Fast robust correlation

Fitch, A.J.; Kadyrov, A. A.; Christmas, William J.; Kittler, Josef

doi:10.1109/tip.2005.849767

Cited by 100 publications

(66 citation statements)

References 23 publications

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“…The cosinebased distance function was initially introduced by Fitch et al to replace the l 2 -norm function in the computation of displacement between video frames [27]. In this work, we propose the novel introduction of the cosine-based dissimilarity measure as a distance function for the training of the HOG-LBP descriptors by the SVM classifier.…”

Section: ) Svm Classifiermentioning

confidence: 99%

“…This holds because the cosine-based distance function for vector values in the range [0,1] has an influence function (i.e. derivative) that is equivalent to Andrew's M-Estimate [27], [35]:…”

Section: ) Svm Classifiermentioning

confidence: 99%

“…One of the main contributions of our work is the use of a novel special denoising measure that computes the distance between the HOG-LBP features in the SVM classifier. A cosine-based distance function was initially introduced by Fitch et al in an attempt to robustly and accurately compute the translational displacements between video frames [27]. This distance function was found to allow for a suppression of the effects of noise and outliers and be more robust than its l 2 -norm counterpart.…”

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confidence: 99%

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Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

Konstantinidis

Stathaki

Argyriou

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Building detection from 2D high-resolution satellite images is a computer vision, photogrammetry and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilised in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) from image regions. Using a novel approach, a Support Vector Machine (SVM) classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG-LBP descriptors before their classification to the building or non-building class. The second module consists of a set of region refinement processes that employs the output of the HOG-LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.

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Section: ) Svm Classifiermentioning

confidence: 99%

Section: ) Svm Classifiermentioning

confidence: 99%

mentioning

confidence: 99%

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Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

Konstantinidis

Stathaki

Argyriou

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Fitch et al [15] introduced the fast robust correlation and expressed a robust matching surface as a series of correlations to improve the performance of matching. Liwicki et al [9] adopted this cosine-based dissimilarity measure as follows:…”

Section: Fast Robust Correlation and Image Representation In Complex mentioning

confidence: 99%

A New Approach of Matrix Factorization on Complex Domain for Data Representation

Duong

Bui

Ding

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis work presents a new approach which derives a learned data representation method through matrix factorization on the complex domain. In particular, we introduce an encoding matrix-a new representation of data-that satisfies the simplicial constraint of the projective basis matrix on the field of complex numbers. A complex optimization framework is provided. It employs the gradient descent method and computes the derivative of the cost function based on Wirtinger's calculus.

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“…Outliers and noisy data are common problems when matching feature vectors in applications such as image registration [29], image matching [6], shape matching [8], face recognition [20], object tracking [18], and feature learning [23]. Standard distances (e.g.…”

Section: Introductionmentioning

confidence: 99%

Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations

Liwicki

Pham

Zafeiriou

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale and translation which outperforms the previous state-of-the-art approach on a public dataset. For the latter, we incorporate online subspace learning with the proposed FAQ representation to highlight the benefits of the new representation.

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Fast robust correlation

Cited by 100 publications

References 23 publications

Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

A New Approach of Matrix Factorization on Complex Domain for Data Representation

Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations

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