MCPNS: A Macropixel Collocated Position and Its Neighbors Search for Plenoptic 2.0 Video Coding

Recently, it was demonstrated that a newly focused plenoptic 2.0 camera can capture much higher spatial resolution owing to its effective light field sampling, as compared to a traditional unfocused plenoptic 1.0 camera. However, due to the nature difference of the optical structure between the plenoptic 1.0 and 2.0 cameras, the existing fast motion estimation (ME) method for plenoptic 1.0 videos is expected to be sub-optimal for encoding plenoptic 2.0 videos. In this paper, we point out the main motion characteristic differences between plenoptic 1.0 and 2.0 videos and then propose a new fast ME, called macropixel collocated position and its neighbors search (MCPNS) for plenoptic 2.0 videos. In detail, we propose to reduce the number of macropixel collocated position (MCP) search candidates based on the new observation of center-biased motion vector distribution at macropixel resolution. After that, due to large motion deviation behavior around each MCP location in plenoptic 2.0 videos, we propose to select a certain number of key MCP locations with the lowest matching cost to perform the neighbors MCP search to improve the motion search accuracy. Different from existing methods, our method can achieve better performance without requiring prior knowledge of microlens array orientations. Our simulation results confirmed the effectiveness of the proposed algorithm in terms of both bitrate savings and computational costs compared to existing methods.Comment: Under revie

CLASSIFICATION OF MULTISPECTRAL IMAGE DATA WITH SPATIAL-TEMPORAL CONTEXT

Pattern recognition technology has had a very important role in many fields of application including image processing, computer vision, remote sensing, etc. The advent of more powerful sensor systems should enable one to extract far more detailed information than ever before from observed data, but to realize this goal requires the development of concomitant data analysis techniques which can utilize the full potential of the observed data. This report investigates classification using spatial and/or temporal contextual information. Although contextual information has been an important and powerful data analysis clue for the human-analyst, the lack of a good contextual classification scheme especially which can both use spatial and temporal context has not allowed its usefulness to be put to full use. Two different approaches to spatial-temporal contextual classification are investigated. One is based on statistical spatial-temporal contextual classification, and the other is based on decision fusion of temporal data sets which are classified individually with spatial contexts. In the first approach, a general form of maximum a posterior spatialtemporal contextual classifier is derived after spatial and temporal neighbors are defined. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. In the second approach based on ,the decision fusion, each temporal data set is separately fed into the local classifier and a final classification is performed by summarizing the local class decisions with an optimum decision fusion rule which is derived based on the minimum expected cost. The new decision fusion rule is designed to handle not only data set reliabilities but also classwise reliabilities of each data set. Experimental results with three temporal Landsat Thematic Mapper data show significant improvement of classification accuracy over non-contextual pixelwise classifier. These spatial-temporal contextual classifiers will find their use in many real applications of remote sensing, especially when the classification accuracy is important

DESIGN OF PARTIALLY SUPERVISED CLASSIFIERS FOR MULTISPECTRAL IMAGE DATA

This report addresses a partially supervised classification problem, especially when the class definition and corresponding training samples are provided a pnori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class characteristics of all classes in a given data set. Considering the effort in both time and man-power required to have a well-defined, exhaustive list of classes with a corresponding representative set of training samples, this partially supervised capability would be very desirable, assuming adequate classifier performance can be obtained. Two different classification algorithms are developed to achieve simplicity in classifier design by reducing the requirement of prior statistical information without sacrificing significant classifying capability. The first olne is based on optimal significance testing, where the optimal acceptance probability is estimated directly from the data set. In the second approach, the partially supervised classification is considered as a problem of unsupervised clustering with initially one known cluster or class. A weighted unsupervised clustering procedure is developed to automatically define other classes and estimate their class statistics. The operational simplicity thus realized should makes these partially supervised classification schemes very viable tools in pattern classification

Classification with spatial-temporal context and design of partially supervised classifiers

Pattern recognition technology has had a very important role in many fields of application including image processing, computer vision, remote sensing, etc. The advent of more powerful sensor systems should enable one to extract far more detailed information than ever before from observed data, but to realize this goal requires the development of concomitant data analysis techniques which can utilize the full potential of the observed data. In this dissertation, two different issues are investigated. One involves classification using spatial and/or temporal contextual information. Although contextual information has been an important and powerful data analysis clue for the human-analyst, the lack of a good contextual classification scheme especially which can both use spatial and temporal context has not allowed its usefulness to be put to full use. Two different approaches to spatio-temporal contextual classification are investigated. One is based on statistical spatio-temporal contextual classification, and the other is based on decision fusion of temporal data sets which are classified individually with spatial contexts. The second part of this dissertation addresses a partially supervised classification problem, especially when the class definition and corresponding training samples are provided a priori only for just one particular class. In practical applications of pattern classification techniques, a frequently observed characteristic is the heavy, often nearly impossible requirements on representative prior statistical class characteristics of all classes in a given data set. Considering the effort in both time and man-power required to have a well-defined, exhaustive list of classes with a corresponding representative set of training samples, this partially supervised capability would be very desirable, assuming adequate classifier performance can be obtained. Two different classification algorithms are developed to achieve simplicity in classifier design by reducing the requirement of prior statistical information without sacrificing significant classifying capability. The first one is based on optimal significance testing, where the optimal acceptance probability is estimated directly from the data set. In the second approach, this partially supervised classification is considered as a problem of unsupervised clustering with initially one known cluster or class. A weighted unsupervised clustering procedure is developed to automatically define other classes and estimate their class statistics. The operational simplicity thus realized should makes these partially supervised classification schemes very viable tools in pattern classification