In this paper we propose the use of the Randomized Hough Transform algorithm for the determination of the proportions of pure classes present in sets of mixed pixels, for large datasets (for which the deterministic Hough is prohibitively slow) and in the presence of outliers (ie in cases that the classical Least Square Error method cannot cope). We demonstrate our results both with simulated an...

In this work we propose a new method to detect arbitrary planar shapes from a previous template and calculate the parameters that define the transformations between the new image and the template. The image contains a perspective projection of the template subjected to two angles transformation, called tilt and pan, a displacement a rotation and a scaling. The method uncouples parameter calcula...

This paper addresses the localization of anatomical structures in medical images by a Generalized Hough Transform (GHT). As localization is often a pre-requisite for subsequent model-based segmentation, it is important to assess whether or not the GHT was able to locate the desired object. The GHT by its construction does not make this distinction. We present an approach to detect incorrect GHT...

The recognition of shapes in images using Pairwise Geometric Histograms has previously been confined to fixed scale shape. Although the geometric representation used in this algorithm is not scale invariant, the stable behaviour of the similarity metric as shapes are scaled enables the method to be extended to the recognition of shapes over a range of scale. In this paper the necessary addition...

An approach for automatic object localization in medical images utilizing an extended version of the generalized Hough transform (GHT) is presented. In our approach the shape model in the GHT is equipped with specific model point weights, which are used in the voting process. The weights are adjusted in a discriminative training procedure, which aims at a minimal localization error of the GHT. ...

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...