Ellipzoid 1.0: a multiple population genetic algorithm for detection of multiple elliptic objects in noisy microscopic image

这个项目的目的是产生一种能够检测二值图像中的椭圆和椭圆曲线的算法。为了对现实世界的应用有用，该算法必须足够健壮，能够检测出畸形的椭圆，并能够扫描有背景噪声的图像。该算法划分了不同的对象群，每个对象在潜在的椭圆曲线周围形成一个簇。每个对象代表一个候选椭圆。该算法使用进化和聚类来逐渐更好地逼近实际的椭圆曲线，假设在每个种群的中心，直到该曲线被检测到并终止种群。这一过程在图像中的所有群体中同时发生，直到所有椭圆曲线被定位。我们有全面的数据表明，该算法比用于椭圆检测的标准技术随机霍夫变换(RHT)更快、更准确(特别是在图像具有多条椭圆曲线和/或噪声的情况下)。事实上，我们因为提供证据证明我们已经实现了这一目标而在GECCO 2006上获奖。这项技术有许多潜在的应用，包括对显微图像中细胞的检测和计数，手写图案的检测等。

The purpose of this project is to produce an algorithm that can detect ellipses and elliptic curves in binary images.  To be useful for real-world applications, the algorithm must be robust enough to detect malformed ellipses and be able to scan images that have background noise.The algorithm partitions different populations of objects, each of which forms a cluster around a potential elliptic curve. Each object represents a candidate ellipse. The algorithm uses both evolution and clustering to gradually better approximate the actual elliptic curve, presumably at the centre of each population, until that curve is detected and the population is terminated. This process occurs simultaneously within all populations in the image until all elliptic curves are located.We have comprehensive data that show that this algorithm operates faster and more accurately (especially in cases in which images have multiple elliptic curves and/or noise) than the standard technique used for ellipse detection, the Randomized Hough Transform (or RHT). In fact, we were awarded a prize at GECCO 2006 for providing evidence that we have achieved exactly that objective.There are many potential applications for this technology, including the detection and counting of cells in microscopic images, the detection of hand-writing patterns, etc.