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Feature-Based Image Comparison and Its Application in Wireless Visual Sensor Networks

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Feature-Based Image Comparison and Its Application in Wireless Visual Sensor Networks University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2011 Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks Yang Bai University of Tennessee Knoxville, Department of EECS, [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Other Computer Engineering Commons, and the Robotics Commons Recommended Citation Bai, Yang, "Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks. " PhD diss., University of Tennessee, 2011. https://trace.tennessee.edu/utk_graddiss/946 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Yang Bai entitled "Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Computer Engineering. Hairong Qi, Major Professor We have read this dissertation and recommend its acceptance: Mongi A. Abidi, Qing Cao, Steven Wise Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Yang Bai May 2011 Copyright c 2011 by Yang Bai All rights reserved. ii Dedication I dedicate this dissertation to my parents, who gave me the opportunity to explore the world, and to my wife, who has been always supportive no matter what happens. iii Acknowledgments I would like to thank all the individuals who have inspired, encouraged, and advised me in the preparation of this dissertation. First and foremost, I would like to thank my advisor, Dr. Hairong Qi. Her willingness to support my work and her guidance throughout my studies has allowed me to develop my skills as a researcher within a supportive team environment. I thank her for that opportunity. I would further like to thank the other members of my committee: Dr. Mongi A. Abidi, Dr. Qing Cao, and Dr. Steven Wise. I greatly appreciate their time and input to this dissertation. Within the AICIP Lab, I owe many thanks to my fellow graduate students. I enjoyed the many conversations and discussions that have had a tremendous impact on my research and myself as a person. Dayu Yang, Sangwoo Moon, Mahmut Karakaya, Li He, Jiajia Luo, Shuangjiang Li, Bryan Bodkin, Wei Wang, Zhibo Wang, Austin Albright and Paul Donnelly thank you very much. Last but not the least, I express my deepest appreciation to my parents and my wife, for their unconditional love, support and encouragement. iv Abstract This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks. Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring prac- tices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor col- laboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. Besides, theenergyresourceontheWVSNsisalsoverytight,withwirelesscommunicationand complicated computation consuming most energy in WVSNs. Therefore the feature-based image comparison method has been proposed, which directly compares the captured image from each visual sensor in an economical way in terms of both the computational cost and the transmission overhead. The feature-based image comparison method compares different images and aims to find similar image pairs using a set of local features from each image. The image feature is a numerical representation of the raw image and can be more compact in terms of the data volume than the raw image. The feature-based image comparison contains three steps: feature detection, descriptor calculation and feature comparison. For the step of feature detection, the dissertation proposes two computationally effi- cient corner detectors. The first detector is based on the Discrete Wavelet Transform that provides multi-scale corner point detection and the scale selection is achieved efficiently v through a Gaussian convolution approach. The second detector is based on a linear un- mixing model, which treats a corner point as the intersection of two or three “line” bases in a 3 × 3 region. The line bases are extracted through a constrained Nonnegative Matrix Factorization (NMF) approach and the corner detection is accomplished through counting the number of contributing bases in the linear mixture. For the step of descriptor calculation, the dissertation proposes an effective dimen- sionality reduction algorithm for the high dimensional Scale Invariant Feature Transform (SIFT) descriptors. A set of 40 SIFT descriptor bases are extracted through constrained NMF from a large training set and all SIFT descriptors are then projected onto the space spanned by these bases, achieving dimensionality reduction. The efficiency of the proposed corner detectors have been proven through theoretical analysis. In addition, the effectiveness of the proposed corner detectors and the dimen- sionality reduction approach has been validated through extensive comparison with several state-of-the-art feature detector/descriptor combinations. vi Contents 1 Introduction 1 1.1ImageComparisonfromVisualFeatures.................... 1 1.2WirelessVisualSensorNetworks........................ 3 1.3Motivations.................................... 5 1.4Contributions................................... 6 1.5DissertationOutline............................... 7 2 Literature Survey 8 2.1FeatureDetection................................. 9 2.1.1 Moravec’sCornerDetector....................... 10 2.1.2 HarrisCornerDetector......................... 12 2.1.3 VariationsoftheHarrisCornerDetector................ 14 2.1.4 HarrisDetectorforColorImages.................... 16 2.1.5 FASTCornerDetector.......................... 17 2.1.6 The Feature Detector in Scale Invariant Feature Transform (SIFT) . 18 2.1.7 The Feature Detector in Speed-Up Robust Features (SURF) . 21 2.2FeatureDescription................................ 23 2.2.1 Feature Descriptor in Scale Invariant Feature Transform (SIFT) . 23 2.2.2 FeatureDescriptorinSpeed-UpRobustFeature(SURF)....... 24 2.2.3 MomentInvariants............................ 24 2.2.4 ComparisonofFeatureDetectorsandDescriptors........... 27 2.3FeatureComparison............................... 29 vii 3 Light-weight Feature Detectors 31 3.1DWT-basedCornerDetector.......................... 31 3.1.1 Localize the Corner Points using Gaussian Interpolation from Deci- matedDWTDecomposition....................... 32 3.1.2 Estimate Corner Point Scales from Gaussian Convolution on the Cor- nerMap.................................. 33 3.2 An Efficient Corner Detector Based on Linear Unmixing . .......... 36 3.2.1 CornerDetectionusingLinearMixingModel............. 36 3.2.2 ConstrainedNMFforBasisCalculation................ 38 3.2.3 TrainingSetandtheExtractedBases................. 41 3.2.4 RobustDetectionofCorners...................... 44 3.2.5 ComputationalEfficiency........................ 46 3.3Discussion..................................... 49 4 Dimensionality Reduction for SIFT Descriptors 51 4.1ConstrainedNMFforCalculatingDescriptorBases.............. 52 4.2OfflineTrainingandDimensionSelection................... 54 5 Experimental Results 57 5.1PerformanceComparisonforFeatureDetectors................ 57 5.1.1 Repeatability............................... 58 5.1.2 DatasetandtheGroundTruth..................... 59 5.1.3 ExperimentalResultsfortheRepeatability.............. 62 5.2PerformanceComparisonforFeature-basedImageComparison....... 64 5.2.1 EvaluationMetrics............................ 65 5.2.2 DatasetsandtheGroundTruth..................... 65 5.2.3 Feature-basedImageComparisonontheOxfordDataset....... 67 5.3Conclusion.................................... 70 6 Feature-based Image Comparison on WVSN Datasets 72 6.1Feature-basedImageComparisonontheMSPImageDataset........ 72 6.2Feature-basedImageComparisonontheCOIL-100Dataset......... 76 viii 6.3Discussion..................................... 81 7 Conclusions and Future Works 83 7.1SummaryofContributions............................ 83 7.2DirectionsforFutureResearch......................... 84 7.2.1 RedundancyRemovalforWVSNs................... 85 7.2.2 Super-resolutionImageReconstructioninWVSNs.......... 87 7.3ClosingRemarks................................. 90 Publications 91 Bibliography 93 Vita 100 ix List of Tables 2.1Featuredetectors................................. 28 2.2Featuredescriptors...............................
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