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53
Image registration methods: a survey.
, 2003
"... Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrical ..."
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Cited by 760 (10 self)
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Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and featurebased) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. q
The angular difference function and its application to image registration
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2005
"... Abstract—The estimation of large motions without prior knowledge is an important problem in image registration. In this paper, we present the angular difference function (ADF) and demonstrate its applicability to rotation estimation. The ADF of two functions is defined as the integral of their spect ..."
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Cited by 13 (5 self)
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Abstract—The estimation of large motions without prior knowledge is an important problem in image registration. In this paper, we present the angular difference function (ADF) and demonstrate its applicability to rotation estimation. The ADF of two functions is defined as the integral of their spectral difference along the radial direction. It is efficiently computed using the pseudopolar Fourier transform, which computes the discrete Fourier transform of an image on a near spherical grid. Unlike other Fourierbased registration schemes, the suggested approach does not require any interpolation. Thus, it is more accurate and significantly faster. Index Terms—Global motion estimation, Fourier domain, pseudopolar FFT, image alignment. æ 1
Transformation invariant component analysis for binary images
 In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, volume I
, 2006
"... There are various situations where image data is binary: character recognition, result of image segmentation etc. As a first contribution, we compare Gaussian based principal component analysis (PCA), which is often used to model images, and ”binary PCA ” which models the binary data more naturally ..."
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Cited by 12 (1 self)
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There are various situations where image data is binary: character recognition, result of image segmentation etc. As a first contribution, we compare Gaussian based principal component analysis (PCA), which is often used to model images, and ”binary PCA ” which models the binary data more naturally using Bernoulli distributions. Furthermore, we address the problem of data alignment. Image data is often perturbed by some global transformations such as shifting, rotation, scaling etc. In such cases the data needs to be transformed to some canonical aligned form. As a second contribution, we extend the binary PCA to the ”transformation invariant mixture of binary PCAs ” which simultaneously corrects the data for a set of global transformations and learns the binary PCA model on the aligned data. 1 1.
Improved Video Mosaic Construction by Selecting a Suitable Subset of Video Images
 In Proc. TwentySeventh Australasian Computer Science Conference (ACSC2004
, 2004
"... By stitching together adjacent images from a video sequence surveying a scene, a video mosaic of the entire panorama can be formed. Since a video survey consists of a sequence of images having small relative displacements with respect to each other, there is redundant overlapping information in cons ..."
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Cited by 7 (0 self)
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By stitching together adjacent images from a video sequence surveying a scene, a video mosaic of the entire panorama can be formed. Since a video survey consists of a sequence of images having small relative displacements with respect to each other, there is redundant overlapping information in consecutive images so that not all consecutive video images are required to create a mosaic, and only a subset of suitable images needs to be chosen. Images that are misaligned due to subpixel translation, rotation or shear are difficult to perfectly realign and stitching of such images can result in a mosaic where discontinuities are noticeable. We propose a new technique for the construction of a seamless mosaic to minimise discontinuities. Our technique partitions an image into four quadrants to register with those of successive images. These registration values are used to form a misalignment index for selecting the best images for stitching.
Fast transformationinvariant component analysis
 IJCV, Submitted
, 2003
"... Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high dimensional data samples and points in a lower dimensional subspace. Previously, transformationinvariant component analysis (TCA) was introduced to learn t ..."
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Cited by 6 (0 self)
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Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high dimensional data samples and points in a lower dimensional subspace. Previously, transformationinvariant component analysis (TCA) was introduced to learn this linear mapping in a way that is invariant to a set of global transformations. The expectation maximization algorithm used to learn the parameters of TCA requires a number of scalar operations on the order of N 2, where N is the number of elements in each training example. This is prohibitive for many applications of interest such as modelling mid to large size images, where N may be quite large (e.g. 262144 dimensions for a 512×512 grayscale image). In this paper, we present an efficient algorithm that reduces the computational requirements to the order of N log N. With this speedup, we show the effectiveness of TCA in various applications including tracking, video textures, clustering, object recognition and object detection in images. I.
handdrawn sketches
"... We describe a trainable, handdrawn symbol recognizer based on a multilayer recognition scheme. Symbols are internally represented as binary templates. An ensemble of four different classifiers compares and ranks definition symbols according to their similarity to the unknown symbol. The scores of ..."
