Distance transform

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

Text line segmentation for gray scale historical document images

Abstract: In this paper we present a new approach for text line segmentation that works directly on gray-scale document images. Our algorithm constructs distance transform directly on the gray-scale images, which is used to compute two types of seams: medial seams and separating seams. A medial seam is a chain of pixels that crosses the text area of a text line and a separating seam is a path that passes between two consecutive rows. The medial seam determines a text line and the separating seams define the upper and lower boundaries of the text line. The medial and separating seams propagate according to energy maps, which are defined based on the constructed distance transform. We have performed various experimental results on different datasets and received encouraging results.

Visualizing time-varying features with TAC-based distance fields

Abstract: To analyze time-varying data sets, tracking features over time is often necessary to better understand the dynamic nature of the underlying physical process. Tracking 3D time-varying features, however, is non-trivial when the boundaries of the features cannot be easily defined. In this paper, we propose a new framework to visualize time-varying features and their motion without explicit feature segmentation and tracking. In our framework, a time-varying feature is described by a time series or Time Activity Curve (TAC). To compute the distance, or similarity, between a voxel's time series and the feature, we use the Dynamic Time Warping (DTW) distance metric. The purpose of DTW is to compare the shape similarity between two time series with an optimal warping of time so that the phase shift of the feature in time can be accounted for. After applying DTW to compare each voxel's time series with the feature, a time-invariant distance field can be computed. The amount of time warping required for each voxel to match the feature provides an estimate of the time when the feature is most likely to occur. Based on the TAC-based distance field, several visualization methods can be derived to highlight the position and motion of the feature. We present several case studies to demonstrate and compare the effectiveness of our framework.

Image segmentation by geodesic voting. Application to the extraction of tree structures from confocal microscope images

Abstract: This paper presents a new method to segment thin tree structures, such as extensions of microglia and cardiac or cerebral blood vessels. The Fast Marching method allows the segmentation of tree structures from a single point chosen by the user when a priori information is available about the length of the tree. In our case, no a priori information about the length of the tree structure to extract is available. We propose here to compute geodesics from a set of points scattered in the image. The targeted structure corresponds to image points with a high geodesic density. To compute the geodesic density we propose two methods. The first method defines the geodesic density of pixels in the image as the number of geodesics that cross this pixel. The second method consists in solving the transport equation with a velocity computed from the gradient of the distance map. In this method, the geodesic density is computed by integrating in short time the solution of the transport equation. To our knowledge this is the first time that geodesic voting is introduced. Numerical results from confocal microscope images are presented and show the interest of our approach.

Synthesising Objects and Scenes Using the Reverse Distance Transformation in 2D and 3D

Abstract: The reverse distance transformation has proved useful in image synthesis. This paper describes how digital objects are created from a number of seed labels in an image. The shape of the obtained objects depends on the metric used. In 2D the Euclidean and the 3-4 metrics are mentioned, and in 3D the D6, the D26, and the 3-4-5 metrics are discussed. The proposed method has no need of expensive CAD systems. It is an excellent image synthesising tool when developing image processing algorithms, i. e. shape quantification, visualisation, scene analysis and range imaging, as the obtained objects are well-defined in the image. The method is most advantageous in 3D, as there is an increasing need for volume images, but synthesising objects in 2D can also be useful.