Integral Contour Angle: An Invariant Shape Descriptor for Classification and Retrieval of Leaf Images

2018 ◽  
Author(s):  
Feng Ni ◽  
Bin Wang
Keyword(s):  
Shape Descriptor ◽  
Integral Contour ◽  
Invariant Shape

scholarly journals Robust Affine Invariant Shape Descriptors

2021 ◽  
Author(s):  
Ye Mei
Keyword(s):  
Shape Descriptor ◽  
Zernike Moment ◽  
Shape Descriptors ◽  
Affine Invariant ◽  
Shape Features ◽  
Image Content ◽  
Shape Representations ◽  
Mellin Moment ◽  
Change Of Perspective ◽  
Invariant Shape

With the increasing number of available digital images, there is an urgent need of image content description to facilitate content based image retrieval (CBIR). Besides colour and texture, shape is an important low level feature in describing image content. An object can be photographed from different distances and angles. However, we often want to classify the images of the same object into one class, despite the change of perspective. So, it is desired to extract shape features that are invariant to the change of perspective. The shape of an object from one viewpoint to another can be linked through an affine transformation, if it is viewed from a much larger distance than its size along the line of sight. Those invariant shape features are known as affine invariant shape representations. Because of the change of perspective, it is more difficult to develop affine invariant shape representations than normal ones. The goal of this work is to develop affine invariant shape descriptors. Through shape retrieval experiments, we find that the performance of the existing affine invariant shape representations are not satisfactory. Especially, when the shape boundary is corrupted by noise, their performance degrades quickly. In this work, two new affine invariant contour-based shape descriptors, the ICA Fourier shape descriptor (ICAFSD) and the whitening Fourier shape descriptor (WFSD) have been developed. They perform better than most of the existing affine invariant shape representations, while having compact feature size and low computational time requirement. Four region-based affine-invariant shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD), the whitening Zernike moment shape descriptor (WZMSD), the ICA orthogonal Fourier Mellin moment shape descriptor (ICAOFMMSD), and the whitening orthogonal Fourier Mellin moment shape descriptor (WOFMMSD), are also proposed, in this work. They can be applied to both simple and complex shapes, and have close to perfect performance in retrieval experiments. The advantage of those newly proposed shape descriptors is even more apparent in experiments on shapes with added boundary noise: Their performance does not deteriorate as much as the existing ones.

scholarly journals Mesh Generation and Flexible Shape Comparisons for Bio-Molecules

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhanheng Gao ◽  
Reihaneh Rostami ◽  
Xiaoli Pang ◽  
Zhicheng Fu ◽  
Zeyun Yu
Keyword(s):  
Software Tool ◽  
Shape Descriptor ◽  
Beltrami Operator ◽  
Molecular Surface ◽  
Modeling Tools ◽  
Generating Method ◽  
Small Set ◽  
User Friendly ◽  
Invariant Shape ◽  
Memory Efficient

AbstractNovel approaches for generating and comparing flexible (non-rigid) molecular surface meshes are developed. The mesh-generating method is fast and memory-efficient. The resulting meshes are smooth and accurate, and possess high mesh quality. An isometric-invariant shape descriptor based on the Laplace- Beltrami operator is then explored for mesh comparing. The new shape descriptor is more powerful in discriminating different surface shapes but rely only on a small set of signature values. The shape descriptor is applied to shape comparison between molecules with deformed structures. The proposed methods are implemented into a program that can be used as a stand-alone software tool or as a plug-in to other existing molecular modeling tools. Particularly, the code is encapsulated into a software toolkit with a user-friendly graphical interface developed by the authors.

scholarly journals Robust Affine Invariant Shape Descriptors

2021 ◽  
Author(s):  
Ye Mei
Keyword(s):  
Shape Descriptor ◽  
Zernike Moment ◽  
Shape Descriptors ◽  
Affine Invariant ◽  
Shape Features ◽  
Image Content ◽  
Shape Representations ◽  
Mellin Moment ◽  
Change Of Perspective ◽  
Invariant Shape

With the increasing number of available digital images, there is an urgent need of image content description to facilitate content based image retrieval (CBIR). Besides colour and texture, shape is an important low level feature in describing image content. An object can be photographed from different distances and angles. However, we often want to classify the images of the same object into one class, despite the change of perspective. So, it is desired to extract shape features that are invariant to the change of perspective. The shape of an object from one viewpoint to another can be linked through an affine transformation, if it is viewed from a much larger distance than its size along the line of sight. Those invariant shape features are known as affine invariant shape representations. Because of the change of perspective, it is more difficult to develop affine invariant shape representations than normal ones. The goal of this work is to develop affine invariant shape descriptors. Through shape retrieval experiments, we find that the performance of the existing affine invariant shape representations are not satisfactory. Especially, when the shape boundary is corrupted by noise, their performance degrades quickly. In this work, two new affine invariant contour-based shape descriptors, the ICA Fourier shape descriptor (ICAFSD) and the whitening Fourier shape descriptor (WFSD) have been developed. They perform better than most of the existing affine invariant shape representations, while having compact feature size and low computational time requirement. Four region-based affine-invariant shape descriptors, the ICA Zernike moment shape descriptor (ICAZMSD), the whitening Zernike moment shape descriptor (WZMSD), the ICA orthogonal Fourier Mellin moment shape descriptor (ICAOFMMSD), and the whitening orthogonal Fourier Mellin moment shape descriptor (WOFMMSD), are also proposed, in this work. They can be applied to both simple and complex shapes, and have close to perfect performance in retrieval experiments. The advantage of those newly proposed shape descriptors is even more apparent in experiments on shapes with added boundary noise: Their performance does not deteriorate as much as the existing ones.

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