away the connected components to overcome the impossibility of separating them by vertical projection based approach. Graph theoretic modeling is proposed to solve the problem of connected component extraction. In the sequel, these components are subjected to thorough analysis in order to obtain the constituent sub-words where a sub-word may consist of many components. The proposed algorithm was tested using variety of handwritten Arabic samples taken from different databases and the results obtained are encouraging. 

Remote sensing textual image classification technology has been the hottest topic in the filed of remote sensing. Texture is the most helpful symbol for image classification. In common, there are complex terrain types and multiple texture features are extracted for classification, in addition; there is noise in the remote sensing images and the single classifier is hard to obtain the optimal classification results. Integration of multiple classifiers is able to make good use of the characteristics of different classifiers and improve the classification accuracy in the largest extent. In the paper, based on the diversity measurement of the base classifiers, J48 classifier, IBk classifier, sequential minimal optimization (SMO) classifier, Naive Bayes classifier and multilayer perceptron (MLP) classifier are selected for ensemble learning. In order to evaluate the influence of our proposed method, our approach is compared with the five base classifiers through calculating the average classification accuracy. Experiments on five UCI data sets and remote sensing image data sets are performed to testify the effectiveness of the proposed method. 

The signal processing applications are limited with high-resolution signal parameter estimation. Therefore the Direction of Arrival estimation algorithm needs to be effective and efficient in order to improve the performance of smart antennas. This paper presents the simulation for a subspace based DOA estimation algorithm with high resolution. MUSIC (Multiple signal classification) and the IMUSIC (Improved MUSIC) are presented and optimized by varying various parameters. The basic MUSIC algorithm is ineffective in estimating the incoming coherent signals. The new improved MUSIC algorithm overcomes this ineffectiveness and correctly estimates the related signals with improved accuracy. The improved version of MUSIC algorithm is brought about by taking into account the conjugate of the data matrix for MUSIC algorithm and then reconstructing it. The various factors like the number of array elements, number of snapshots, varying the distance between array elements, varying SNR and the difference in arrival angles can bring about better resolutions. The comparisons for MUSIC and Improved MUSIC algorithm are widely discussed. 

Shadows are physical phenomena that appear on a surface when direct light from a source is unable to reach the surface due to the presence of an object between the source and the surface. The formation of shadows and their various features has evolved as a topic of discussion among researchers. Though the presence of shadows can aid us in understanding the scene model, it might impair the performance of applications such as object detection. Hence, the removal of shadows from videos and images is required for the faultless working of certain image processing tasks. This paper presents a survey of notable shadow removal techniques for single image available in the literature. For the purpose of the survey, the various shadow removal algorithms are classified under five categories, namely, reintegration methods, relighting methods, patch-based methods, color transfer methods, and interactive methods. Comparative study of qualitative and quantitative performances of these works is also included. The pros and cons of various approaches are highlighted. The survey concludes with the following observations- (i) shadow removal should be performed in real time since it is usually considered as a preprocessing task, (ii) the texture and color information of the regions underlying the shadow must be recovered, (iii) there should be no hard transition between shadow and non-shadow regions after removing the shadows. 

Image compression is playing a vital role in the development of various multimedia applications. Image Compression solves the problem of reducing the amount of data required to represent the digital image. In image compression methods there are several techniques evolved. All techniques of image compression basically divided into two parts, spatial domain compression technique and frequency domain compression technique. In frequency domain techniques there are numerous techniques like Fourier Transform, Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) etc. after converting the image into frequency domain transformation, it uses several encoding technique like Embedded Zero Tree (EZW) coding, SPIHT (Set Partitioning in Hierarchical Tree), ASWDR (Adaptively Scanned Wavelet Difference Reduction) WDR (Wavelet Difference Reduction) and STW (Spatial orientation Tree Wavelet) etc. These encoding schemes are also known as Progressive Coefficients Significance Methods (PCSM). In this paper the wavelet filters combine with improved PCSM codes and proposed a new filter for further improvement. In new wavelet proposed filter has slightly change in the scaling and wavelet function of existing filter. It gives the wide range of selectivity of frequencies in higher and lower side of it. Hence it provides better lower bandwidth range with greater high preservation of frequencies. Scaling and wavelet impulse response of proposed filter then a comparison is made on the proposed work with all the filters. Filters are demonstrated to show the performance of compression using wavelet functions. The filters are used in the work like bi-orthogonal (BIO), Reverse bi-orthogonal (RBIO), Coiflets (COIF), Daubechies (DB), Symlet (SYM) and Improved Progressive Coefficients Significance Method (IPCSM) encoding scheme will be compare and analyze with all compression parameters like mean square error (MSE), peak to signal noise ratio (PSNR), compression ratio (CR), execution time (ET), bits per pixel (BPP), root mean square error (MSE). 

In the modern digital systems, the digital image processing and digital signal processing application form an integrated part in the system design. Many designers proposed and implemented various resources and speed efficient approaches in the recent past. The important aspect of designing any digital system is its memory efficiency. The image consists of various pixels and each pixel is again holds a value from 0 to 255 which requires 8 bits to represent the range. So a larger memory is required to process the image and with the increase in size of the image the number of pixels also increases. A buffering technique is used to read the pixel data from the image and process the data efficiently. In the work presented in this paper, different window sizes are compared on the basis of timing efficiency and area utilization. An optimum window size must be selected so as to reduce the resources and maximize the speed. Results show the comparison of various window operations on the basis of performance parameters. In future other window operation along with convolution filter like Adaptive Median filter must be implemented and used by changing the row and column values in Window size. 

Face recognition using subspace methods are quite popular in research community. This paper proposes an efficient face recognition method based on the application of recently developed triplet half band wavelet filter bank (TWFB) as pre-processing step to further enhance the performance of well known linear and nonlinear subspace methods such as principle component analysis(PCA),kernel principle component analysis (KPCA), linear discriminant analysis (LDA), and kernel discriminant analysis (KDA). The design of 6th order TWFB is used as the multiresolution analysis tool to perform the 2-D discrete wavelet transform (DWT). Experimental results are performed on two standard databases ORL and Yale. Comparative results are obtained in terms of verification performance parameters such as false acceptance rate (FAR), false rejection rate (FRR) and genuine acceptance rate (GAR). Application of TWFB enhances the performance of PCA, KPCA, LDA, and KDA based methods.