A comparison of prefilters in ORB-based object detection

Sharif, Helia; Holzel, Matthew S.

doi:10.1016/j.patrec.2016.11.007

Cited by 12 publications

(13 citation statements)

References 8 publications

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“…In [51], authors tried to understand whether some pre-filters such as a curve pre-filter and pre-filters for edge detection/smoothing were able in object detection accuracy improvement of ORB or not (without having a noticeable effect on the runtime). Moreover, a new survey on co-variance ellipses was done in order to evaluate the performance of the filter and determining the threshold in Hamming distance [51]. In the suggested technique of denoising in [47], the noisy image was divided into a great number of patches which were overlapped.…”

Section: Related Workmentioning

confidence: 99%

“…Combination of the Feature from Accelerated Segment Test (FAST) key point detector and Binary Robust Independent Elementary Features (BRIEF) descriptor with some changes is called ORB [51]. Two main features of ORB which can be highlighted are being resistant to noise and rotation invariant [25].…”

Section: ) Orb Featuresmentioning

confidence: 99%

“…To evaluate the corner intensity, the Harris corner measure has been used [25]. Computation of the moment m pq of the patch I p and centroid C can be done by Equations 2 and 3 [17], [51]:…”

Section: ) Orb Featuresmentioning

confidence: 99%

“…The relative orientation of the patch can be defined via the construction of a vector which starts from the patch center O and ends at the centroid C as below [17], [51]:…”

Section: ) Orb Featuresmentioning

confidence: 99%

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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Nowadays with the rapid development of technologies, machine vision has been used widely in various industries. The main applications of machine vision in industrial product lines are quality control (QC) and quality assurance (QA). The intelligent defects and anomalies recognition throughout the supply chain have come to be an integral part of quality control systems, in particular, in the food and pharmaceutical industries. In these industries, it is a legal requirement in manufacturing processes which can lead to minimizing the total number of defected products as well as maximizing the performance. In this paper, the machine vision has been utilized to monitor and control the proper packaging of drugs in pharmaceutical product lines. The main goal is counting the number of blister cards within a drug package. To tackle this problem, a new model based on object detection, feature extraction, and classification is proposed. Thanks to several strong approaches, such as the Haar cascade, HOG, ORG, Gabor wavelet, Radon transform, KNN, and SVM, and the accuracy over 88% is achieved in our experiments.

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Section: Related Workmentioning

confidence: 99%

Section: ) Orb Featuresmentioning

confidence: 99%

“…To evaluate the corner intensity, the Harris corner measure has been used [25]. Computation of the moment m pq of the patch I p and centroid C can be done by Equations 2 and 3 [17], [51]:…”

Section: ) Orb Featuresmentioning

confidence: 99%

“…The relative orientation of the patch can be defined via the construction of a vector which starts from the patch center O and ends at the centroid C as below [17], [51]:…”

Section: ) Orb Featuresmentioning

confidence: 99%

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A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Bahaghighat¹,

Akbari²,

Xin

2019

IEEE Access

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“…However, this technique requires high computation cost. The Oriented FAST and Rotated BRIEF (ORB) show good performances and low computation costs in keypoint detection [11,17]. ORB utilizes the Feature from Accelerated Segment Test (FAST) to extract keypoints.…”

Section: Orb Detector and Improved Matching Techniquementioning

confidence: 99%

Pose Estimation of Automatic Battery-Replacement System Based on ORB and Improved Keypoints Matching Method

et al. 2019

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This paper presents an improved Oriented Feature from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) keypoints matching method for pose estimation of automatic battery-replacement systems. The key issue of the system is how to precisely estimate the pose of the camera in respect to the battery. In our system, the pose-estimation hardware module is mounted onto the robot manipulator, composed of double high brightness LED light source, one monocular camera, and two laser rangefinders. The camera is utilized to take an image of the battery, the laser rangefinders on both sides of the camera are utilized to detect the real-time distance between the battery and the pose-estimation system. The estimation result is significantly influenced by the matching result of the keypoints detected by the ORB technique. The modified matching procedure, based on spatial consistency nearest hamming distance searching method, is used to determine the correct correspondences. Meanwhile, the iterative reprojection error minimization algorithm is utilized to discard incorrect correspondences. Verified by the experiments, the results reveal that this method is highly reliable and able to achieve the required positioning accuracy. The positioning error is lower than 1 mm.

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Handling hypercolumn deep features in machine learning for rice leaf disease classification

Akyol

2022

Multimed Tools Appl

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A comparison of prefilters in ORB-based object detection

Cited by 12 publications

References 8 publications

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

A Machine Learning-Based Approach for Counting Blister Cards Within Drug Packages

Pose Estimation of Automatic Battery-Replacement System Based on ORB and Improved Keypoints Matching Method

Handling hypercolumn deep features in machine learning for rice leaf disease classification

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