A Novel Key-Point Detector Based on Sparse Coding

Hong-Phuoc, Thanh; Guan, Ling

doi:10.1109/tip.2019.2934891

Cited by 26 publications

(18 citation statements)

References 26 publications

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“…Something that the method in [11] is unable to do. To achieve this, the affine distortion model of illumination is used [14]. One of the main assumptions of this model is that the illumination effects in a small area are uniform.…”

Section: Illumination Offset Creationmentioning

confidence: 99%

Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis

Psaila

Hong-Phuoc

Guan

2021

2021 IEEE International Conference on Image Processing (ICIP)

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In this paper, an algorithm for improved image matching is presented using enhancement offsets that are created dynamically with adaptive parameter selection. The algorithm operates by analyzing an input image and building image offsets to improve colour contrast, non-uniform illumination and lack of detail which allows for additional keypoints to be detected by numerous different detectors. Keypoint detection testing on the enhanced images is conducted, as well as image matching using SIFT and SURF. It is quantitatively shown that the proposed algorithm results in visual improvements, as well as in additional, stronger keypoints being detected in all images irrespective of the detector used. Matching experiments are conducted using the Webcam and Oxford/EF datasets wherein the proposed algorithm consistently outputs more correct matches and achieves higher or comparable matching accuracy over other related enhancement algorithms using SIFT and SURF.

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Section: Illumination Offset Creationmentioning

confidence: 99%

Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis

Psaila

Hong-Phuoc

Guan

2021

2021 IEEE International Conference on Image Processing (ICIP)

Self Cite

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“…For example, the Wu's method [16] must provide a reference image when extracting feature points. The method of Hong-Phuoc and Guan [18] does not work well for severely underexposed or overexposed images.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, based on the zero-norm LoG filter, they developed a new feature point detector. Hong-Phuoc and Guan [18] pointed out that most hand-crafted feature detectors rely on pre-designed structures, and this pre-designed structure will be affected by uneven illumination. They proposed a feature detector to locate feature points in the image by calculating the complexity of the blocks surrounding the pixels.…”

Section: Introductionmentioning

confidence: 99%

“…However, Harris was based on the autocorrelation matrix introduces textured patterns and noise while detecting corners. The Hessian matrix contains second-order partial differential, and the feature detector constructed with the Hessian matrix as the response function will inevitably introduce unstable and error points around the structure [18]. Though there are also some other illumination-robust feature detection methods, these methods are not widely used due to own limitations.…”

Section: Introductionmentioning

confidence: 99%

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Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Wang

Zeng

et al. 2020

Sensors

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Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.

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“…Alcantarilla et al [21] applied the anisotropic diffusion theory to construct a nonlinear scale space and extract the maxima of the Hessian in this scale space to detect local features. Hong-Phuoc and Guan [36] utilized a sparse coding method to detect local features. Cho et al [22] utilized higher-order DoG and LoG filters to detect local features.…”

Section: Introductionmentioning

confidence: 99%

Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative

Gao

Ji²,

Liu³

et al. 2020

IEEE Access

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High-efficiency image corner detection, one of the most important and critical basic technology in industrial image processing, is to detect point features from an input image in real-time. In this paper, we propose a new corner detection method which has both good performance of corner detection and real-time processing abilities. Firstly, the integral image and the box filter are combined to obtain the secondorder derivative response in each direction of the image. Secondly, a new coarse screening mechanism for candidate corners is presented to reduce the complexity of the corner metric. Thirdly, a non-maximum suppression operation is utilized to obtain corners. Finally, the performance evaluation on accuracy of corner detection, localization error, average repeatability, region repeatability, different lighting conditions, and execution time are used to assess the proposed method against twelve state-of-the-art methods. The experimental results show that our proposed detector has good corner detection performance and achieves the requirement of real-time processing. INDEX TERMS Corner detection, real-time processing, directional derivative, detection performance.

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A Novel Key-Point Detector Based on Sparse Coding

Cited by 26 publications

References 26 publications

Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis

Image Matching Using Enhancement Offsets With Adaptive Parameter Selection Via Histogram Analysis

Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative

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