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- A Survey of Learning-Based Intelligent Optimization Algorithms251
A comprehensive survey of LIOAs is conducted in this paper, which includes statistical analysis about LIOA, classification of L IOA learning method, application of LioAs in complex optimization scenarios, and L IOAs in engineering applications.
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- Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles70
The results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development, and hold promise for application in the field for disease diagnosis and management.
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- Robust hand tracking via novel multi-cue integration30
The experimental results show that the integration algorithm outperforms tracking algorithms that only use a single cue, and the proposed tracking algorithm is more robust in complex environments than other state-of-art algorithms.
- Computationally evaluating and synthesizing Chinese calligraphy27
The experiments demonstrate that the approach can obtain a similar topological style Chinese calligraphy with training samples and hypothesis testing and the decay function of transformation amplitude to improve the converge speed.
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- Optical Fourier convolutional neural network with high efficiency in image classification20
This study fully leverages the coherence of optical systems and introduces an optical Fourier convolutional neural network based on the diffraction of complex image light fields, which is not only structurally simple and fast in computation but also excels in image classification accuracy.
- Camera Sensor Activation Scheme for Target Tracking in Wireless Visual Sensor Networks20
Experimental results show that the proposed observation correlation coefficient can model the correlation characteristics of visual information in WVSN and simulations shows that the correlation-based camera activation scheme has satisfied performance in target tracking compared with other sensor selection methods.
- Wavelet-based network for high dynamic range imaging19
A novel frequency-guided end-to-end deep neural network (FHDRNet) is proposed to conduct HDR fusion in the frequency domain, and Discrete Wavelet Transform (DWT) is used to decompose inputs into different frequency bands.
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- Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks16
Simulation results verify that, compared with other algorithms, the proposed hybrid wireless multimedia sensors networks composed of acoustic and image sensors networks can reduce the energy consumption and improve tracking accuracy.
- ISPDiffuser: Learning RAW-to-sRGB Mappings with Texture-Aware Diffusion Models and Histogram-Guided Color Consistency15
A texture-aware diffusion model that leverages the generative ability of diffusion models to focus on local detail recovery is proposed, and a texture enrichment loss is further proposed to prompt the diffusion model to generate more intricate texture details.
- Human Activity Recognition using Deep Neural Network with Contextual Information15
This paper proposes context features along with a deep model to recognize the individual subject activity in the videos of real-world scenes and designs a deep neural network structure to obtain the high-level representation of human activity combining both motion features and context features.
- Using Granule to Search Privacy Preserving Voice in Home IoT Systems14
A novel personalized search scheme of encrypting voice with privacy-preserving by the granule computing technique is proposed, designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granules of the voice.
- Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism13
The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved, and it also has higher robustness in different scenarios.
- An adaptable active contour model for medical image segmentation based on region and edge information13
Experimental results show that compared with the traditional active contour models, the proposed method can achieve an satisfactory both in segmentation speed and accuracy.
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- Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition11
A frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the baseclassifiers.
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- Design and Implementation of Equipment Maintenance Predictive Model Based on Machine Learning6
The service life of equipment is taken as the research object, machine learning theory is applied, the equipment maintenance prediction model based on random forest regression is built, and the typical technology implementation and verification of the model is given, which has good adaptability and reference value.
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- Scalable Visual Instance Mining with Instance Graph3
This paper proposes a scalable mining method by leveraging the graph structure with images as vertices, capable of mining challenging small visual instances with diverse variations and outperformed the state of the arts on both datasets with higher recalls.
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- LAM-YOLOv10: lightweight and multiscale feature enhancement for paint surface defect detection model2
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- LUAI Challenge 2021 on Learning to Understand Aerial Images1
The results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV’2021, which focuses on object detection and semantic segmentation in aerial images, receive a total of 146 registrations on the three tasks.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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