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检索条件"机构=Key Laboratory of Jiangxi Provincial for Image Processing and Pattern Recognition"

共 80 条记录，以下是11-20 订阅

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Object detection algorithm based on mixed dilated convolution pyramid 3

Object detection algorithm based on mixed dilated convolutio...

引用

3rd International Conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Yu, Tingjian Yuan, Zemin Huang, Tao Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

To address the problem that FPN in the one-stage object detection algorithm loses a large amount of semantic information after upsampling and downsampling fusion, leading to difficulties in detecting small objects in dense scenes. This paper proposes a new FPN structure, Residual dilated FPN(RdFPN), compared to the traditional FPN, which uses multiple Dilated Block instead of the standard convolution in the FPN horizontal structure to mix the receptive fields of different layers in the feature map and expand the object size range of the feature map, while improving the network's ability to extract features at different scales. Experimental results show that RdFPN improves the mAP of the one-stage object detection algorithms RetinaNet, FCOS, and GFL by 0.9%, 0.7%, and 0.8% on the MS COCO dataset, enough to improve the overall performance of the detector. © 2023 SPIE.

关键词： Object detection

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Dense Pedestrian Detection Based on Multiple Link Feature Pyramid Networks 3

Dense Pedestrian Detection Based on Multiple Link Feature Py...

引用

3rd International Conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Huang, Tao Yang, Siyu Yu, Tingjian Fu, Xiang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Software Nanchang Hangkong University Nanchang China

ISBN: (纸本)9781510667563

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR-2, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates. © 2023 SPIE.

关键词： Feature extraction

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OCF-Layout2Img: Object-Wise Constraint Free Layout to image Generation 23

OCF-Layout2Img: Object-Wise Constraint Free Layout to Image ...

引用

15th International Conference on Digital image processing, ICDIP 2023

作者： Lin, Xiao Liu, Bin He, Zhifen Wang, Kang Li, Bo School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center China

ISBN: (纸本)9798400708237

Complex scene generation is an important and challenging image synthesis task. Though latent space based conditional generative methods get impressive results, the accurate locating of objects for more detailed situation and editable contiguous synthesis are still a snag. In addition, previous methods with spatial awareness are easy to lose the location information after a series fusion operation. Focusing on such kind of problems, in this paper we propose an object-wise constraint free and location-lossless way of layout embedding by combining VAE and recent high performance VQGAN to achieve a location accurate and stable synthesis for layouts of complex scene. Instead of conditioning on layout, we apply a pretrained VQGAN to define the latent space and project every region of layout to image latent space directly by aligning their distribution with a novel light Cross-VAE structure. Then the quantization and decoder of VQGAN are reused for synthesis. This VAE plugged into VQGAN framework, which is called Layout-DVGAN in this paper. Experimental results demonstrate our method has competitive performance compared with the approaches which applied explicit spatial awareness way. © 2023 ACM.

关键词： Location

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GFENet: Enabling Attention and Adaptive Feature Fusion in Real-time Object Detection 2

GFENet: Enabling Attention and Adaptive Feature Fusion in Re...

引用

2nd International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Pan, Chaolin Zhu, Xiaoyang Chu, Jun Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Real-time object detection has been a critical component of onboard instrumentation for next-generation autonomous driving. To enable safety and reliability in autonomous driving systems, we must continue advancing its real-time object detection accuracy for future autonomous driving systems in complex environments. Existing works in real-time object detection cannot accurately detect objects with similar appearances and in complex environments. In particular, existing approaches missed many essential features because of the loss of multi-scale contextual features. Therefore, they cannot differentiate objects that have a similar appearance, a low-resolution, and a small size. To solve these issues, and to advance real-time object detection accuracy, we develop GFENet (Global Feature Enhancement Network), an end-to-end real-time object detection framework. First, we introduce a convolutional attention module that integrates spatial and channel-wise features and eliminates ineffective features to enable a strong feature representation of the network, followed by a lightweight residual module that further advances the feature representation by highlighting salient changes in features. Second, we build an adaptive feature fusion module to adaptively learn weights of multi-scale contextual features across layers, aiming to strengthen weights for small-size objects. We comprehensively evaluate GFENet on two leading object detection datasets-PASCAL VOC and MS COCO. The evaluation results (mAP of 82.9 and FPS of 31 on PASCAL VOC;mAP of 39.0 and FPS of 23 on MS COCO) show the outperformance of GFENet compared to competing baseline methods. © 2022 IEEE.

关键词： Instruments Object detection Detectors Feature extraction Real-time systems Safety Reliability

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

引用

15th International Conference on Digital image processing, ICDIP 2023

作者： Liu, Wenhui Liu, Bin Lin, Xiao Li, Bo He, Zhifen Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang China Department of Supercomputing Management Jiangxi Science and Technology Infrastructure Center Jiangxi Nanchang China

ISBN: (纸本)9798400708237

Artwork Generation is an important research area of computer vision. Recently, kinds of generative models have achieved great success in natural image generation. However, artwork generation has rarely been studied due to the unfixed structure of artworks. Combined with prevailing diffusion models, we propose a simple yet effective framework, named as ArtDiff, for artwork generation. Given a name of an artist, we can generate diverse and novel artworks which reflecting the style of the artist. To the best of our knowledge, ArtDiff is the first diffusion model that generate artworks with the guidance of artist's name. Experimental results demonstrate that ArtDiff is able to recognize the artist's preference and generate artworks with reasonable structures and fine-grained details. © 2023 ACM.

