An area-based shape representation for affine invariant content-based retrieval

1998 ◽  
pp. 132-142 ◽  
Author(s):  
Horace H S Ip ◽  
Dinggang Shen ◽  
Wai-him Wong ◽  
Ken C K Law
Keyword(s):  
Shape Representation ◽  
Affine Invariant ◽  
Content Based Retrieval

Affine-invariant curve normalization for object shape representation, classification, and retrieval

2001 ◽  
Vol 13 (2) ◽  
pp. 80-94 ◽  
Author(s):  
Yannis Avrithis ◽  
Yiannis Xirouhakis ◽  
Stefanos Kollias
Keyword(s):  
Shape Representation ◽  
Invariant Curve ◽  
Affine Invariant ◽  
Object Shape

scholarly journals Affine-Invariant Feature Extraction for Activity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Samy Sadek ◽  
Ayoub Al-Hamadi ◽  
Gerald Krell ◽  
Bernd Michaelis
Keyword(s):  
Activity Recognition ◽  
Shape Representation ◽  
Svm Classifier ◽  
Affine Invariant ◽  
Computationally Efficient ◽  
Invariant Representation ◽  
Affine Invariants ◽  
3D Volume ◽  
Affine Moment Invariants ◽  
Invariant Shape

We propose an innovative approach for human activity recognition based on affine-invariant shape representation and SVM-based feature classification. In this approach, a compact computationally efficient affine-invariant representation of action shapes is developed by using affine moment invariants. Dynamic affine invariants are derived from the 3D spatiotemporal action volume and the average image created from the 3D volume and classified by an SVM classifier. On two standard benchmark action datasets (KTH and Weizmann datasets), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

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