Neighbourhood Based Image Analysis

这项工作的目的是基于这样一种观察，即比较物体的相似性是人类条件的一种特征，可以无缝地进行，不需要努力。在很小的时候，我们就能熟练地识别环境中物体的相似性，并能迅速评估相似程度。事实上，我们的日常对话中充满了副词和形容词，用于概括我们环境中“事物”的接近程度。像“他和你差不多高”或“那些衣服看起来很相似”这样的短语可以证明我们对不同但有一些共同特征的物体进行类比的频率。在这项工作中考虑的问题是建立一个理论框架，在此基础上构建应用程序，以产生类似于人类执行相同任务的结果。所提出的方法是基于一个直观的观点，评估接近和apartness接地理论的基础上，近集，粗糙集，开集，和一般拓扑结构。具体而言，这项工作研究的基础上提取的特征值形成的点的邻域。在一般情况下，这项工作的贡献是不同的方法来寻找和评估最相似的对邻里。特别是，这种方法可以应用于工程和数学科学中的许多问题，例如使用数字图像内的感兴趣区域（ROI）来测量图像对应性以促进图像检索。在这里，图像ROI可以被视为称为邻域的点集的集合，这些点集通常是开集。通过研究这些邻域内相似对象的不同属性和结构，以及开发和改进相似性检测算法，可以开发基于邻近度的应用程序。这将导致加拿大人的生活水平和生活质量的提高，例如，在安全监视，国防和遥感的新应用，基于这项工作的图像分析，图像通信和基于内容的图像检索系统领域的贡献。

The aim of this work is based on the observation that comparing the similarity of objects is a faculty characteristic of the human condition, performed seamlessly and without effort. At a young age, we become adept at identifying the similarity of objects in our environment, and can quickly assess the degree of similarity. In fact, our day-to-day conversations are full of adverbs and adjectives used to encapsulate the nearness of "things" in our environment. Phrases like "he was about as tall as you are" or "those suits look similar" serve to demonstrate the frequency with which we are making analogies to objects that are not the same, but share some common characteristics. The problem considered in this work is one of establishing a theoretical framework on which to build applications to produce results similar to a human performing the same task. The proposed approach is based on an intuitive view of assessing nearness and apartness grounded in theory based on near sets, rough sets, open sets, and general topology. Specifically, this work investigates neighbourhoods of points formed based on extracted feature values. In general, the contribution of this work is different methods of finding and assessing the most similar pairs of neighbourhoods. In particular, this approach can be applied to many problems in engineering and mathematical sciences, such as using regions-of-interest (ROI) within digital images to measure image correspondence to facilitate image retrieval. Here, image ROIs can be viewed as collections of point sets called neighbourhoods, that are usually open sets. By investigating different properties and structures of similar objects within these neighbourhoods, as well as developing and improving similarity-detection algorithms, it is possible to develop nearness-based applications. This will lead to improvements in the standard of living and quality of life for Canadians, through, e.g., new applications in security surveillance, defense, and remote sensing, based on the contributions of this work to the areas of image analysis, image correspondence, and content-based image retrieval systems.