One-dimensional Histogram Research Articles

Invariant feature extraction for 3D model retrieval: An adaptive approach using Euclidean and topological metrics

With the fast development of 3D model construction and widespread popularity of 3D graphic engines, more applications employ 3D geometric models to provide an interactive environment. As the number of 3D models increases, some 3D model retrieval systems have been proposed for indexing and matching these models. An important issue in a retrieval system is feature extraction. An efficient and invariant feature is a global shape distribution that collects some geometric properties of a model. The D2 shape descriptor by Osada et al. is a one-dimensional histogram of Euclidean distances between two random points. Although the D2 is effective for some cases, it changes when the model deforms. We propose two shape descriptors in this paper: GD, which is the topological metric, and ASF, which combines both Euclidean and topological metrics. The topological metric is an invariant deformation factor. The two features are also robust against common geometric processing, including scaling, rotation, resampling, compression, and remeshing. In experiments, we implement these methods and confirm their feasibility.

A Method for 3D Sphere Detection Using one-Dimensional Histogram and Polytope Method

We propose a new method for a detection of a 3D sphere. This method uses polytope method, a kind of minimization algorithm. One-dimensional histogram is used to detect a 3D sphere. The histogram has two characteristics. (1) The distribution of the histogram changes if the parameters of representing the 3D sphere changes. (2) The value of highest frequency of histogram becomes maximum if the best parameters are obtained. Therefore, the maximum value of highest frequency of histogram is searched to obtain the best parameters of a 3D sphere by using polytope method. By using polytope method, proposed method can detect a 3D sphere from 3D vertex data including other shapes without a large memory space and a lot of processing time.

Image Segmentation Method Based on Fisher Criterion and Genetic Algorithm

Precise recognition of the weed by computer vision, furthermore raising the weeding efficiency, reducing the use of herbicide, and decreasing the pollution to the environment is one of the key technologies in the field of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on fisher criterion and improved adaptive genetic algorithm is proposed. This method can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence. The result shows that the proposed approach has better immunity to Salt and Pepper Noise and greatly shortens the time of image segmentation.

Feature Description and Image Retrieval Based on Visual Attention Model

B ased on the bottom-up human visual attention model, a procedure for feature description and image retrieval is proposed. In the procedure, an integrated saliency map for a given image , which reflects the significance of regions in the image, is generated first, then two region segmentation methods, the image segmentation using the maximum entropy of the one-dimensional histogram and the region growing based on the focus of attention, are defined and used to separate the image into different regions. Here, the focus of attention is obtained from the saliency map and the secondary or uncorrelated regions can be filtered then . Hereafter, combining the saliency map and the segmented image, the first three most salient regions, defined as the salient region set, are obtained in term s of the average saliency of the regions. Radius entropy and angle entropy are also defined and performed to describe the global feature s and a shape feature for saliency regions is used to describe the local feature. Finally, the distance s between the features are calculated to measure the similarity between images. Experimental results show ed that our method can reflect the semantic feature of a given image to a certain extent, and the method is beneficial for content-based image retrieval.

Image Segmentation Based on Improved Adaptive Genetic Algorithm

Using computer vision technology to accurately identify weeds and crops, positioning weed and spraying of weedcide has become a hotspot of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on two-dimensional histogram and Improved Adaptive Genetic Algorithm is proposed. In the method, the genetic algorithm carries on the global optimization to get the threshold rapidly, and the computational method for crossover probability and mutation probability of the Adaptive Genetic Algorithm is improved. The Improved Adaptive Genetic Algorithm can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence in Simple Genetic Algorithm. The result shows that the proposed approach greatly enhances the speed of thresholding and has better immunity to Salt and Pepper Noise.

Proposal of Driver Symbol Mark Detection Using One-Dimensional Histogram Figure Extraction Method

Nowadays, research and development in vehicle safety system has been intensively conducted. In the research field of intelligent transportation systems (ITS), various suggestions have been made concerning road signs which can be automatically recognized by driver-support systems. But, human factors are important risks of traffic accidents. However, research is focused on road signs, in the driver information analysis is not enough. So, in this study we describe a new method to extract driver symbol mark using one-dimensional histogram. The method utilizes the Polytope method which is one of minimization algorithms. For the extraction of figures, one-dimensional histogram is used. On the method of this study, the algorithm is simple, the memory space is very small, and the processing time is very short.

