Illumination-Invariant Feature Point Detection Based on Neighborhood Information

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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Illumination-Invariant Feature Detection based on Shallow Hybrid Network Model

10.1145/3474198.3478273 ◽

2021 ◽

Author(s):

Ruiping Wang ◽

Meihang Zhang ◽

Liangcai Zeng ◽

Kelvin K.L. Wong

Keyword(s):

Network Model ◽

Feature Detection ◽

Hybrid Network ◽

Illumination Invariant ◽

Invariant Feature

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A biologically inspired scale-space for illumination invariant feature detection

Measurement Science and Technology ◽

10.1088/0957-0233/24/7/074024 ◽

2013 ◽

Vol 24 (7) ◽

pp. 074024 ◽

Cited By ~ 23

Author(s):

Vasillios Vonikakis ◽

Dimitrios Chrysostomou ◽

Rigas Kouskouridas ◽

Antonios Gasteratos

Keyword(s):

Feature Detection ◽

Scale Space ◽

Biologically Inspired ◽

Illumination Invariant ◽

Invariant Feature

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Detecting Shilling Attacks with Automatic Features from Multiple Views

Security and Communication Networks ◽

10.1155/2019/6523183 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yaojun Hao ◽

Fuzhi Zhang ◽

Jian Wang ◽

Qingshan Zhao ◽

Jianfang Cao

Keyword(s):

Principal Component Analysis ◽

Recommender Systems ◽

Detection Method ◽

Principal Component ◽

Component Analysis ◽

Experimental Results ◽

Detection Methods ◽

Detection Accuracy ◽

Multiple Views ◽

Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

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Automatic Tracking Tunnel Cables Using Egomotion Estimation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.710.546 ◽

2013 ◽

Vol 710 ◽

pp. 546-549

Author(s):

Chang An Liu ◽

Zhe Sun ◽

Hua Wu ◽

Guo Tian Yang

Keyword(s):

Detection Algorithm ◽

Power Line ◽

Experimental Results ◽

Feature Points ◽

Automatic Tracking ◽

Egomotion Estimation ◽

The Matrix ◽

Point Detection

We proposed an online method of tracking the tunnel cable based on egomotion estimation. The method is firstly applied key point detection algorithm to extract feature points, and then the points are matched to estimate the matrix of egomotion representing the camera movement. Finally, we use the matrix to locate a mask around the cable in each frames captured inside the power line tunnel. The experimental results show robustness and efficiency of our method.

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A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

Journal of Sensors ◽

10.1155/2016/3789570 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Hanlun Li ◽

Aiwu Zhang ◽

Shaoxing Hu

Keyword(s):

Phase Correlation ◽

Feature Point ◽

Reference Image ◽

Feature Points ◽

Scale Invariant ◽

Matching Problem ◽

Invariant Feature ◽

Significant Difference ◽

Scale Invariant Feature ◽

Band Image

In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT) to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

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Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Energies ◽

10.3390/en12020323 ◽

2019 ◽

Vol 12 (2) ◽

pp. 323 ◽

Cited By ~ 8

Author(s):

Qiwei Lu ◽

Zeyu Ye ◽

Yilei Zhang ◽

Tao Wang ◽

Zhixuan Gao

Keyword(s):

Fault Detection ◽

Research And Development ◽

Hardware Implementation ◽

Detection Method ◽

Low Cost ◽

Experimental Methods ◽

Experimental Results ◽

National Standard ◽

Detection Methods ◽

The Relationship

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

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Recognition System for Fatigue Facial Expression of Driver

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4174 ◽

2014 ◽

Vol 644-650 ◽

pp. 4174-4177

Author(s):

Xue Mei Wang ◽

Jia Jun Zhang

Keyword(s):

Facial Expression ◽

Image Recognition ◽

Detection Method ◽

Recognition Rate ◽

Recognition System ◽

Attention Mechanism ◽

Experimental Results ◽

Feature Points ◽

Driver Fatigue ◽

Recognition Model

In order to improve the accuracy of recognition system for fatigue facial expression of driver, driver fatigue expression of this paper, the detection method for key feature points in the fatigue facial expression image of driver is applied in the paper to establish a fatigue expression image recognition model based on attention mechanism. Experimental results show that the algorithm can improve the recognition rate of driver's expression image, so as to record fatigue expression image of driver more accurate.

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Equi-affine differential invariants for invariant feature point detection

European Journal of Applied Mathematics ◽

10.1017/s0956792519000020 ◽

2019 ◽

Vol 31 (2) ◽

pp. 277-296

Author(s):

STANLEY L. TUZNIK ◽

PETER J. OLVER ◽

ALLEN TANNENBAUM

Keyword(s):

Feature Point ◽

Image Feature ◽

Differential Invariants ◽

Feature Points ◽

Affine Invariant ◽

Moving Frames ◽

Feature Detectors ◽

Point Detection ◽

Point Detector ◽

Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

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