关键词： Diffusion

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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

引用

15th International Conference on Digital image processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. The proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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MixerSR: A New Feature Extraction Paradigm for Single image Super-Resolution 23

MixerSR: A New Feature Extraction Paradigm for Single Image ...

引用

15th International Conference on Digital image processing, ICDIP 2023

作者： Ding, Hui Chen, Weifeng He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center and Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Single-image super-resolution (SR) tasks have achieved fancy success in recent years by leveraging deep convolution neural network (CNN). Although CNNs obtain powerful representation capabilities of reconstructing a high-resolution (HR) image from its corresponding low-resolution (LR) observation, it is hard to apply these accomplishments to real world for its large number of parameters that bring about huge computing resource cost. To solve this problem, many researchers turn their directions to lightening the network by delicate designs while keeping the performance in the high level. In this paper, we propose a new method to further reduce the parameters by using the new feature extraction paradigm called Mixer which only contains Multi-Layer Perceptron (MLP). Compared to convolutional operations, Mixer operation has the same representation capabilities with fewer parameters. In this paper, three typical lightweight networks, where we replace the convolutional operation with Mixer, are used to show the excellent ability. Our experimental results demonstrate that Mixer can help these lightweight networks further reduce the number of parameters by up to 38% while keep their performance in the same level. © 2023 ACM.

关键词： Feature extraction

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Cross-GAN: Unsupervised image-to-image Translation 6

Cross-GAN: Unsupervised Image-to-Image Translation

引用

6th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2022

作者： Peng, Xu Li, Qishen Wu, Taida Yuan, Sihao Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9781665431859

In recent years, unsupervised image-to-image translation is a topic in computer graphics. This paper proposes a new architecture to translate the image through the idea of sharing and decoupling, termed as Cross-GAN, which can better extract the style in the process of translation, and does not change the original outline and background. We improve the cycle consistency loss and reconstruction loss for the unique properties of the model to make the image more realistic. Because GAN[1] only generates the target style image by inputting the content image, and the information of the target domain image is not used in the training process. We input style images and content images through cross-input. The style images play a guiding role in the generator, and the effect of image are better by fusing style features and content features. Due to the fact that the image of the target domain is input, it is easy to produce the phenomenon of over-fitting. In order to prevent over-fitting training, we improve the cycle consistency loss and reuse the input encoder to make the model more compact. © 2022 IEEE.

关键词： image enhancement

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Investigation of electroencephalogram-based emotion recognition and channel importance analysis for dual-induction patterns

Investigation of electroencephalogram-based emotion recognit...

引用

2024 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Li, Zhengxiu Liao, Yijie Li, Zebin Chen, Hao Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China School of Information Engineering Nanchang Hangkong University Nanchang 330063 China

ISBN: (纸本)9781510689275

Both external stimulus and self-induction can evoke human emotions. However, most current research on emotion recognition by Electroencephalogram (EEG) signals focuses only on external stimuli. It was found that ignoring the differences between induced patterns results in lower emotion recognition accuracy. To overcome this problem, we construct a hierarchical emotion recognition model, which first determines whether emotions originate from external stimulus induction or self-induction and then classifies specific emotion categories. Meanwhile, we systematically evaluate the performance of multiple feature types, feature selection, and channel reduction schemes in identifying induction methods and emotional categories. Notably, we make channel importance analysis operators to investigate the distribution of electrodes associated with different induction patterns. Our model works on a dataset that simulates real induced-emotion scenarios. The experimental results show for the first time that the features extracted in the time domain can most effectively discriminate between the induction modalities. And the hierarchical emotion recognition model with feature selection and channel importance analysis operators achieves the best accuracy of 88.53%, which is 14.69% higher than a baseline model. More critically, channel importance analysis indicates that both induction modalities display similar electrode distributions, with EEG signals from temporal and prefrontal electrodes considered to be highly correlated with emotions;stimulus-induced emotion showing an apparent lateralization effect with higher importance of the left side of the brain electrodes, while self-induction is equally distributed on the left and right sides. © 2025 SPIE.

关键词： Channel importance analysis Electroencephalogram Emotion induction method Emotion recognition Self-induction

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A multi-feature fusion approach for electroencephalogram personality analysis

A multi-feature fusion approach for electroencephalogram per...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Liao, Yijie Li, Zhengxiu Jie, Lilin Cheng, Ruipeng Li, Ming Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China School of Information Engineering Nanchang Hangkong University Nanchang 330063 China

ISBN: (纸本)9781510689275

Electroencephalogram(EEG) signals have the potential to objectively analyze personality, but most of the existing personality analysis studies use a single EEG feature. In order to improve the accuracy of personality analysis, this paper proposes an EEG multi-feature fusion method for personality analysis. Firstly, a personality test paradigm which covers three emotional stimuli was designed, and the EEG data of 22 subjects were acquired;the preprocessed EEG data were extracted to contain multiple EEG features in the time domain, frequency domain, time-frequency domain, and nonlinear features. Then a multi-layer perceptual machine and sparse linear discriminant analysis were employed to integrate and reduce the extracted multi-electroencephalographic features, and the classification task was carried out using the deep learning network CNN-Transformer. Compared with other methods, the method proposed in this paper has an improvement in the analysis accuracy of the five personality dimensions of extraversion, agreeableness, conscientiousness, neuroticism, and openness by 4.6%-23.91%, 8.98%-34.5%, 7.89%-34.5%, 5.5%-30.5%, and 2.88%-22.88% respectively. © 2025 SPIE.

关键词： CNN-Transformer Electroencephalogram(EEG) feature fusion Feature Reduction personality analysis

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