Shape matching by integral invariants on eccentricity transformed images

Janan, Faraz; Brady, Michael

doi:10.1109/embc.2013.6610695

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Contour flexibility [23] makes effort to represent the deformable potential at each point along the contour and shows that both local and global features can be extracted by this descriptor. There are also other similar methods in [24], [25], [35], [43].…”

Section: Related Workmentioning

confidence: 99%

Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Wang

Gao

Sun

et al. 2019

IEEE Trans. on Image Process.

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Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behaviour that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.

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Section: Related Workmentioning

confidence: 99%

Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Wang

Gao

Sun

et al. 2019

IEEE Trans. on Image Process.

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show abstract

“…[18] Other studies [60,61] confirmed its efficiency in matching visual parts and shape contexts. However, the method suffers from the "city block" problem and sub-pixel accuracy, which has subsequently been improved [62] using the Fast Marching Algorithm. [63][64][65] Other effective methods to match shapes include: Eccentricity transforms, [35,[66][67][68] Skeletonization, [69,70] dynamic programing, [32] Fast Sweeping Algorithm, [71,72] Ant Colony Optimization, [73] Bee Colony optimization, [74][75][76] and Bending invariants.…”

Section: Approaches To Match Shapesmentioning

confidence: 99%

Shape localization, quantification and correspondence using Region Matching Algorithm

Janan

Brady

2015

JBGC

Self Cite

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We propose a method for local, region-based matching of planar shapes, especially as those shapes that change over time. This is a problem fundamental to medical imaging, specifically the comparison over time of mammograms. The method is based on the non-emergence and non-enhancement of maxima, as well as the causality principle of integral invariant scale space. The core idea of our Region Matching Algorithm (RMA) is to divide a shape into a number of "salient" regions and then to compare all such regions for local similarity in order to quantitatively identify new growths or partial/complete occlusions. The algorithm has several advantages over commonly used methods for shape comparison of segmented regions. First, it provides improved key-point alignment for optimal shape correspondence. Second, it identifies localized changes such as new growths as well as complete/partial occlusion in corresponding regions by dividing the segmented region into sub-regions based upon the extrema that persist over a sufficient range of scales. Third, the algorithm does not depend upon the spatial locations of mammographic features and eliminates the need for registration to identify salient changes over time. Finally, the algorithm is fast to compute and requires no human intervention. We apply the method to temporal pairs of mammograms in order to detect potentially important differences between them.

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False Positive Reduction in CADe Using Diffusing Scale Space

Janan¹,

Brady

Highnam³

2014

Breast Imaging

Self Cite

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Abstract.Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image enhancement prior to segmentation. However, this can drastically affect the true positive results and their boundaries. We present a post-segmentation method to reduce the number of false positives by using a diffusion scale space. The method is illustrated using Integral Invariant scale space, though this is not a requirement. It is quite general, does not require any prior information, is fast and easy to compute, and gives very encouraging results. Experiments are performed both on intensity mammograms as well as on Volpara® density maps.

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Shape matching by integral invariants on eccentricity transformed images

Cited by 3 publications

References 8 publications

Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves

Shape localization, quantification and correspondence using Region Matching Algorithm

False Positive Reduction in CADe Using Diffusing Scale Space

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