UGC: Real-Time, Ultra-Robust Feature Correspondence via Unilateral Grid-Based Clustering

Zheng, Zhaohui; Ma, Yong; Zheng, Hong; Ju, Jianping; Li, Mingyu

doi:10.1109/access.2018.2871729

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…3 = 16 building blocks, each block is 3 layers, so there are a total of 1 ? 16 9 3 = 49 convolutional layers [20,21].…”

Section: Resnet and Se-blockmentioning

confidence: 99%

Classification of jujube defects in small data sets based on transfer learning

Zheng

et al. 2021

Neural Comput & Applic

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Although convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

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“…3 = 16 building blocks, each block is 3 layers, so there are a total of 1 ? 16 9 3 = 49 convolutional layers [20,21].…”

Section: Resnet and Se-blockmentioning

confidence: 99%

Classification of jujube defects in small data sets based on transfer learning

Zheng

et al. 2021

Neural Comput & Applic

Self Cite

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“…This approximation does not hold true in general and fails in practice for forward/backward translations, where the depth dependent projection scales nonuniformly. Also [63] employ locality information to filter match outliers and Zheng et al [64] compute cluster centers from fixed grid patches to compare between frames. Wrong matches in the grid cells, however, shift the cluster center and the method requires initial brute force matching.…”

Section: Related Workmentioning

confidence: 99%

DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching

Ruhkamp,

Gong,

Navab

et al. 2020

Preprint

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Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time. Current feature matching pipelines either rely solely on the descriptive capabilities of the feature extractor or need computationally complex optimization schemes. We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures. The theoretical backbone of the method lies within a probabilistic formulation of feature matching and the respective study of physically motivated constraints. A dynamically adaptable local motion model encapsulates groups of features in an efficient data structure. Temporal constraints transfer information of the local motion model across time, thus additionally reducing the search space complexity for matching. DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods while being computationally more efficient.

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“…• Proving that the FUGC algorithm is more efficient and robust than traditional algorithms such as RANSAC [24], vector field consensus (VCF) [25], grid based motion statistics (GSM) [26] and unilateral grid based clustering (UGC) [27] when applied to the standard test set, which is very important for real-time video image analysis.…”

Section: Clustering Analysis Of the Local Regionmentioning

confidence: 99%

Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

Zheng

et al. 2019

Remote Sensing

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In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

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UGC: Real-Time, Ultra-Robust Feature Correspondence via Unilateral Grid-Based Clustering

Cited by 11 publications

References 37 publications

Classification of jujube defects in small data sets based on transfer learning

Classification of jujube defects in small data sets based on transfer learning

DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching

Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

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