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HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients
2014
IEEE Transactions on Image Processing
In this paper, we introduce a novel and powerful local image descriptor that extracts the histograms of second-order gradients (HSOGs) to capture the curvature related geometric properties of the neural ...
gradient location and orientation histogram, only employ the first-order gradient information related to the slope and the elasticity, i.e., length, area, and so on of a surface, and thereby partially ...
HSOG: A Novel Local Image Descriptor Based on Histograms of the Second-Order Gradients in computer vision, ranging from traditional vision tasks, e.g., panoramic stitching [2] , wide-baseline matching ...
doi:10.1109/tip.2014.2353814
pmid:25203990
fatcat:l4r5cmcgzfagxdoiabrik2swom
Local Intensity Ordering based Binary Patterns for Image Region Description
2017
Advances In Image and Video Processing
In this paper, two novel local image region descriptors called Local Intensity Order-based Center Symmetric Local Binary Patterns (LIOCSLBP) and Local Intensity Order-based Orthogonally Combined Local ...
Extensive experiments are conducted to evaluate the performance of the proposed descriptors on standard benchmark datasets for image matching, object recognition and scene recognition against the state-of-the-art ...
In contrast, HSOG [32] used the second-order gradients to compute the Histogram of Second Order Gradients. ...
doi:10.14738/aivp.53.3279
fatcat:rypreie43jdkzpzdfgu7ftjmmq
Local Feature Descriptor for Image Matching: A Survey
2019
IEEE Access
The major goal of the paper is to present a unique survey of the state-of-the-art image matching methods based on feature descriptor, from which future research may benefit. ...
The current and future challenges of local feature descriptors are discussed. ...
[61] suggested a novel and powerful local image descriptor for making use of the histograms of the second order gradients (HSOGs) to capture curvature related local geometric properties. ...
doi:10.1109/access.2018.2888856
fatcat:rshfx326izcfdgk5ed6umit7wi