Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Isomorphism Distance in Multidimensional Time Series and Similarity Search

Abstract: Describing the similarity of time series as distance is the basis for most of data mining research. Existing studies on similarity distance is based on the "point distance" without considering the geometric characteristics of time series, or is not a metric distance which doesn't meet the triangle inequality and can't be directly used in indexing and searching process. A method for time series approximation representation and similar measurement is proposed. Based on the subspace analysis representation, the time series are represented approximately with an isomorphic transformation. The basic concepts and properties of the included isomorphism distance are proposed and proved. This distance overcomes the problem when other non-metric distance is used as the similar measurement, such as the poor robustness and ambiguous concepts. The proposed method is also invariant to translation and rotation. A new pruning method for indexing in large time series databases is also proposed. Experimental results show that the proposed method is effective.

Device for measuring distance using stereographic picture

Abstract: PROBLEM TO BE SOLVED: To eliminate the reduction of distance measuring resolution in a long distance due to parallax that a picture element obtained through processing of a stereographic picture is set as a unit, and to improve the distance measuring resolution in a range from short distance to long distance. SOLUTION: A pair of pictures photographed by a stereoscopic camera 10 are processed by a stereo processing part 30 to calculate a distance of a city clock and obtain a correlation thereof every small area of the respective picture, and a stereo matching is performed to specify the corresponding small area and a displacement of the picture element (parallax) generating corresponding with a distance to an object is imaged as a distance data, thus generating a distance picture. Further, a recognition processing part 40 conducts stereo matching using both a reference picture and a comparison picture so as to obtain a parallax (sub pixel element) of one picture element or less, and a parallax that the picture element obtained from the distance picture is set as a unit is interpolated by a resolution of one picture element or less. Thus, the reduction of distance measuring resolution in a long distance can be eliminated and the accuracy of distance measurement in a range from short distance to long distance be also secured.

Image analysis for the study of chromatin distribution in cell nuclei with application to cervical cancer screening

Abstract: This thesis describes a set of image analysis tools developed for the purpose of quantifying the distribution of chromatin in (light) microscope images of cell nuclei. The distribution or pattern of chromatin is influenced by both external and internal variations of the cell environment, including variations associated with the cell cycle, neoplasia, apoptosis, and malignancy associated changes (MACs). The quantitative characterisation of this pattern makes possible the prediction of the biological state of a cell, or the detection of subtle changes in a population of cells. This has important application to automated cancer screening. The majority of existing methods for quantifying chromatin distribution (texture) are based on the stochastic approach to defining texture. However, it is the premise of this thesis that the structural approach is more appropriate because pathologists use terms such as clumping, margination, granulation, condensation, and clearing to describe chromatin texture, and refer to the regions of condensed chromatin as granules, particles, and blobs. The key to the structural approach is the segmentation of the chromatin into its texture primitives. Unfortunately all of the chromatin segmentation algorithms published in the literature suffer from one or both of the following drawbacks: (i) a segmentation that is not consistent with a human's perception of blobs, particles, or granules; and (ii) the need to specify, a priori, one or more subjective operating parameters. The latter drawback limits the robustness of the algorithm to variations in illumination and staining quality. The structural model developed in this thesis is based on several novel low-, med-ium-, and high-level image analysis tools. These tools include: a class of non-linear self-dual filters, called folding induced self-dual filters, for filtering impulse noise; an algorithm, based on seeded region growing, for robustly segmenting chromatin; an improved seeded region growing algorithm that is independent of the order of pixel processing; a fast priority queue implementation suitable for implementing the watershed transform (special case of seeded region growing); the adjacency graph attribute co-occurrence matrix (AGACM) method for quantifying blob and mosaic patterns in the plane; a simple and fast algorithm for computing the exact Euclidean distance transform for the purpose of deriving contextual features (measurements) and constructing geometric adjacency graphs for disjoint connected components; a theoretical result establishing an equivalence between the distance transform of a binary image and the grey-scale erosion of its characteristic function by an elliptic poweroid structuring element; and a host of chromatin features that can be related to qualitative descriptions of chromatin distribution used by pathologists. In addition, this thesis demonstrates the application of this new structural model to automated cervical cancer screening. The results provide empirical evidence that it is possible to detect differences in the pattern of nuclear chromatin between samples of cells from a normal Papanicolaou-stained cervical smear and those from an abnormal smear. These differences are supportive of the existence of the MACs phenomenon. Moreover the results compare favourably with those reported in the literature for other stains developed specifically for automated cytometry. To the author's knowledge this is the first time, based on a sizable and uncontaminated data set, that MACs have been demonstrated in Papanicolaou stain. This is an important finding because the primary screening test for cervical cancer, the Papanicolaou test, is based on this stain.

Computation of the surface area of natural snow 3d images from x-ray tomography: two approaches

Abstract: Once fallen on the ground, snow undergoes a structure metamorphism governed by local temperature and humidity fields. Local 3D curvature is a governing parameter of metamorphism, whereas surface area, directly related to surface energy, can provide a valuable bridge between 3D fine-scale and 1D field-scale snow cover models. X-ray tomography was applied to natural snow at ESRF in order to parameterise the fine-scale behaviour of snow in avalanche risk prediction models. Starting from raw data consisting of a B/W 3D data file, two main approaches are considered. The former uses a triangulation procedure derived from the “marching cube” algorithm. Before triangulation, the B/W data are converted into grey levels and preprocessed in order to smooth the final mesh. The other uses a distance map of the object, from which the field of normal vectors is computed. Assuming the tangent plane approximation, the effective projected area of each surface voxel is derived from simple geometric laws. A cross-validation of both methods is provided on a natural snow image.

Watershed Segmentation based on Distance Transform and Edge Detection Techniques

Abstract: An edge detection algorithm for digital images is proposed in this paper. Edge detection is one of the important and most difficult tasks in image processing and analysis. In images edges can create major variation in the picture quality where edges are areas with strong intensity contrasts. Edges in digital images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. This paper proposed an effective edge detection algorithm based morphological edge detectors and watershed segmentation algorithm using distance transform. The result confirms that the proposed algorithm is found to yield satisfactory and efficient segmentation of the digital images for edge detection. Experimental result presented in this paper is obtained by using MATLAB.