Large displacement optical flow based image predictor model

This paper proposes a Large Displacement Optical Flow based Image Predictor Model for generating future image frames by applying past and present image frames. The predictor model is an Artificial Neural Network (ANN) and Radial Basis Function Neural Network (RBFNN) Model whose input set of data is horizontal and vertical components of velocities estimated using Large Displacement Optical Flow for every pixel intensity in a given image sequence. There has been a significant amount of research in the past to generate future image frames for a given set of image frames. The quality of generated images is evaluated by Canny's edge detection Index Metric (CIM) and Mean Structure Similarity Index Metric (MSSIM). For our proposed algorithm, CIM and MSSIM indices for all the future generated images are found better when compared with the most recent existing algorithms for future image frame generation. The objective of this study is to develop a generalized framework that can predict future image frames for any given image sequence with large displacements of objects. In this paper, we have validated our developed Image Predictor Model on an image sequence of landing jet fighter and obtained performance indices are found better as compared to most recent existing image predictor models.

[1]  Nikhil R. Pal,et al.  Prediction of satellite images using fuzzy rule based Gaussian regression , 2010, 2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR).

[2]  Li-li Ma,et al.  RBF Network-Based Chaotic Time Series Prediction and It's Application in Foreign Exchange Market , 2007 .

[3]  Nishchal K. Verma,et al.  Future image frame generation using Artificial Neural Network with selected features , 2012, 2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[4]  Nishchal K. Verma,et al.  Image sequence Prediction using ANN and RBFNN , 2013, Int. J. Image Graph..

[5]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Xinghuo Yu,et al.  A general backpropagation algorithm for feedforward neural networks learning , 2002, IEEE Trans. Neural Networks.

[10]  W.C. Chan,et al.  A wavelet and Canny based image comparison , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[11]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Shikha Singh,et al.  Generation of Future Image Frames for an Image Sequence , 2013 .

[13]  Nishchal K. Verma,et al.  Generation of Future image frames using Adaptive Network Based Fuzzy Inference System on spatiotemporal framework , 2012, 2012 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[14]  Nishchal K. Verma,et al.  Generation of future image frames using optical flow , 2013, 2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).