This paper describes a new application of the technique known as Gradient Pattern Analysis (GPA), focused here on computer vision. In the GPA domain, the image is translated into a tessellation triangulation field bas...
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This paper constructs an energy model based on local features used in stereo matching. The local features include the similarity between different image areas, the matching cost function pattern, the connection betwee...
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This paper constructs an energy model based on local features used in stereo matching. The local features include the similarity between different image areas, the matching cost function pattern, the connection between neighbor pixels, and the occlusion geometric relationship. Based on these features, we define the weight of each data term and smoothing term in the energy function and then design an algorithm to solve the energy model and get disparity results. The significant improvements of this paper include as following. 1) We modify the structure of the energy function. First, we define the weight of the data term based on the reliability of its corresponding disparity result, which is obtained by cost function features and the occlusion geometric relationship. Then we define the weight of the smoothing term by analyzing the characteristic relation between neighbor super-pixels. We can also reduce the computational complexity by detecting and reducing some low-strength connections. 2) We proposed an algorithm based on pairwise Markov random field (MRF) (Taniai et al., IEEE Trans Pattern Anal machine Intell 40(11): 2725-2739, 2017) and local greedy iteratively, which can be used to solve the energy model. 3) In post-optimation, we select some areas with severe occlusion and fewer matching clues for post-interpolation fitting to optimize the results. The experiment shows that the proposed method reduced the average percentage of bad pixels (in bad 3) to 6.06 on the Middlebury dataset and 1.42 on the KITTI dataset. Finally, we compare our results with those of MC-Cnn (Zbontar and LeCun 2015), CF-Net (Shen et al., 2021), Guided-Stereo (Poggi et al., 2019), Gwc-Net (Guo et al., 2019) and Patchmatch-Net(PM-Net) (Wang et al., 2021) to verify the improved speed and accuracy of our algorithm, especially at recognizing the depth of changing edges and small objects. This paper's relevant research can contribute to practical engineering practices such as assisted vision, i
The explosive growth of image data facilitates the fast development of imageprocessing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to processed images. This p...
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The explosive growth of image data facilitates the fast development of imageprocessing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to processed images. This poses a grand challenge to existing blind image quality assessment (BIQA) models, which are weak at adapting to subpopulation shift. Recent work suggests training BIQA methods on the combination of all available human-rated IQA datasets. However, this type of approach is not scalable to a large number of datasets and is cumbersome to incorporate a newly created dataset as well. In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data. We first identify five desiderata in the continual setting with three criteria to quantify the prediction accuracy, plasticity, and stability, respectively. We then propose a simple yet effective continual learning method for BIQA. Specifically, based on a shared backbone network, we add a prediction head for a new dataset and enforce a regularizer to allow all prediction heads to evolve with new data while being resistant to catastrophic forgetting of old data. We compute the overall quality score by a weighted summation of predictions from all heads. Extensive experiments demonstrate the promise of the proposed continual learning method in comparison to standard training techniques for BIQA, with and without experience replay. We made the code publicly available at https://***/zwx8981/BIQA_CL.
Writing in air has become a significant research area in imageprocessing and pattern recognition, contributing to automation and improving human-machine interfaces in various applications. Object tracking, a crucial ...
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In recent 10 years, deep learning has successfully shown its effectiveness in various computer vision fields such as autonomous vehicles, robotics, and AI surveillance. Numerous machinevision AI systems have been acc...
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Medicinal plants have long been the foundation of the medical system and a source of health and healing, but many people nowadays are unaware of these priceless natural resources or the range of possible applications ...
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This paper investigates the potential of advanced object detection technologies to automate and enhance the accuracy and efficiency of the vote counting process in democratic elections that utilize paper-based ballots...
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A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves imageprocessing, computer vision, and machine learning techniques. Deep learning has made si...
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The proceedings contain 127 papers. The topics discussed include: Advanced data storage and processing technologies in a next-generation electric information acquisition system;analyzing file access characteristics fo...
ISBN:
(纸本)9798350355253
The proceedings contain 127 papers. The topics discussed include: Advanced data storage and processing technologies in a next-generation electric information acquisition system;analyzing file access characteristics for deep learning workloads on mobile devices;optimal scheduling of distributed energy storage for electric vehicles based on evolutionary dissipation theory;a novel semi-supervised learning approach for referring expression comprehension;research and implementation of material image subject segmentation method based on machinevision;application of image recognition and 3D reconstruction technology in virtual museum system;knowledge graph technology-based active research and judgment technology for electric power customer complaint risk;and path planning for unmanned underwater vehicles based on improved ant colony algorithm.
A generic fundus foreground extractor is required for the standardization of fundus datasets in machine-learning applications due to the vast range of retinal fundus images. Some fundus images have a large amount of n...
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