Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Adaptive frame rate up-conversion based on motion classification
Signal Processing
IEEE Transactions on Image Processing
Spatiotemporal Saliency in Dynamic Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Motion vector refinement for high-performance transcoding
IEEE Transactions on Multimedia
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
A multilevel successive elimination algorithm for block matching motion estimation
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
A Multistage Motion Vector Processing Method for Motion-Compensated Frame Interpolation
IEEE Transactions on Image Processing
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Motion-compensated frame interpolation (MCFI) is a technique used extensively for increasing the temporal frequency of a video sequence. In order to obtain a high quality interpolation, the motion field between frames must be well-estimated. However, many current techniques for determining the motion are prone to errors in occlusion regions, as well as regions with repetitive structure. We propose an algorithm for improving both the objective and subjective quality of MCFI by refining the motion vector field. We first utilize a discriminant saliency classifier to determine which regions of the motion field are most important to a human observer. These regions are refined using a multistage motion vector refinement (MVR), which promotes motion vector candidates based upon their likelihood given a local neighborhood. For regions which fall below the saliency-threshold, a frame segmentation is used to locate regions of homogeneous color and texture via normalized cuts. Motion vectors are promoted such that each homogeneous region has a consistent motion. Experimental results demonstrate an improvement over previous frame rate up-conversion (FRUC) methods in both objective and subjective picture quality.