【作者】 李刚；

【导师】 李继云；

【作者基本信息】 东华大学 ， 计算机应用， 2008， 硕士

【摘要】 图像分割是图像分析、识别和理解的基础。图像分割主要是指将图像分成各具特性的区域并提取出感兴趣的区域的技术,其研究多年来一直受到人们的高度重视。由于待分割图像的可变性比较大,且混有噪声,构成了图像分割所面临的主要困难。到目前虽然已经有了许多各种类型的分割算法,但是这些方法普遍存在问题和缺陷,影响了性能和应用,因此需要继续探索新的途径,对图像分割继续深入下去。人工生命是一个新兴起的多学科交叉的研究领域,已经在解决现实世界中的许多复杂问题上显示了潜在的应用前景。在图像分割的研究中引入人工生命的思想,将具有广阔的研究空间和良好的应用前景,将有希望发现新颖的更优良的分割方法。本文分别就单一的静态图像和图像视频序列提出了两个人工生命模型,基于细胞自动机的人工生命模型和基于多粒度的人工生命模型。在第一种模型中我们将待分割图像看作人工生命智能体的生存环境,通过生存在其上的人工生命智能体模型一代代繁衍、扩张来最终得到图像的分割结果。在根据视频图像序列中图像的特征提出的基于多粒度的人工生命改进模型里是将视频图像序列看作是生命体的生存环境,不同的视频帧视为环境的变化。生命体个体体积的大小也不再仅仅局限在像素级上,而是同时考虑了由小的生命个体聚集而成的更大规模的生命群落及群落之间的交互。生命体通过环境的变化获得能量才能够生存,它们能感觉到周围的变化并向着变化的方向不断扩张。每个智能体在规则的作用下自主选择自身的行为。该模型具有自底向上的,非全局受控等特点。通过生命个体和群体的繁衍,死亡,扩张,迁移等行为,使的前景图像被最终被分割出来。实验表明,该方法不仅具有很好的性能而且具有较好的应用潜力。更多还原

【Abstract】 Image segmentation is the base of image analysis, image recognition and the image understanding. Image segmentation is a technique which divides an image into some special areas and gets interesting areas. Many researchers have been working on it for a long time. Main difficulties or obstacles to image segmentation are the changing of image and the noise. So far there have been many image segmentation algorithms, however, each of them has its own problems that impact the algorithm’s performance and application. So we need to get more new methods and makes an intensive study of it.Artificial Life is a new research area which is transdisciplinary. It shows its potential superiority on solving complex problems. If we apply the artificial life to the image segmentation, there would be more extensive research space and good application prospects. And maybe we will find a lot of novel and much better methods. This paper puts forward two artificial life modes based on frozen picture and video frequency sequence respectively. The first is an artificial life model based on the Cellular Automata, the other is a ALife mode which has a variety of size. In the first ALife model we take the image as the environment of the agents. Through some living action like propagation, death, expansion, moving and so on we finally get the result of image segmentation.In the multi-granular ALife model which is extracted according to video feature of video sequence, we also take the image as the environment of agents. It’s not a single image at this time but a serial of images of a video. The difference of the images is the environment changing. Agents can live by getting the changing energy. They can detect the changing nearby and extensive to that direction. And in this model some small agents can aggregate a cluster as a big agent. Both of modes have their rules which every agent must observe. Under the rules each agent can choose its next action. The two models have bottom-up and non-overall control features. Through the results of experiments, we conclude that both models have a good performance and application prospect.更多还原