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Claim this profileWorks255 from public data
- An overview of multi-modal medical image fusion366
In this review, methods in the field of medical image fusion are characterized by image decomposition and image reconstruction, image fusion rules, image quality assessments, and experiments on the benchmark dataset.
- Shape-Adaptive Selection and Measurement for Oriented Object Detection309
Novel flexible shape-adaptive selection (SA-S) and shape- Adaptive measurement (SA -M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples are proposed.
- Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation240
Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets and performs competitively to the fully- supervised segmentation models.
- TransZero: Attribute-Guided Transformer for Zero-Shot Learning206
This paper proposes an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero, which achieves a new state-of-the-art on three ZSL benchmarks.
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- Single image dehazing with a physical model and dark channel prior171
The proposed single image dehazing method is based on a physical model and the dark channel prior principle and the selection of an atmospheric light value is directly responsible for the color authenticity and contrast of the resulting image.
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- A Real-Time Hand Posture Recognition System Using Deep Neural Networks148
This article proposes a two-stage HPR system for Sign Language Recognition using a Kinect sensor and applies deep neural networks (DNNs) to automatically learn features from hand posture images that are insensitive to movement, scaling, and rotation.
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- A novel approach of lung segmentation on chest CT images using graph cuts114
A new algorithm based on an improved graph cuts algorithm with Gaussian mixture models (GMMs) that can directly provide explicit lung regions without any postprocessing operations even in complex scenarios is proposed.
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- An improved fractional-order differentiation model for image denoising113
An improved model based on the Grunwald-Letnikov (G-L) fractional differential operator based on G-L fractional order differentiation is proposed for image denoising and successfully used to denoise three-dimensional magnetic resonance images.
- Research on manufacturing grid resource service optimal-selection and composition framework104
The case study and comparison of performances of the algorithms demonstrate that the proposed methods are sound on success rate and executing efficiency.
- G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection99
A unified Gaussian representation called G-Rep is proposed to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems and shows excellent performance on several publicly available datasets.
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- Performance evaluation of deep feature learning for RGB-D image/video classification94
This paper conducts extensive experiments on five popular RGB-D datasets and presents a detailed analysis about the comparison between the learned feature representations from the four deep learning models.
- Particle Swarm Optimization based dictionary learning for remote sensing big data93
This paper proposes a method of improved online dictionary learning based on Particle Swarm Optimization (PSO), which improves the performance of the algorithm for large-scale remote sensing images, and has a better effect on noise suppression.
- Robust object removal with an exemplar-based image inpainting approach91
In the improved model, a regularized factor is introduced to adjust the patch priority function and a modified sum of squared differences (SSD) and normalized cross correlation are combined to search for the best matching patch.
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- IK-SVD: Dictionary Learning for Spatial Big Data via Incremental Atom Update82
The information, from both old and new samples, is explored in the proposed incremental K-SVD (IK-SVD) algorithm, but only the current atoms are adaptively updated, which makes the dictionary better represent all the samples without the influence of redundant information from old samples.
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- Multiview Hessian regularized logistic regression for action recognition78
This paper carefully conduct extensive experiments on the unstructured social activity attribute (USAA) dataset and the experimental results demonstrate the effectiveness of the proposed multiview Hessian regularized logistic regression for human action recognition.
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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 2026-10-14. 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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