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An image is an artifact that depicts visual perception, having a similar appearance to an object or person, thus providing a depiction of it. Images accomodate various types of noises which are due to sensor defects, lens distortion, software artifacts, blur etc. Denoising an image not only aims at removing the undesired noise but also, at retaining the features of the original image. The same goes...
In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing...
An image can be basically defined as an object that represents visual observation, which can be created and stored in the electronic form, produced from an optical device. When we take a photograph, there can be many problems associated with that particular image. Among them, one of the main issue is the blur of the image. Blur can be defined as something which will become vague or less distinct....
In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support...
Noise in an image is caused due to various reasons. Removal of noise in an efficient way is a big challenge for researchers. In this paper, one dimensional signal denoising based on weighted regularized least square method is mapped to two dimensional image denoising. The proposed technique of image denoising based on least square is experimented on standard images sub-jected to different noises with...
Detailed and accurate extraction of information using hyperspectral imagery has a much wider potential compared to other data acquired by remote sensing. This mainly corresponds to the bulk of information which was easily embedded on each hyperspectral image. A large number of kernel based techniques were formulated and used in this regard. Since the dimension of the hyperspectral image (HSI) is very...
This paper deals with the performance evaluation of sparse banded matrix filter applied for Face recognition. Edges extracted using the sparse banded matrix filter (ABFilter) is used as a feature descriptor for face recognition. The classification is done using Random Kitchen Sink which is accessed through GURLS library and also classified using Support Vector Machines (SVM). The experimental evaluation...
Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares...
Normally images obtained from satellites are of low-contrast type which hides major information carried by the image. Hence, image restoration is necessary in the image processing domain to extract all the information present in the images. The low contrast satellite image restoration based on adaptive histogram equalization combined with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform...
Semantic textual similarity measures the semantic equivalence between a pair of sentences. Lexical overlapping approach evaluates similarity among a sentence pair depending on the number of terms the sentence pair shares. The similarity can be measured at same level of abstraction or at multi levels. This paper presents the influence of token similarity measures using lexical overlap semantic similarity...
In signal processing, many a time people deal with smooth stationary signals mixed with sharp spikes and most of the time their analysis demands separation of the smooth and spike elements. In this paper, we propose a methodology for this kind of separation based on the well-known notion of using an over complete dictionary to define an underdetermined system of linear equations and picking out its...
Most of the natural signals are complex and are highly time varying, since they are non stationary in nature. In this paper, a comparative study for separating spikes and smooth signal components from a non-stationary signal are performed based on different overcomplete dictionaries. The experiment is evaluated using the sparse representation with different bases such as the Discrete Cosine Transform...
Smoothing and differencing is one of the major important and necessary step in the field of signal processing, image processing and also in the field on analytical chemistry. The search for an efficient image smoothing and edge detection method is a challenging task in image processing sector. Savitzky Golay Filters are one among the widely used filters for analytical chemistry. Even though they have...
Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal...
High-performance variable speed drives require a better transient performance compared with the steady-state operation. Nowadays, vector control and direct torque control (DTC) are popular methods for high performance drives. This paper presents a new vector control method for voltage-source inverter fed induction motors using look-up tables. The proposed method uses a predetermined look-up table...
In this paper, we present an effective pre-processing algorithm for band selection approach which is an essential task in hyperspectral image analysis. The pre-processing algorithm is developed based on the average inter-band block-wise correlation coefficient measure and a simple thresholding strategy. Here, the threshold parameter is found based on the standard deviation of the average inter-band...
In this paper, we propose a fusion technique based on framelets to obtain super resolution image from sub-pixel shifted, noisy, blurred low resolution images. This method has high advantages over all existing methods. A Tight frame filter bank provides symmetry and has a redundancy that allows for approximate shift invariance which leads to clear edges, high spatial information with effective denoising...
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