Rotation invariant texture classification using LBP variance (LBPV) with global matching

Local or global rotation invariant feature extraction has been widely used in texture classification. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally rotation invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.

Proposal of a method to extract straight line and circle using one-dimensional histogram

A new method for fast extraction of straight line and circle is proposed in this study. The method utilizes the Polytope method which is one of minimization algorithms. For the extraction of figures, one-dimensional histogram is used. Basically, main algorithm of the extraction of straight line is the same as those of circle and ellipse. Only the definition of histogram and the evaluation function are changed according as figures. By the comparison with Hough transform, it is understood that the using of memory space is very small and processing time is very short.

Proposal of a method to extract arbitrary figure using one-dimensional histogram

Proposal of a method to extract arbitrary figure using one-dimensional histogram

Arbitrary-Shaped Cluster Separation Using One-Dimensional Data Mapping and Histogram Segmentation

Of the many clustering methods proposed for separating arbitrarily shaped clusters, most had drawbacks in parameter sensitivity and high-computational cost requiring large amounts of memory. We propose one-dimensional (1D) mapping for separating arbitrarily shaped clusters using a list of neighbors. After mapping, we apply a discriminant threshold selection to the histogram of the data distribution in 1D space. We verified the feasibility of performance in experiments on synthetic toy data, image, and video segmentation.

The ODF-Spectrum a New and Comprehensive Characterization of the Degree of Anisotropy of Orientation Distributions

The texture index F2 (1£ < F2 £ ¥ , F2 = 1 – random distribution) is up to now the only parameter used in a global way to characterize the degree of anisotropy of a given ODF – the foremost question to be answered in applied TA. However, one number is a quite poor characteristic, and a concrete F2-value can hardly be interpreted. It depends on many parameters and may belong to very different orientation distributions. Introducing so-called ODF-channels a one-dimensional ODFchannel histogram can be got for any ODF. Its form and spectral properties provide a comprehensive characterization of the character of anisotropy of a given ODF.

Thin film description by wavelet coefficients statistics

Descriptive and robust features based on wavelet transform coefficients are proposed for a multiscale thin film image analysis. The features are based on one- and two-dimensional histograms of the wavelet transform coefficients and they can be calculated for every scale of the wavelet decomposition. A one-dimensional histogram model is extended to describe also two-dimensional histograms, by means of calculating marginal histograms and by sampling the two-dimensional histograms in orientation. A computer experiment has been performed to demonstrate correspondence of the derived features to various physical phenomena.

Multidimensional Histograms for Density Modification

Publisher Summary Density modification improves the quality of an approximate electron-density map by imposing physical constraints based on some conserved features of the correct electron density map. Density modification methods generally require an initial map with substantial phase information. In most cases, these phases are obtained from multiple isomorphous replacement (MIR) or multiwavelength anomalous dispersion (MAD), and these phases are of pivotal importance when the experimental source of phase information is bimodal as it is in single-wavelength anomalous scattering (SAD) and single isomorphous replacement methods (SIR). The most commonly used density modification method is solvent flattening that exploits the observation that the solvent region of an electron density map is featureless at medium resolution because of the high thermal motion and disorder of the solvent molecules. Xiang and Carter proposed extending the density histogram to include relationships between neighboring density values via multidimensional histograms defined as joint probabilities of the density values and their higher-order derivatives. In the conventional one-dimensional (1D) electron density histogram matching method, a one-to-one mapping is made on the original electron density to the new electron density so that the density histogram of the modified map matches that of the ideal histogram. The two-dimensional (2D) histogram specifies not only the probability of the electron density value for a given grid point in the map but also the local environment around that grid point as reflected by the density gradients that arise from chemical bonding. The strategy of using alternating 1D histogram matching to achieve 2D histogram matching can be generalized to the matching of higher dimension histograms.

A two-dimensional histogram-matching method for protein phase refinement and extension.

A new method has been developed for protein phase improvement using the joint distribution of the electron density and its gradient (two-dimensional histogram) as a constraint in a density-modification procedure. Matching the two-dimensional (2D) histogram of a given map to that of an ideal 2D histogram was achieved through alternating applications of one-dimensional (1D) histogram matching on electron density and on density gradient. The 2D histogram-matching method was compared with the 1D density histogram-matching method for phase refinement and extension starting from either medium-resolution or high-resolution data on three different types of phases. These included phase refinement and extension using MIR phases of T(6) insulin and phases with randomly generated errors. The test results demonstrated significant improvement of the phases and the overall map quality using the 2D histogram-matching method compared with the 1D density histogram-matching method in each of the three test cases.

