Comparison of Handcrafted Features and Deep Learning in Classification of Medical X-ray Images

Zare, Mohammad Reza; Alebiosu, David Olayemi; Lee, Sheng Long

doi:10.1109/infrkm.2018.8464688

Cited by 16 publications

(14 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, an extensive comparison between deep learning based models and hand-crafted based models are presented in Table 3 for human action recognition. [208][209][210][211].…”

Section: Pose Estimation and Multi-view Action Recognitionmentioning

confidence: 99%

A Review on Computer Vision-Based Methods for Human Action Recognition

et al. 2020

View full text Add to dashboard Cite

Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.

show abstract

“…In addition, an extensive comparison between deep learning based models and hand-crafted based models are presented in Table 3 for human action recognition. [208][209][210][211].…”

Section: Pose Estimation and Multi-view Action Recognitionmentioning

confidence: 99%

A Review on Computer Vision-Based Methods for Human Action Recognition

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Liu & Xu, 2022;Miao & Srimahachota, 2021;Ni et al, 2019;Piyathilaka et al, 2020;Żarski et al, 2022;Zhang & Yuen, 2021;Zheng et al, 2022;Zhou et al, 2022;Zou et al, 2022). Alternatively, several studies have focused on pattern recognition crack TA B L E 1 Comparison of unsupervised pattern recognitions, machine learning (ML) and artificial neural networks (Al-Faris et al, 2020;Georgiou et al, 2020;Salehi & Burgueño, 2018;Wang et al, 2020;Zare et al, 2018) detection as another approach to image-based methods (Dorafshan et al, 2019;Iyer & Sinha, 2005;Li et al, 2014;Miao et al, 2020;Safaei et al, 2022;W. Wang et al, 2018;Y.…”

Section: Introductionmentioning

confidence: 99%

“…The artificial neural network can spontaneously extract features of crack patterns from a training set of images and does not depend on pre‐processing to ensure high accuracy. This approach utilizes a set of large data for training the predictive model that necessitates a computationally powerful processor (Zare et al., 2018). Other machine learning (ML) methods have high computational performance and are appropriate for traditional and data‐driven SHM systems.…”

Section: Introductionmentioning

confidence: 99%

Methodology to integrate augmented reality and pattern recognition for crack detection

Malek

Mohammadkhorasani

Moreu

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

In-field visual inspections have inherent challenges associated with humans such as low accuracy, excessive cost and time, and safety. To overcome these barriers, researchers and industry leaders have developed image-based methods for automatic structural crack detection. More recently, researchers have proposed using augmented reality (AR) to interface human visual inspection with automatic image-based crack detection. However, to date, AR crack detection is limited because: (1) it is not available in real time and (2) it requires an external processing device. This paper describes a new AR methodology that addresses both problems enabling a standalone real-time crack detection system for field inspection. A Canny algorithm is transformed into the single-dimensional mathematical environment of the AR headset digital platform. Then, the algorithm is simplified based on the limited headset processing capacity toward lower processing time. The test of the AR crack-detection method eliminates AR image-processing dependence on external processors and has practical real-time image-processing.

show abstract

“…Therefore, these image processing techniques require expertise in the specific discipline to identify the important features characterizing the categories, and to then extract them from the images [13][14][15][16]. These expert-defined features are fed into the machine learning classification algorithms to create a model [17][18][19][20][21][22][23] that learns the best mapping between the features and the different categories. This model is subsequently applied on new, unseen images.…”

Section: Review Introduction: Traditional Machine Learning Vs Deep Learningmentioning

confidence: 99%

The role of convolutional neural networks in scanning probe microscopy: a review

Azuri¹,

Rosenhek‐Goldian²,

Regev‐Rudzki³

et al. 2021

Beilstein J. Nanotechnol.

View full text Add to dashboard Cite

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

show abstract

Comparison of Handcrafted Features and Deep Learning in Classification of Medical X-ray Images

Cited by 16 publications

References 32 publications

A Review on Computer Vision-Based Methods for Human Action Recognition

A Review on Computer Vision-Based Methods for Human Action Recognition

Methodology to integrate augmented reality and pattern recognition for crack detection

The role of convolutional neural networks in scanning probe microscopy: a review

Contact Info

Product

Resources

About