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Cited by 3 (1 self)
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We describe a trainable, handdrawn symbol recognizer based on a multilayer recognition scheme. Symbols are internally represented as binary templates. An ensemble of four different classifiers compares and ranks definition symbols according to their similarity to the unknown symbol. The scores of the individual classifiers are aggregated to produce a combined score for each definition. The definition with the best combined score is assigned to the unknown symbol. All four classifiers use templatematching techniques to compute similarity (and dissimilarity) between symbols. Ordinarily, templatematching is sensitive to rotation, and existing solutions for rotation invariance are too expensive for interactive performance. We have developed a fast technique that uses a polar coordinate representation to achieve rotational invariance. This technique is applied prior to the multiclassifier recognition step to determine the best alignment of the unknown with each definition. One advantage of this technique is that it filters out the bulk of unlikely definitions, thereby reducing the number of definitions the multiclassifier recognition step must consider.
GRADIENT FIELD DISTRIBUTIONS FOR THE REGISTRATION OF IMAGES
"... This paper introduces a new method to register images that are rotated and translated with respect to each other. The method works by transforming each image to a gradient distribution space. This space represents the likelihood of finding a particular gradient in the image and is invariant to trans ..."
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Cited by 3 (0 self)
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This paper introduces a new method to register images that are rotated and translated with respect to each other. The method works by transforming each image to a gradient distribution space. This space represents the likelihood of finding a particular gradient in the image and is invariant to translation. Once transformed the rotation between the images is efficiently found using correlation. Unlike Fourier based methods, phase information is retained in the gradient distribution space, thus a larger class of images can be accurately registered. The method is computationally efficient and does not require nonlinear optimization or iterative methods. Furthermore, large rotations and translations can easily be handled.
PseudoPolar Based Estimation of Large Translations Rotations and Scalings in Images
, 2002
"... One of the major challenges related to image registration is the estimation of large motions without prior knowledge. This paper presents a Fourier based approach that estimates large translation, scale and rotation motions. The algorithm uses the pseudopolartransform to achieve substantial improve ..."
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Cited by 2 (1 self)
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One of the major challenges related to image registration is the estimation of large motions without prior knowledge. This paper presents a Fourier based approach that estimates large translation, scale and rotation motions. The algorithm uses the pseudopolartransform to achieve substantial improved approximations of the polarandlogpolar Fourier transforms of an image. Thus, rotation and scale changes are reduced to translations which are estimated using phasecorrelation. By utilizing the pseudopolar grid we increase the performance (accuracy, speed, robustness) of the registration algorithms. Scales up to 4 and arbitrary rotationangles can be robustly recovered, compared to a maximum scaling of 2 recovered by the currrnt state oftheartalgorithms. The algorithm utilizes only 1DFFT calculations whose overall complexity is signifficantly lower than prior works. Experimental results demonstrate the applicability of these algorithms.
Realtime human detection, tracking, and verification in uncontrolled camera motion environments
 In IEEE Int. Conf. on Computer Vision Systems, ICVS’06
, 2006
"... In environments where a camera is installed on a freely moving platform, e.g. a vehicle or a robot, object detection and tracking becomes much more difficult. In this paper, we presents a real time system for human detection, tracking, and verification in such challenging environments. To deliver a ..."
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In environments where a camera is installed on a freely moving platform, e.g. a vehicle or a robot, object detection and tracking becomes much more difficult. In this paper, we presents a real time system for human detection, tracking, and verification in such challenging environments. To deliver a robust performance, the system integrates several computer vision algorithms to perform its function: a human detection algorithm, an object tracking algorithm, and a motion analysis algorithm. To utilize the available computing resources to the maximum possible extent, each of the system components is designed to work in a separate thread that communicates with the other threads through shared data structures. The focus of this paper is more on the implementation issues than on the algorithmic issues of algorithmic details away from the system structure. 1
A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye using Proper Orthogonal Decomposition
"... Glaucoma is the second leading cause of blindness worldwide. Often the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In ..."
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Glaucoma is the second leading cause of blindness worldwide. Often the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this work, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of an eye present during a followup exam were estimated by comparing the ONH topograph acquired from the followup exam with its baseline topograph subspace representation. Image correspondence measures of L1 and L2 norms, correlation, and image Euclidean