Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Wang, Ruiping; Zeng, Liangcai; Wu, Shiqian; Cao, Wei; Wong, Kelvin Kian Loong

doi:10.3390/s20226630

Cited by 4 publications

(2 citation statements)

References 40 publications

(55 reference statements)

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“…To solve these problems, a new prediction method based on DL using a key-point detection algorithm is proposed in our study. The key-point detection algorithm was considered the most suitable model because of the ability of the algorithm to find a specific point where the WBL passes through the tibia plateau [ 22 , 23 ]. Key-point detection algorithms are often used for pose estimation, face detection, and object detection [ 17 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Nam

Park

et al. 2023

JCM

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(1) Background: There have been many attempts to predict the weight-bearing line (WBL) ratio using simple knee radiographs. Using a convolutional neural network (CNN), we focused on predicting the WBL ratio quantitatively. (2) Methods: From March 2003 to December 2021, 2410 patients with 4790 knee AP radiographs were randomly selected using stratified random sampling. Our dataset was cropped by four points annotated by a specialist with a 10-pixel margin. The model predicted our interest points, which were both plateau points, i.e., starting WBL point and exit WBL point. The resulting value of the model was analyzed in two ways: pixel units and WBL error values. (3) Results: The mean accuracy (MA) was increased from around 0.5 using a 2-pixel unit to around 0.8 using 6 pixels in both the validation and the test sets. When the tibial plateau length was taken as 100%, the MA was increased from approximately 0.1, using 1%, to approximately 0.5, using 5% in both the validation and the test sets. (4) Conclusions: The DL-based key-point detection algorithm for predicting lower limb alignment through labeling using simple knee AP radiographs demonstrated comparable accuracy to that of the direct measurement using whole leg radiographs. Using this algorithm, the WBL ratio prediction with simple knee AP radiographs could be useful to diagnose lower limb alignment in osteoarthritis patients in primary care.

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Section: Discussionmentioning

confidence: 99%

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Nam

Park

et al. 2023

JCM

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“…After that, RFA fuses the features of multiple branches and uses 1 × 1 convolution to adjust the channel size. Finally, the receptive field enhancement module also simulates the residual structure using a shortcut connection method, weights the input and summarizes the features of multiple branches to obtain the result [ 22 ]. To accommodate various situations, this section proposes two similar structures RFA and RFA+.…”

Section: Methodsmentioning

confidence: 99%

Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network

Dai

Zhu

et al. 2022

Sensors

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Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic. Methods: This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm. Results: We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four. Conclusion: The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets.

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Improving Weeds Detection in Pastures Using Illumination Invariance Techniques

Alyatimi,

Al-Dala’in,

Chung

et al. 2024

Lecture Notes in Networks and Systems

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Illumination-Invariant Feature Point Detection Based on Neighborhood Information

Cited by 4 publications

References 40 publications

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

Single-Stage Underwater Target Detection Based on Feature Anchor Frame Double Optimization Network

Improving Weeds Detection in Pastures Using Illumination Invariance Techniques

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