A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Kok, Kai Yit; Rajendran, Parvathy

doi:10.3390/sym13081337

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…• Binarization, the threshold value determines whether a grayscale pixel value changes from 0 to 255 or black and white [18]. The value chosen as a threshold is 128 [19].…”

Section: B Data Pre-processingmentioning

confidence: 99%

Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction

Handayani

Herwanto

Chandrika

et al. 2021

Knowl. Eng. Data Sci.

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Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish handwritten Javanese characters. The best accuracy is attained at 77.00%, with the network architecture comprising 64 input neurons, 40 hidden neurons, a learning rate of 0.003, a momentum of 0.03, and an iteration of 5000.

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“…• Binarization, the threshold value determines whether a grayscale pixel value changes from 0 to 255 or black and white [18]. The value chosen as a threshold is 128 [19].…”

Section: B Data Pre-processingmentioning

confidence: 99%

Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction

Handayani

Herwanto

Chandrika

et al. 2021

Knowl. Eng. Data Sci.

View full text Add to dashboard Cite

show abstract

“…In recent years, with the development of computer technology and artificial intelligence, image segmentation tasks have gradually become important elements of computer vision [1][2][3]. Semantic segmentation for remote sensing images is a popular branch of image segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images

Sun

Gao

et al. 2022

Symmetry

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In recent years, with the development of deep learning, semantic segmentation for remote sensing images has gradually become a hot issue in computer vision. However, segmentation for multicategory targets is still a difficult problem. To address the issues regarding poor precision and multiple scales in different categories, we propose a UNet, based on multi-attention (MA-UNet). Specifically, we propose a residual encoder, based on a simple attention module, to improve the extraction capability of the backbone for fine-grained features. By using multi-head self-attention for the lowest level feature, the semantic representation of the given feature map is reconstructed, further implementing fine-grained segmentation for different categories of pixels. Then, to address the problem of multiple scales in different categories, we increase the number of down-sampling to subdivide the feature sizes of the target at different scales, and use channel attention and spatial attention in different feature fusion stages, to better fuse the feature information of the target at different scales. We conducted experiments on the WHDLD datasets and DLRSD datasets. The results show that, with multiple visual attention feature enhancements, our method achieves 63.94% mean intersection over union (IOU) on the WHDLD datasets; this result is 4.27% higher than that of UNet, and on the DLRSD datasets, the mean IOU of our methods improves UNet’s 56.17% to 61.90%, while exceeding those of other advanced methods. The implementation code is available on the following Github Link.

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Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering

Tao

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm.

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A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Cited by 4 publications

References 33 publications

Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction

Recognition of Handwritten Javanese Script using Backpropagation with Zoning Feature Extraction

A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images

Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering

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