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In this paper, we present a novel, effective, and efficient approach to image retrieval. Basically, it is a fusion of both global and local features of images, which achieves significantly higher retrieval competency. Initially, the global features of images are determined using polar cosine transforms (PCTs). For local features, we use rotation invariant local binary patterns (RLBP) rather than using...
Hyperspectral image classification is a challenging task due to its large dimension. Dimensionality reduction has an important role in hyperspectral image classification as it reduces redundancy by mapping the higher dimensional data into lower dimension without losing any valuable information in the spectral signature of each pixel. In this paper, dimensionality reduction is achieved by band partitioning...
The rich spectral information in hyperspectral imagery gives rise to huge storage and transmission costs. Dimensionality reduction aims to reduce the space complexity in hyperspectral imagery by projecting data into a low-dimensional subspace. There has been an increasing interest in dimensionality reduction driven by random projections due to its data-independent representation as well as desirable...
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments...
Shape-specific points are special data points invariant to translation, scaling, and rotation. The radius weighted mean (RWM) and the system center are two examples of shape-specific points. These points feature in contour registration, color quantization, and the detection of rotationally symmetric shape orientations. This study uses shape-specific points to cluster nonlinearly separable data into...
In this paper, we consider non-Bayesian periodic parameter estimation and present a new class of mean-cyclic-error (MCE) lower bounds based on integral transform of the likelihood-ratio (LR) function. The MCE bounds in this class are valid for any cyclic-unbiased estimator, in the Lehmann sense, with uniform cyclic performance. Based on the general class of MCE bounds, we propose a novel MCE bound,...
This paper develops an exponentially convergent observer for a reaction-advection-diffusion integro-partial differential equation (IPDE) with time-dependent coefficients, via the PDE backstepping method. For the (I)PDEs with timedependent coefficients, the backstepping transform gain kernel system is an (integral) evolution equation, and its coefficients also depend on time, which makes the derivation...
Image compression by the warped stretch transform is introduced, where the input image is reshaped by a signal-dependent mapping i.e. designed “warp kernel”. The warped image can be downsampled at a lower uniform rate for the same PSNR, effectively achieving reversible and context-aware non-uniform sampling.
Compactness conditions and estimates for the singular values of the sandwiched Airy transform fAg in ¿2(R) are investigated for suitable functions f (x), g(x), x G R. Sufficient conditions for inclusion of the operator f Ag in the Schatten — von Neumann classes Sp, p G (0,2), are obtained. In particular, conditions for inclusion of the operator fAg in the trace class are found.
A Cauchy problem for the wave equation with data given on a time-like hyperplane is considered. The problem is ill-posed. Assuming the data to be such that the problem is solvable, we propose a constructive procedure, which provides the solution.
In this paper a novel fusion strategy for multiband images is presented. The proposed technique preserves the strong and weak features in the multiband images in an appropriate proportion while fusing. The fused image is computed with the normalized weighted average of the pixels at the corresponding location. To obtain the weight, we find the difference image that provides important local details...
For intelligent vehicle systems, lane detection is still a challenging task because it must cope with various road environments. In this paper, we propose a reliable method with Gabor filters. The proposed approach consists of two step. In the first step, the vanishing-point locations is estimated by the texture feature based method. The key attributes of this method consist of the dominant texture...
Traditional hand vein recognition technology is usually based on 2D infrared images in which the hand vein patterns are unavoidable distorted with hand posture changes. Point cloud matching vein recognition based on Kernel Correlation method provides a new perspective to hand vein recognition but also suffers from posture change. In this paper, a 3D point cloud registration algorithm is proposed to...
Design of a computer-aided automatic system is very important for identification of different ocular diseases. A vital concern within this framework is the accurate retinal blood vessel extraction. This paper extracts vessels using curvelet transform, morphological operation, matched filtering and Differential Evolution based optimal clustering. Curvelet transform is implemented to enhance vessel...
In this paper, we propose a new approach for the person re-identification problem, discovering the correct matches for a query pedestrian image from a set of gallery images. It is well motivated by our observation that the overall complex inter-camera transformation, caused by the change of camera viewpoints, person poses and view illuminations, can be effectively modelled by a combination of many...
Traditional face recognition methods such as Principal Components Analysis(PCA), Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are linear discriminant methods, but in the real situation, a lot of problems can't be linear discriminated; therefore, researchers proposed face recognition method based on kernel techniques which can transform the nonlinear problem of inputting...
Significant feature extraction for texture retrieval can be perfectly achieved using multiscale image decompositions, such as contourlet and Gabor representations. In this paper we compare the efficiency of contourlet decomposition variants and Gabor transform in terms of texture search and retrieval rates. Two distinct approaches, namely energy computation and generalized Gaussian distribution modeling...
A disparity map is usually obtained by stereo images using a stereo matching method. Edge boundaries in the disparity map separate two different objects. Therefore, edge preservation is one of important issues in the stereo matching method. A conventional distance transform was proposed to preserve edge boundaries in the disparity map. However, this method has a complexity problem because of its iterative...
Multi-focus image fusion is an important technique that extracts sharpness regions from multiple images and composites them into a fully focused image. In this paper, a novel spatial domain based fusion algorithm for multi-focus image through gradient based decision map construction using morphology and active contour model is proposed. Firstly, the original focus maps are constructed based on the...
Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region...
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