KNN is an important part of classification algorithms in machine learning, which has been extensively employed in various fields. In this research, we proposed a Lp Norm-Based Local Means k-Nearest Neighbor Classifica...
KNN is an important part of classification algorithms in machine learning, which has been extensively employed in various fields. In this research, we proposed a Lp Norm-Based Local Means k-Nearest Neighbor Classification with Feature Reduction. We extend the original Euclidean distance formula, and obtain the distance calculation formula based on P-value. Then classify and predict the data set after dimension reduction. In the experiment, we verified the effectiveness and superiority of our method by setting different k values in KNN, LMKNN and the classification accuracy obtained by our methodology (PLMKNN).
In recent years, imageprocessing techniques have gained increased significance across a broad range of applications concerning computer technology. The digital image is processed by applying a set of algorithms this ...
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This paper uses a new visualization method. The goal is to improve the results of 3D image reconstruction. Firstly, the hardware architecture of the system is improved. The clock of the system is extended. The combina...
This paper uses a new visualization method. The goal is to improve the results of 3D image reconstruction. Firstly, the hardware architecture of the system is improved. The clock of the system is extended. The combination of CY25400 micro controller and CDCVF2510 micro controller greatly speeds up the detection speed. Then the key technologies such as image parsing module, image preprocessing module and 3D visualization module are studied. The visualization elements are fed back to the 3D image to realize virtual reconstruction. Finally, the conventional 3D imaging technology is used to complete the simulation and comparison test in the same scene. Experiments show that this method can obtain high 3D image reconstruction effect. The system can clearly show the edge area of the image, and can meet the requirements of users.
In the AI applications for natural language definitions, image captioning is a field that is expanding quickly. It attempts to capture meaningful interpretations of the interactions between the acquired picture data f...
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Sufficient training data play a crucial role in deep-learning techniques, however, available training samples are not always accessible for specific targets, such as satellites. Producing synthetic data through genera...
Sufficient training data play a crucial role in deep-learning techniques, however, available training samples are not always accessible for specific targets, such as satellites. Producing synthetic data through generative model is a major solution for the lack of training data. In this paper, we propose a Denoising Diffusion Implicit Model (DDIM) to generate the Inverse Synthetic Aperture Radar (ISAR) satellite images from the synthesized optical counterparts. Efficient and abbreviated feature extraction method is proposed in optical-ISAR image translation. A domain-cross generative model is then established with respect to the generative task and feature learning modules obtained from the translation model. Extensive experiments validate the feasibility and performance of the proposed approach, which outperforms the existing generative models such as CycleGAN in terms of inception score and structural similarity index.
Camouflage is a type of adaptive coloring in which the target blend into its environment making it harder for the predators to see it. It can be accomplished by color matching, disruptive patterns or a combination of ...
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Underwater image enhancement, as an important branch of imageprocessing, has attracted the attention of many scholars in recent years. Due to selective scattering and degradation of light in water, images captured un...
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Robust real-world image enhancement from multi-exposure low dynamic range (LDR) images is a challenging task due to the unexpected inconsistency among the input images, such as the large motion or various exposures. I...
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ISBN:
(纸本)9781665493468
Robust real-world image enhancement from multi-exposure low dynamic range (LDR) images is a challenging task due to the unexpected inconsistency among the input images, such as the large motion or various exposures. In this paper, we propose a novel end-to-end image enhancement network to solve this problem. After extracting contextual information from the LDR images, we design a novel matching volume to align them by considering the motion and exposure differences among the input images. A stacked hourglass with dilated convolution is further utilized to aggregate the matched feature maps to the final enhanced image. In addition, we design a weakly-supervised pairwise loss function to evaluate the color consistency in the enhanced image, which further boosts the performance. We show the effectiveness of our methods on high dynamic ranging imaging (HDR) and End-to-End image signal processing (E2E-ISP) tasks. Experimental results demonstrate that our model achieves state-of-the-art enhancement performance.
The fields of computer vision and imageprocessing, which deal with the analysis and modification of visual data like pictures and videos, are closely related. While computer vision focuses on tasks like object recogn...
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With the development of information technology, patents or long sentences of science and technology with huge vocabulary and complex structure have widely appeared in people's daily work. This kind of long sentenc...
With the development of information technology, patents or long sentences of science and technology with huge vocabulary and complex structure have widely appeared in people's daily work. This kind of long sentence often contains several clauses and non-predicate verb phrases, and these clauses and phrases often restrict and depend on each other, thus forming a complex language phenomenon in which there are phrases in clauses and clauses in phrases. This kind of long sentence plays a great role in the logic and rigor of English itself, but it brings considerable difficulties to machine translation. In order to solve this problem, this paper proposes a multilingual and general algorithm for complex long sentence translation based on multi-strategy analysis. The algorithm uses a combination of case-based pattern matching and rule analysis to comprehensively utilize a variety of relevant language features in the source language sentences, including grammatical semantic features, sentence length, punctuation marks Functional words and contextual conditions are used to simplify the segmentation of complex long sentences and compound the translation.
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