基于自适应网络的时空框架模糊推理系统生成未来图像帧

N. Verma, Shimaila
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引用次数: 8

摘要

本文提出了一种基于自适应网络的模糊推理系统(ANFIS)在时空框架上生成未来图像帧的算法。网络的输入是图像序列中像素的超维颜色和时空特征。ANFIS分别对图像帧中的每个像素进行R、G和B值的训练。采用主成分分析、交互信息和Bhattacharyya距离测度对特征集进行降维。该方案已成功地应用于一个热带气旋的卫星图像序列。两种图像质量评估技术,基于Canny边缘检测的图像比较度量(CIM)和平均结构相似指数度量(MSSIM)已被用于评估未来图像帧的质量。该方法成功地生成了9个未来的图像帧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of Future image frames using Adaptive Network Based Fuzzy Inference System on spatiotemporal framework
This paper presents an algorithm for Future image frames generation using Adaptive Network Based Fuzzy Inference System (ANFIS) on spatiotemporal framework. The input to the network is a hyper-dimensional color and spatiotemporal feature of a pixel in an image sequence. The ANFIS is trained for R, G and B values separately for each and every pixel in image frame. Principal Component Analysis, Interaction Information and Bhattacharyya Distance measure have been used to reduce the dimensionality of the feature set. The resulting scheme has successfully been applied on satellite image sequence of a tropical cyclone. Two image quality assessment techniques, Canny edge detection based Image Comparison Metric (CIM) and Mean Structural Similarity Index Measure (MSSIM) have been used to evaluate future image frames quality. The proposed approach is found to have generated nine future image frames successfully.
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