Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment

Kashif, Muhammad; Deserno, Thomas M.; Haak, Daniel; Jonas, Stephan

doi:10.1016/j.compbiomed.2015.11.006

Cited by 102 publications

(51 citation statements)

References 21 publications

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“…By doing this, we can compare the performance of matching method under the same conditions. In addition, SIFT showed better performance compared with the other feature descriptors such as SURF and BRISK in our experiment which is consistent with other findings [35,36] for images with various deformations. Although SIFT has slower speed for extracting features, it was determined to be an appropriate choice for the feature descriptor.…”

Section: Image Set Annotationssupporting

confidence: 82%

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

et al. 2017

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Deformable objects have changeable shapes and they require a different method of matching algorithm compared to rigid objects. This paper proposes a fast and robust deformable object matching algorithm. First, robust feature points are selected using a statistical characteristic to obtain the feature points with the extraction method. Next, matching pairs are composed by the feature point matching of two images using the matching method. Rapid clustering is performed using the BST (Binary Search Tree) method by obtaining the geometric similarity between the matching pairs. Finally, the matching of the two images is determined after verifying the suitability of the composed cluster. An experiment with five different image sets with deformable objects confirmed the superior robustness and independence of the proposed algorithm while demonstrating up to 60 times faster matching speed compared to the conventional deformable object matching algorithms.

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Section: Image Set Annotationssupporting

confidence: 82%

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

et al. 2017

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“…[34] Another study, using a larger sample of 1100 images, reported an average difference of 0.60 years when compared to two experienced radiologists. [35] One study comparing the performance of different algorithms to estimate skeletal age reported a root-mean-square error (RMSE) of 0.24 years with ANN and 0.25 years with a genetic algorithm when compared to traditional estimates of skeletal age. [36] Taking panoramic radiographs make orthodontists legally liable if they overlook diagnosing a lesion or a tumor.…”

Section: Decision Treesmentioning

confidence: 99%

Applications of artificial intelligence and machine learning in orthodontics

Asiri

Tadlock

Schneiderman

et al. 2020

APOS

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Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

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“…In 2015, five leading feature extraction algorithms, SIFT, SURF, BRIEF, BRISK, and FREAK, were used to generate keypoint descriptors of radiographs to classify assessments of bone age [4]. After comparing the five algorithms, SIFT performed the best in terms of precision.…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Jin

Kim

2017

Applied Sciences

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Abstract:With the increased development of 360-degree production technologies, artwork has recently been photographed without authorization. To prevent this infringement, we propose an artwork identification methodology for 360-degree images. We transform the 360-degree image into a three-dimensional sphere and wrap it with a polyhedron. On the sphere, several points are located on the polyhedron to determine the width, height, and direction of the rectilinear projection. The 360-degree image is divided and transformed into several rectilinear projected images to reduce the adverse effects from the distorted panoramic image. We also propose a method for improving the identification precision of artwork located at a highly distorted position using the difference of keypoint shapes. After applying the proposed methods, identification precision is increased by 45% for artwork that is displayed on a 79-inch monitor in a seriously distorted position with features that were generated by scale-invariant feature transformations.

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Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment

Cited by 102 publications

References 21 publications

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Applications of artificial intelligence and machine learning in orthodontics

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

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