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Generation of Future image frames using Adaptive Network Based Fuzzy Inference System on spatiotemporal framework

机译:基于时空框架的自适应网络模糊推理系统生成未来图像帧

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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.
机译:本文提出了一种基于时空框架的基于自适应网络的模糊推理系统(ANFIS)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。针对图像帧中的每个像素,分别针对R,G和B值对ANFIS进行训练。主成分分析,交互信息和Bhattacharyya距离度量已用于减少特征集的维数。所得方案已成功地应用于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)和平均结构相似性指标度量(MSSIM)已用于评估未来的图像帧质量。发现所提出的方法已经成功地生成了九个未来图像帧。

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