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Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing...
The study integrates the resonance principle of quantum physics and random walks concept to analyze the image multi-understandings. The quantum model of the photo and bounded electron interaction is recruited to simulate the machine image understanding problems. The pixel or the unit cell of the image will be resonance when the pixel or the unit cell satisfied the given quantum condition. Besides,...
Static occlusions are a common impediment to successful object tracking in many realistic scenes. Knowledge about the locations of occlusions in the field of view of video cameras can allow tracking algorithms to successfully handle occlusion events. We present a simple and efficient rule-based method for finding large, rigid occluders in a scene by analysis of images from a single camera. Pixels...
Counting the total number of chromosomes in each metaphase cell and computing the DNA index can predict the outcome of pediatric patients' treatment. The primary obstacle affecting the result is how to count the number of chromosomes contained in the overlapped chromosomes. In this paper, a counting algorithm for overlapped chromosomes was presented. In the algorithm, we mainly use ultra erosion and...
The aim of terrain relative navigation systems (TRN) is to augment inertial navigation by providing position estimates relative to known lunar surfaces. Also the purpose of TRN systems is to assist a lunar landing spacecraft with precise and safe landing. Such systems collect the height values from the lunar surface with the help of active range sensors which are then matched within a terrain digital...
We propose efficient and effective sequential-scan algorithms for intelligent emergency planning, spatial analysis and disaster decision support through the use of Voronoi Tessellations. We propose a modified distance transform algorithm to include complex primitives (point, line and area), Minkowski metrics, different weights, obstacles and higher-order Voronoi diagrams. Illustrated examples demonstrate...
The inclusion of the free-running Purkinje network in computational simulations provides a significant insight into understanding the mechanisms of cardiac pathophysiologies. However, its automatic extraction is challenging due to the presence of abundant local complexities. We thereby introduce a novel algorithm to track the Purkinje fibers in high resolution magnetic resonance (MR) images. Our formulation...
Spatiograms were generalization of histograms, which can harvest spatial information of images. The similarity measure is important when applying spatiograms to various computer vision problems such as tracking and image retrieval. The original proposed measures use Mahalanobis distance of coordinate mean to measure spatial information in spatiograms. However, spatial information which is described...
Logo detection is important for brand advertising and surveillance applications. The central issues of this technology are fast localization and accurate matching. Based on key traits analysis of common logos, this paper presents a two-stage detection scheme based on spatialspectral saliency (SSS) and partial spatial context (PSC). SSS speeds up logo location and avoid the impact of cluttered background...
We propose an algorithm that simultaneously extracts disparities and alpha matting information given a stereo image pair. Our method divides the reference image into a set of overlapping, partially transparent color segments. Each segment pixel is assigned an alpha value and color. The disparity inside the segment is modeled via a plane. The goodness of alphas, colors and disparity planes is measured...
We study the cosegmentation problem where the objective is to segment the same object (i.e., region) from a pair of images. The segmentation for each image can be cast using a partitioning/segmentation function with an additional constraint that seeks to make the histograms of the segmented regions (based on intensity and texture features) similar. Using Markov random field (MRF) energy terms for...
Image segmentation is, in general, an ill-posed problem and additional constraints need to be imposed in order to achieve the desired result. Particularly in the field of medical image segmentation, a significant amount of prior knowledge is available that can be used to constrain the solution space of the segmentation problem. However, most of this prior knowledge is, in general, vague or imprecise...
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body shape. We automatically learn the mark (shape) process from training video by estimating a mixture of Bernoulli shape prototypes along with an extrinsic...
National forest inventories (NFI) are essential for countrywide estimations of a wide range of forest functions. Our research aim is to derive measurable forest features out of airborne image data by using automatic computer-vision based methods. This paper focuses on tree layer detection of high resolution ADS40 data for automation. Preliminary experimental results of mean-shift segmentation method...
This paper proposes a novel and effective invariant shape representation by Radon and stationary wavelet transforms for images with complex inner shapes. The proposed method is invariant to general geometrical transformations. Instead of analyzing shapes directly in the spatial domain, the proposed method retrieves features in Radon transform domain by statistical and spectral analysis to make shapes...
In this paper, we address the problem of recovering a hyperspectral texture descriptor. We do this by viewing the wavelength-indexed bands corresponding to the texture in the image as those arising from a stochastic process whose statistics can be captured making use of the relationships between moment generating functions and Fourier kernels. In this manner, we can interpret the probability distribution...
This paper introduces a feature descriptor called shape of Gaussian (SOG), which is based on a general feature descriptor design framework called shape of signal probability density function (SOSPDF). SOSPDF takes the shape of a signal's probability density function (pdf) as its feature. Under such a view, both histogram and region covariance often used in computer vision are SOSPDF features. Histogram...
When trying to discover knowledge on a collection of data, one of the first arising tasks is to identify groups of similar objects, that is, to carry out cluster analysis for obtaining data partitions. Thus, a decision must be taken for choosing the clustering result that produces the best data partition for a given data collection. In order to support such a decision, indexes for measuring the quality...
This paper presents despeckling and information extraction using non-quadratic regularization. The novelty of this paper is that instead of the Gaussian prior model a Gauss-Markov random field model is chosen, because it can efficiently model textures in the images. The iterative procedure consists of noise-free image and texture parameter. The experimental results show that the proposed method satisfactorily...
Level set tracking has been widely used for tracking contours of objects. However, traditional methods are sensitive to noise, partial occlusions, background disturbance and some other factors, especially there exist serious problems for non-rigid objects tracking. With respect to this point, we propose a level set-based tracking framework in which color and dynamical shape priors are fused together...
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