Distance transform

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

An Approach to the Parameterization of Structure for Fast Categorization

Abstract: A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the contour geometry, a contour is transformed into a local/global space, from which parameters are derived classifying its global geometry (arc, inflexion or alternating) and describing its local aspects (degree of curvature, edginess, symmetry). Regions are parameterized based on their symmetric axes, which are evolved with a wave-propagation process enabling to generate the distance map for fragmented contour images. The methodology is evaluated on three image sets, the Caltech 101 set and two sets drawn from the Corel collection. The performance nearly reaches the one of other categorization systems for unsupervised learning.

Intermodality 3D medical image registration with global search

Abstract: Describes a new registration method for three-dimensional medical images. It is important clinically to bring images from different modalities into alignment so that equivalent points can be identified. Often, functional images (showing metabolism or blood flow) are registered to structural images in order to more accurately interpret and quantify these images. This is especially important in areas of decreased function. This registration method determines the optimal transformation between the images using a match metric based on the distance transform of a structure visible in both modalities. The globally optimal transform is determined using a genetic optimization method and a hybrid technique using both genetic and gradient optimization. This provides a feasible way of determining the global solution making this method robust to local minima and insensitive to initial positioning. >

Distance transform algorithm for measuring nanofiber diameter

Abstract: This paper describes a new distance transform method used for measuring fiber diameter in electrospun nanofiber webs. In this algorithm, the effect of intersection is eliminated, which brings more accuracy to the measurement. The method is tested by a series of simulated images with known characteristics as well as some real webs obtained from electrospinning of PVA. Our method is compared with the distance transform method. The results obtained by our method were significantly better than the distance transform, indicating that the new method could successfully be used to measure electrospun fiber diameter.

Plant leaf classification using centroid distance and axis of least inertia method

Abstract: In this paper, we use centroid distance and axis of least inertia method for plant leaf classification. For this propose the RGB (Red, Green, Blue) image are converted to the binary image. Then, Canny operator is applied to the binary image to recognize the edges of the image before thinning the edges. After that, the boundary of the image is traced to sample the shape. Sampling helps us to avoid time-consuming computations. We compute the centroid distance of these points and distance of sampling points from axis of least inertia line. By selecting a fixed start point and normalizing the distances, the proposed method is shown to be invariant to image transformations (translation, rotation, reflection and scaling) and robust to minor deformations and occlusions. In this study, probabilistic neural network (PNN) has been used as a classifier. Two public leaf datasets including: Swedish leaf dataset and Flavia dataset are evaluated. Experimental results demonstrate the superior performance of the proposed feature in plant leaf classification.