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Learning to Distill Convolutional Features into Compact Local Descriptors
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021Extracting local descriptors or features is an essential step in solving image matching problems. Recent methods in the literature mainly focus on extracting effective descriptors, without much attention to the size of the descriptors. In this work, we study how to learn a compact yet effective local descriptor.
Jongmin Lee 0005 +4 more
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Selection of Optimal Bands for Hyperspectral Local Feature Descriptor
IEEE Geoscience and Remote Sensing Letters, 2022The use of hyperspectral images (HSI) and 3D data has proven to be an efficient combination for numerous applications. A common method of obtaining corresponding data is 3D reconstruction from HSI, for which the local feature descriptor is vital to the final accuracy.
Zhaocong Wu, Zhao Yan
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Performance Evaluation of Binary Descriptors of Local Features
2014The article is devoted to the evaluation of performance of image features with binary descriptors for the purpose of their utilization in recognition of objects by service robots. In the conducted experiments we used the dataset and followed the methodology proposed by Mikolajczyk and Schmid.
Jan Figat +2 more
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An efficient iris recognition using local feature descriptor
2009 16th IEEE International Conference on Image Processing (ICIP), 2009This paper presents a robust iris recognition system using local feature descriptor. The proposed biometric system accounts for two crucial issues. Firstly, iris texture is usually occluded by upper and lower eyelids. To handle this problem, a novel sector based normalisation is proposed.
Hunny Mehrotra +3 more
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Locality-preserving descriptor for robust texture feature representation
Neurocomputing, 2016Recent texture classification methods include rotation-invariant feature-encoding procedures based on local binary patterns. Such methods are robust to rotational changes, but they result in discarded locality information (i.e., geometrical information) of texture images.
Jongbin Ryu, Hyun Seung Yang
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An improved local feature descriptor via soft binning
2010 IEEE International Conference on Image Processing, 2010We describe a robust feature descriptor called soft ordinal spatial intensity distribution (soft OSID) that is invariant to any monotonically increasing brightness changes. In traditional histogram-based feature descriptors, each pixel is explicitly assigned to a single histogram bin, making them not robust to image deformations and appearance changes.
Feng Tang, Suk Hwan Lim, Nelson L. Chang
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A Sparse Local Feature Descriptor for Robust Face Recognition
2011A good face recognition algorithm should be robust against variations caused by occlusion, expression or aging changes etc. However, the performance of holistic feature based methods would drop dramatically as holistic features are easily distorted by those variations.
Na Liu 0017 +2 more
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Multi-directional local gradient descriptor: A new feature descriptor for face recognition
Image and Vision Computing, 2019Abstract The performance of the face recognition systems is vulnerable to occlusion, light and expression changes and such constraints need to be handled effectively in a robust face recognition system. This paper presents a new multi-directional local gradient descriptor (MLGD) method for face recognition based on local directional gradient features
Vishwanath C. Kagawade +1 more
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Local Feature Descriptors with Deep Hypersphere Learning
2021 IEEE International Conference on Image Processing (ICIP), 2021Song Wang 0008 +4 more
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A Comprehensive Performance Evaluation of 3D Local Feature Descriptors
International Journal of Computer Vision, 2015A number of 3D local feature descriptors have been proposed in the literature. It is however, unclear which descriptors are more appropriate for a particular application. A good descriptor should be descriptive, compact, and robust to a set of nuisances. This paper compares ten popular local feature descriptors in the contexts of 3D object recognition,
Yulan Guo +5 more
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