Urban environments of modern cities are described digitally in large public databases and datasets of e.g. laser scanning and ortho photos. These data sets are not necessarily linked to each other, except trough their geometry attributes (coordinates), which are mutually displaced and have a low degree of details. However, it is possible to create virtual 3D models of buildings, by processing t...

Recent advances in microscopy enable the capture of temporal sequences during cell development stages. However, the study of such sequences is a complex and time consuming task. In this paper we propose an automatic strategy to address the problem of semantic and instance segmentation of mouse embryos using NYU’s Mouse Embryo Tracking Database. We obtain our instance proposals as refined predic...

This paper discusses the Randomized Hough Transform used to find ellipses in images. The equations to find ellipses and their implementation in the algorithm are explained. The algorithm performed well, finding ellipses of orientation 0o and 9Oo from the x-axis in various images. Ellipses were also found in a real-world image after preprocessing.

In this paper, we present a new method for twodimensional (2D) shape detection applicable under affine transformation. The problem of affine-invariant shape detection is an important and fundamental research subject in computer vision. Although various methods have been proposed to solve this problem, most of those approaches are not well suited for the following general cases: (1) a shape to b...

Background The standard Hough Transform (SHT) is used to determine the parameters of features such as lines and curves within an image. A binary image is used as input where each active pixel represents part of an edge feature. The SHT maps each of these pixels to many points in Hough (or parameter) space. In the case of line detection, a single edge pixel is mapped to a sinusoid in 2D paramete...

Proposed in 1962, the Hough transform (HT) has been widely applied and investigated for detecting curves, shapes, and motions in the fields of image processing and computer vision. However, the HT has several shortcomings, including high computational cost, low detection accuracy, vulnerability to noise, and possibility of missing objects. Many efforts target at solving some of the problems for...