Gray level image thresholding based on fisher linear projection of two-dimensional histogram

Thresholding is an important form of image segmentation and is a first step in the processing of images for many applications. The selection of suitable thresholds is ideally an automatic process, requiring the use of some criterion on which the selection is based. Most such criteria are only based on the one-dimensional (ID) gray-level histogram of image. In an effort to use more information available in the image, the present approaches use the criteria based on the two-dimensional (2D) histogram of the image. However, these methods which simply extend the 1D-histogram-based algorithms to the 2D histogram give rise to the exhaustive search for the threshold values. In this paper, an optimal projection of the 2D histogram is derived by applying Fisher Linear Discriminant. The optimal projection turns out to be the local average histogram. Analysis and experimental results show that, thresholding an image based on the local average histogram, one can obtain segmentation better than that of those simply using criteria based on 2D histogram, while the spent computation time is as much as that costed by the ones using criteria based on 1D histogram.

Cerebrospinal fluid-intracranial volume ratio measurements in patients with HIV infection: CLASS image analysis technique.

To prospectively study the cerebrospinal fluid volume-total intracranial volume ratio (CSF/ICV) in human immunodeficiency virus (HIV)-infected patients at various stages of disease. A total of 258 volume measurements were obtained with use of a 1.5-T magnetic resonance (MR) imager and the cluster localized automated spherical segmentation technique (which reduces two-dimensional pixel data from dual spin-echo MR images to a one-dimensional histogram) in 69 control subjects and 189 HIV-infected patients. The CSF/ICV was statistically significantly increased in patients with late-stage (Centers for Disease Control and Prevention group IV) acquired immunodeficiency syndrome (AIDS) (0.16 +/- 0.05 [standard deviation]) compared with seronegative control subjects (0.12 +/- 0.03) and patients without symptoms (0.13 +/- 0.03). No substantial change in CSF/ICV occurs until development of late-stage AIDS.

Analyses of DNA content of living spermatozoa using flow cytometry techniques.

A two-dimensional fluorescence analysis of spermatozoa stained with Hoechst 33342 was carried out using an Epics V flow cytometer. This analysis involved the measurement of fluorescence both in the conventional manner at 90 degrees and at a narrow forward angle (nominally 0 degree) to the direction of the interrogating laser beam. The arrangement provided an optical means of distinguishing the orientation of flattened particles passing through the interrogation system and enabled the verification and reinterpretation of one-dimensional (90 degree) fluorescence histogram data obtained previously. We now consider that the distinction between live and dead spermatozoa was over simplified and that the observed differences are due partly to an artefact of orientation. It has been confirmed that our earlier use of the low fluorescence peak, consisting of (bull, rabbit, sheep and pig) spermatozoa passing through the interrogation point obliquely to both detectors, as criterion for sorting X- from Y-bearing spermatozoa, is more practical than the use of a very much smaller subpopulation, based on limits imposed by selecting only spermatozoa accurately orientated with their narrow edges to one detector, while measurement of total fluorescence (DNA content) was made simultaneously from a broad face by the other detector. We report marked differences in the rate of staining and in attainment of a staining equilibrium between spermatozoa from different animals or from different ejaculates collected on the same or different days from the same animal. This variation introduces an element of subjectivity into the use of flow cytometry for sorting X- and Y-spermatozoa.(ABSTRACT TRUNCATED AT 250 WORDS)

A segmentation algorithm for color images

A method that combines clustering and region merging for color image segmentation is presented. We start with clustering based on recursive one-dimensional histogram analysis, where the parameter that controls the segmentation process has been set to produce an oversegmentation. The resulting regions are then merged on the basis of a criterion that takes into account color similarity and spatial proximity.

Thresholding of digital images using two-dimensional entropies

Thresholding is an important form of image segmentation and is a first step in the processing of images for many applications. The selection of suitable thresholds is ideally an automatic process, requiring the use of some criterion on which to base the selection. One such criterion is the maximization of the information theoretic entropy of the resulting background and object probability distributions. Most processes using this concept have made use of the one-dimensional (1D) grey-level histogram of the image. In an effort to use more of the information available in the image, the present approach evaluates two-dimensional (2D) entropies based on the 2D (grey-level/local average grey-level) “histogram” or scatterplot. The 2D threshold vector that maximizes both background and object class entropies is selected.