A survey of image registration techniques
ACM Computing Surveys (CSUR)
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Matching Using Epipolar Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
LADAR Scene Description using Fuzzy Morphology and Rules
CVBVS '99 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications
The Use of Force Histograms for Affine-Invariant Relative Position Description
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object registration in scene matching based on spatial relationships
Object registration in scene matching based on spatial relationships
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Linguistic description of relative positions in images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
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This paper outlines further advances from initial findings previously reported in [O. Sjahputera, J.M. Keller, Possibilistic C-means in scene matching, Fourth Internat. Conf. of the European Society for Fuzzy Logic and Technology (EUSFLAT), 2005, pp. 669-675]. We propose a scene matching approach based on spatial relationships among objects in the images to determine if two images acquired under different viewing conditions capture the same scene. This is a difficult problem in computer vision. Our approach produces a mapping of objects from one view to the other, and recovers the viewing transformation parameters. The core of the system relies on capturing spatial relationship information through Force Histograms as affine-invariant image descriptors. Object mapping across images is performed by finding the best correspondence map (FMAP) between force histograms in the two images. The major problem is that the number of potential FMAPs is large, even for modest numbers of scene objects. Hence, search optimization is required. The correct FMAP contains histogram correspondences represented by similar feature vectors. Therefore, dense regions in the feature space are suspected to contain these vectors. Possibilistic C-means (PCM) clustering is used to find these dense regions. The centroids of these dense regions are used to generate the FMAPs. Previously, the FMAP was generated using a nearest-neighbor like approach. In this study, we propose an improved version of this method by incorporating fuzzy memberships into the FMAP building process. Here, the fitness of FMAP candidates are assessed with respect to all histogram correspondences already in FMAP, not just from an initial seed point alone. The best FMAP is selected and translated into a mapping scheme that connects the objects in the two images.