Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis

Şekeroğlu, Kazım; Soysal, Ömer

doi:10.1007/s00138-017-0868-9

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…The main steps of the traditional SIFT algorithm include the following: Firstly, it is necessary to establish the scale space of the image [20], and specifically construct the Gaussian function of the varying scale and the original image convolution [21]. The formulas for this are as follows:…”

Section: Improved Sift Algorithmmentioning

confidence: 99%

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

Li,

et al. 2024

Sensors

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The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

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Section: Improved Sift Algorithmmentioning

confidence: 99%

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

Li,

et al. 2024

Sensors

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“…How to effectively extract features that conform to the data distribution structure has always been a hot issue in this field. Traditional feature extraction methods mostly select features based on different tasks and data designs, such as Gabor features [45], SIFT [46], local binary patterns [47], etc. However, designing a good feature is not simple, especially in the case of a large amount of data, it takes time and effort.…”

Section: E Deep Neural Networkmentioning

confidence: 99%

A Depression Recognition Method for College Students Using Deep Integrated Support Vector Algorithm

Ding

Chen

et al. 2020

IEEE Access

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The infinite increase in population, the pressure of survival, and the pressure of learning make the competition between people more and more fierce. Some college students have also been in a state of anxiety and panic for a long time, and mental health diseases have shown an explosive growth trend. The development of social networks such as Weibo, QQ, and WeChat not only provides more convenient communication methods for college students, but also provides a new emotional vent window for college students. They can record their living conditions in real time through social networks and interact with friends to express emotions and relieve stress. At the same time, the development of social networks has also provided a new way for the detection of depressed users. The current computer technology analyzes the user's social network data to detect the user's depression. This study uses text-level mining of Sina Weibo data from college students to detect depression among college students. First, collect text information of college student users in Sina Weibo, and construct the text information into input data that can be used for machine learning. Deep neural networks are used for feature extraction. An deep integrated support vector machine(DISVM) algorithm is introduced to classify the input data, and finally realize the recognition of depression. DISVM makes the recognition model more stable and improves the accuracy of depression diagnosis to a certain extent. Simulation experiments verify that the proposed depression recognition scheme can detect potential depression patients in the college student population through Sina Weibo data.INDEX TERMS Depression recognition, deep integrated support vector machine, college students, Sina Weibo.

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“…Finally, the extracted feature vectors have good illumination invariance by normalization. Therefore, this paper selects SITF to extract feature vectors, e.g., rotation angle and brightness of logistics packaging boxes, which are classification basis of SVM classifier [21], [22].…”

Section: Svm-based Defects Detection Of Logistics Packaging Boxesmentioning

confidence: 99%

Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing

et al. 2020

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The accuracy of defects detection for logistics packaging box is a critical factor to ensure the quality of goods under edge computing environment. Now, there are few works on this issue. This paper designs an image acquisition process system and then proposes a novel approach in addressing logistics packaging box defect detection (LPDD) on the basis of support vector machine (SVM). Firstly, this paper designs a new mean denoising template and Laplace sharpening template, which are more suitable for logistics packaging based on image preprocessing, image enhancement and other relevant technical theories. Then in the stage of noise removal, this paper proposes an improved morphological method and a gray morphological edge detection algorithm. The edge defect detection of a gray image is carried out by combining the above two methods. Hence, LPDD extracts the features of logistics packaging box by using scale-invariant feature transform (SIFT) algorithm and designs SVM classifiers to classify the logistics package defects. This paper uses a large number of samples to train, learn and test the designed SVM classifier. The simulation results show that the proposed LPDD method can accurately detect two common types of defects in logistics packaging boxes with higher accuracy and less computational costs, which meets the requirements of manufacturers on the classification and recognition of defects in machine vision detection system. INDEX TERMS Package defects, logistics packaging box, support vector machine (SVM).

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Comparison of SIFT, Bi-SIFT, and Tri-SIFT and their frequency spectrum analysis

Cited by 8 publications

References 14 publications

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm

A Depression Recognition Method for College Students Using Deep Integrated Support Vector Algorithm

Detecting Defects With Support Vector Machine in Logistics Packaging Boxes for Edge Computing

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