Cellular neural network–based hybrid approach toward automatic image registration
Cellular neural network–based hybrid approach toward automatic image registration
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DOI：
10.1117/1.jrs.7.073533
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发表时间：
2013-01
影响因子：
1.7
通讯作者：
P. V. Arun;S. Katiyar
中科院分区：
文献类型：
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作者：
P. V. Arun;S. Katiyar
摘要图像配准是涉及不同图像数据集分析的各种图像处理操作的关键组成部分。在过去的十年中，自动图像配准领域已经见证了许多智能方法的应用;然而，无法正确建模对象形状以及上下文信息限制了可达到的准确性。提出了一种利用向量机、细胞神经网络（CNN）、尺度不变特征变换（SIFT）、核心集和细胞自动机等先进技术进行精确特征形状建模和自适应重构的框架。CNN已被发现在改善特征匹配以及注册的重新搜索阶段方面是有效的，并且使用核心集优化大大降低了该方法的复杂性。这项工作的显着特点是基于细胞神经网络方法的SIFT特征点优化，自适应resception，智能对象建模。已开发的方法进行了比较，与当代方法使用不同的统计措施。对各种卫星图像的调查表明，这种方法取得了相当大的成功。该系统采用CNN-Prolog方法动态地使用光谱和空间信息来表示上下文知识。这种方法也被证明是有效的，在提供智能解释和自适应rescue。
Abstract Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach–based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.