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

本计画的目的是提出一个可以侦测二值影像中椭圆与椭圆曲线的演算法。 为了在实际应用中发挥作用，该算法必须具有足够的鲁棒性，以检测畸形椭圆，并能够扫描具有背景噪声的图像。该算法划分不同的对象群体，每个群体围绕潜在的椭圆曲线形成一个集群。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近似实际的椭圆曲线，大概在每个种群的中心，直到检测到该曲线并终止种群。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，我们在2006年的GECCO上获得了一个奖项，因为我们提供了证据证明我们已经实现了这一目标。这项技术有许多潜在的应用，包括显微图像中细胞的检测和计数，手写模式的检测等。

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.