Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification

Tang, Liang; Ma, Shuhua; Ma, Xianchun; You, Hairong

doi:10.3390/app12178448

Cited by 14 publications

(10 citation statements)

References 35 publications

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“…The choice of feature extraction method should be based on the specific characteristics of the data used and the goal of the computer vision application. HOG is better suited for applications that require robust shape and texture analysis [31], LBP is suitable for applications that require efficient texture recognition [32] and SIFT is ideal for applications that require robustness to scale changes and rotation [33].…”

Section: Feature Extractionmentioning

confidence: 99%

A Parallel Approach of Cascade Modelling Using MPI4Py on Imbalanced Dataset

Suprapto,

Wahyono,

Rokhman

et al. 2024

IJCDS

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Machine learning is crucial in categorizing data into specific classes based on their features. However, challenges emerge, especially in classification, when dealing with imbalanced datasets. An imbalanced dataset occurs when there is a disproportionate number of samples across different classes. It leads to a machine learning model's bias towards the majority class and poor recognition of minority classes, often resulting in notable prediction inaccuracies for those less represented classes. This research proposes a cascade and parallel architecture in the training process to enhance accuracy and speed compared to non-cascade and sequential. This research will evaluate the performance of the SVM and Random Forest methods. Our findings reveal that employing the Random Forest method, configured with 100 trees, substantially enhances classification accuracy by 4.72%, elevating it from 58.87% to 63.59% compared to non-cascade classifiers. Furthermore, adopting the Message Passing Interface for Python (MPI4Py) for parallel processing across multiple cores or nodes demonstrates a remarkable increase in training speed. Specifically, parallel processing was found to accelerate the training process by up to 4.35 times, reducing the duration from 1725.86 milliseconds to a mere 396.54 milliseconds. These results highlight the advantages of integrating parallel processing with a cascade architecture in machine learning models, particularly in addressing the challenges associated with imbalanced datasets. This research demonstrates the potential for substantial improvements in classification tasks' accuracy and efficiency.

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Section: Feature Extractionmentioning

confidence: 99%

A Parallel Approach of Cascade Modelling Using MPI4Py on Imbalanced Dataset

Suprapto,

Wahyono,

Rokhman

et al. 2024

IJCDS

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“…Before the dominance of deep learning algorithms, hand-designed features were also widely used in the field of machine vision, such as the well-known SIFT (Tang et al, 2022) operator. How to design compact hand-designed features for asynchronous pulse signals (Ramesh et al, 2019), and have the robust characteristics of scale and rotation invariance, is an important technology for neuromorphic vision sensors to be applied to vision tasks.…”

Section: Hand-designed Featuresmentioning

confidence: 99%

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Li,

Sun

2023

Front. Mater.

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Introduction: Advances in machine vision and mobile electronics will be accelerated by the creation of sophisticated optoelectronic vision sensors that allow for sophisticated picture recognition of visual information and data pre-processing. Several new types of vision sensors have been devised in the last decade to solve these drawbacks, one of which is neuromorphic vision sensors, which have exciting qualities such as high temporal resolution, broad dynamic range, and low energy consumption. Neuromorphic sensors are inspired by the working principles of biological sensory neurons and would be useful in telemedicine, health surveillance, security monitoring, automatic driving, intelligent robots, and other applications of the Internet of Things.Methods: This paper provides a comprehensive review of various state-of-the-art AI vision sensors and frameworks.Results: The fundamental signal processing techniques deployed and the associated challenges were discussed.Discussion: Finally, the role of vision sensors in computer vision is also discussed.

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“…The parameter setup of all flights are defined in Table . 2. Based on the mapping containing the image infomation and GPS-tagged locations, this paper uses the scale-invariant feature transform (SIFT) [18,19] to match the current vision image with the dataset which is recorded in previous flights. Therefore, the current location of the drone could be computed and given to the drone for safetying flight with no GPS conditions.…”

Section: Experiments Setupmentioning

confidence: 99%

Navigation for drones in GPS-Denied environments based on Vision processing

Ninh¹,

Vân²,

Dang³

et al. 2022

Proceedings of the Seventh International Conference on Research in Intelligent and Computing in Engineering

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4This paper presents a method for the navigation of drones in GPS-denied Environments based on vision processing. A camera is attached to the drone to fly in full conditions like GPS, video streaming, control waypoint, etc. Then, some information such as the image features and location of the drone is extracted and stored, which is used for the next flight with GPS-denied Environments. The results are shown that the drone positions could estimate with high accuracy.Index Terms4UAV, GPS-Denied, vision

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Research on Image Matching of Improved SIFT Algorithm Based on Stability Factor and Feature Descriptor Simplification

Cited by 14 publications

References 35 publications

A Parallel Approach of Cascade Modelling Using MPI4Py on Imbalanced Dataset

A Parallel Approach of Cascade Modelling Using MPI4Py on Imbalanced Dataset

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Navigation for drones in GPS-Denied environments based on Vision processing

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