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In this paper, mixed information similarity measure and a multidimensional density estimation method based on multivariate Edgeworth series expansion are proposed and assessed for the task of multi-temporal change detection. To unify mutual information and variational information, mixed information is proposed to quantify the degree of dependence between two random variables, which are intuitively...
In this paper, we present a novel method for segmenting ore images specifically for estimating the size distribution of ore material on conveyer belt. The segmentation system uses the mean shift and watershed algorithm. The mean shift algorithm is used to identify pixel clusters of particular modes of the probability density function of the image data. The pixel clusters are then used to generate...
This paper briefly summaries relation of camera configuration and extended stereo baseline to distortions that can be caused by camera shooting. The reasons, why to use these are also mentioned in the paper. Influence of nontraditional configuration of the cameras on the depth map generation of the 3D video content has been practically described and measured. Depth map dynamic range compression and...
Eye gaze determines the view point, focus of attention and interest of an individual. Gaze estimation systems compute the direction of eye gaze. These systems play an important role in human-computer interaction. The gaze pointing systems is a replacement for existing input devices in gaze communication applications. The gaze tracking methods are based on interpolation, 2D geometric transform mapping,...
Visual interpretation of SAR images plays a critical role in remote sensing applications. For obtaining the image suitable for human observation, a new method of visualization for SAR images is proposed in this paper. The proposed algorithm takes a negative exponential transformation on the original SAR image. The parameter of the transformation can be computed rapidly based on the characteristic...
This paper presents a new approach which combines the Kernel Density Estimation and Trust Region algorithm for tracking objects in video sequences. Kernel density estimation (KDE) of the object's color distribution is built from the object region and used to generate a probability map for each incoming frame. Tracking is accomplished by localizing blobs in the maps. Compared with color histograms...
This paper presents a image restoration method based on knife-edge PSF (Point Spread Function) estimation and Edge-preserving Regularization Model for the China-Brazil Earth Resource Satellite-2 (CBERS-2) image. Based on typical image area, the knife-edge method is used to obtain edge spread function and point spread function. The Huber-Markov prior model is employed to establish the regularization...
In a recent paper we developed a formal theory of demodulation for polarimeters with arbitrary modulation schemes. This theory provides a framework for artifact free reconstruction of polarimetric data provided a band limited scene. Here we apply that theory to three different configurations of 2-D spatially modulated microgrids.
Summary form only given. In this paper, we propose a deblurring framework based on a factor graph representation of the image and the image formation process. Each pixel is described by a variable node, while the statistical relation among pixels is formulated by two sets of check nodes, describing the local image structures and the image formation process, respectively. Belief propagation is employed...
In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We...
We develop a new perspective invariant feature space representation of remotely sensed objects, regarding the features themselves as primitive observables of the 3D objects and to estimate them from multiple sensor measurements. This is formulated as an inverse problem in the feature coefficients. Once the coefficients are estimated they may be used to derive higher level features used by machine...
This paper proposes a Two Minimum Three Step Search (2MN3SS) block matching algorithm that improves the estimation accuracy and retains the simplicity of the Three Step Search (3SS) algorithm. Motion estimation using Full Search (FS) algorithm is computationally extensive. Other previously proposed algorithms reduce the computation by limiting the number of search points at the cost of higher distortion...
A new method of reconstructing three-dimensional (3D) tubular structures from cone-beam projections is proposed in this paper. Minimal path method is applied to detect 2D tubular structures in the cone-beam projections. The extracted 2D information are fused to design a weight function over the 3D volume so that minimal path method can be applied again to extract centerlines of 3D tubular structures...
The proper segmentation of the vascular system of the retina currently attracts wide interest. As a precious outcome, a successful segmentation may lead to the improvement of automatic screening systems. Namely, the detection of the vessels helps the localization of other anatomical parts and lesions besides the vascular disorders. In this paper, we recommend a novel approach for the segmentation...
A new method to estimate the variance of noise from the composite magnitude signal of GRAPPA reconstructed images is presented. Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. However, the reconstruction process yields to a variance of noise value which is dependent on the position within the image. The proposed method uses information of...
Having ground truth is critical for evaluating segmentation algorithms and estimating the ground truth from a collection of manual segmentations remains a hard problem. A proper estimation approach should take into account and compensate for the inter-rater variation. In this paper, we conduct an analysis of manual segmentations in order to have a better understanding of the pattern of the variation...
While basic principles of microtubule organization are well understood, much remains to be learned about the extent and significance of variation in that organization among cell types and conditions. Large numbers of images of microtubule distributions for many cell types can be readily obtained by high throughput fluorescence microscopy but direct estimation of the parameters underlying the organization...
A fast algorithm that approximately estimates the circularity of a digital object is proposed. The algorithm is shown to be capable of incorporating the existing empirical measures of circularity, which are mostly based on area and perimeter computation. The minimum-area orthogonal cover of the object is obtained by a fast combinatorial technique that simultaneously provides approximate measures of...
Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel or point spread function is unknown. Despite of exhaustive research over the last few decades, blind image deconvolution still remains an unsolved problem. In this paper, we present a novel morphology based initial estimation technique of true image for the Iterative Blind Deconvolution (IBD) of linearly...
This paper describes a new bubble segmentation algorithm based on shape from shading for in-situ microscopy. An in-situ microscope is an instrument to capture and analyze intensity images of cells inside of a bioreactor with minimal operator intervention and without the risk of culture contamination. For bubble segmentation, the closed bubble boundaries are first extracted by thresholding a depth...
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