A Review of Local Binary Pattern Based texture feature extraction

Kaur, Navneet; Nazir, Nahida; Manik,

doi:10.1109/icrito51393.2021.9596485

Cited by 22 publications

(3 citation statements)

References 22 publications

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“…As illustrated in Figure 1, first, we need to extract image features, such as gradient features (Al Sadeque et al, 2019), color features (Garcia-Lamont et al, 2018), entropy features (Hu et al, 2021) and texture features (Kaur et al, 2021). Second, we need to create MVD, these feature data are used as view data.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

Cai,

Yang,

2024

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PurposeMulti-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.Design/methodology/approachThe authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.FindingsThe authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.Originality/valueMost of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

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Section: Proposed Algorithmmentioning

confidence: 99%

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

Cai,

Yang,

2024

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“…This process optimizes detecting features such as shapes, angles, or movement in digital images or videos. Some of the current methods used in feature extraction include the Histogram of Oriented Gradients [41][42][43], Local Binary Pattern [44][45][46], Deformable Part Model [47][48][49], and Aggregate Channel Feature (ACF) [50][51][52].…”

Section: Pedestrian Detectionmentioning

confidence: 99%

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Guerrero-Ibañez,

Contreras-Castillo,

Amezcua-Valdovinos

et al. 2023

Machines

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Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.

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“…In response to factors such as illumination changes, researchers have developed a variety of image processing and matching algorithms based on features such as color [19], texture [20], and shape [21]. Multiple images captured under different lighting conditions can be used to reconstruct the target object in 3D [22], thereby reducing the impact of illumination changes on pose measurement.…”

Section: Introductionmentioning

confidence: 99%

A Review of Machine Vision Pose Measurement

Wang¹,

Beng²,

Rahmat³

et al. 2023

Preprint

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This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: The introduction in Section 1 provides a brief overview of the field of machine vision pose measurement. Section 2 describes the commonly used algorithms for machine vision pose measurement. Section 3 discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Section 4 presents the applications of machine vision pose measurement in various fields. The paper provides specific examples of how machine vision pose measurement is used in each of these fields. Finally, Section 5 summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.

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A Review of Local Binary Pattern Based texture feature extraction

Cited by 22 publications

References 22 publications

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

A Review of Machine Vision Pose Measurement

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