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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Gao, Jun Huang, Hua Li, Qishen Xia, Shaoqi School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network. © 2022 IEEE.

关键词： attention mechanism Human Pose Estimation Keypoint detection

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Research on Monocular Depth Estimation Method based on Multi-Level Attention and Feature Fusion 7

Research on Monocular Depth Estimation Method based on Multi...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Wu, Zhongyu Huang, Hua Li, Qishen Chen, Penghui Nanchang Hangkong University School of Information Engineering Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China Nanchang Hangkong University School of Software Jiangxi Nanchang China

ISBN: (纸本)9798350339161

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, attention-based models and encoder-decoder architectures have led to great improvements in monocular depth estimation. Typically, most of the previous methods used repeated simple up-sampling operations during decoding, which may not make full use of the potential properties of the features extracted by the encoder, and there are problems of inaccurate prediction of the edge and depth maximum region. We propose an attention-based feature fusion module for encoder and decoder. We treat the monocular depth estimation as a pixel-level optimization problem, where the coarsest encoder feature is used to initialize the pixel-level optimization, which is then refined to higher resolution by the proposed attentional feature fusion (AFF). We formulate the prediction problem as ordinal regression over the bin centers that discretize the continuous depth range. It predicts a correspondingly different distribution of bins based on different pictures and we predict bins at the coarsest level using global pooling and MLP layers. In the NYUV2 dataset, the proposed architecture improving original model by 2.5.% and 1.1%, in terms of Log10 and Absolute relative error, respectively. © 2024 IEEE.

关键词： Decoding

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Gravel image Segmentation in Noisy Background Based on Partial Entropy Method

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中国地质大学学报(英文版) 2000年第1期11卷 92-94页

作者： Wang Guangjun Tian Jinwen Chen Zhenyu Liu Jian Wu Guoping State Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology WuHan 430074 Faculty of Information Engineering China University of Geosciences Wuhan 430074

Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult o segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fur calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.

关键词： partial entropy segmentation mathematical morphology gravel threshold filter

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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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2nd International Conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： Computer vision

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Iris recognition using Steerable Pyramids

Iris Recognition using Steerable Pyramids

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Nefissa Khiari Hela Mahersia Kamel Hamrouni Laboratory of Signal Processing Image Processing and Pattern Recognition National Engineering School of Tunis University of Tunis Tunis Tunisia

This work presents a new iris recognition method based on steerable pyramid transform. This method consists of four steps: localization, normalization, features extraction and matching. After locating the iris boundaries by Hough Transform, normalization is operated by unwrapping the circular ring and isolating the noisy regions. Steerable pyramid filters are then used to capture orientation details from the iris texture. The features are extracted on each filtered sub-image to form a fixed length feature vector which will be compared to other vectors in the matching step. This technique has been tested on infrared light iris images. It has been compared, in both identification and verification modes, to known methods.

关键词： Iris recognition Feature extraction Wavelet transforms image processing Matched filters Gabor filters Filtering Biometrics Eyelids Eyelashes

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ArtDiff: Artwork Generation via Conditional Diffusion Models 23

ArtDiff: Artwork Generation via Conditional Diffusion Models

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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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Generative Zero-shot image Classification based on Generative Feature Constraint 10

Generative Zero-shot Image Classification based on Generativ...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Yu, Xiao Li, Qishen Huang, Hua Li, Qiufeng School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China Jiangxi China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Zero-shot learning (ZSL) utilizes semantic information that is auxiliary information to transfer knowledge from seen classes to unseen classes, thereby realizing the recognition of unseen classes. The generative methods train the model to generate pseudo image features for the unseen classes, thus transforming ZSL into a supervised learning problem. However, the classification performances of these methods still suffer from weak generalization. To address this issue, the paper proposes a novel Generative Adversarial Network based on Generative Feature Constraint (GFCGAN). First, based on the LsrGAN [1], a semantic constraint module is added to restrict the generated image features by keeping the semantic information consistent. Then, in the case of keeping the semantic relationships between classes consistent, the generated image features of unseen classes are farther away from the feature prototypes of their most similar classes in both semantic and feature space so as to train the generative model and generate high-quality pseudo image features. Finally, the trained classifier is used to classify images of unseen classes. Experiments on standard datasets verify the effectiveness of our model and achieve good results. © 2022 IEEE.

关键词： Zero-shot learning

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Statistical image Upsampling Method Based on CUDA

Statistical Image Upsampling Method Based on CUDA

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International Conference on Computational Intelligence and Security

作者： Xin Zheng Qingqing Xu Peipei Pan Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing P.R. China

In many application fields, an appropriate high-quality fast image upsampling method is required. Although many interpolation-based upsampling methods have been proposed, the quality of result images is not satisfactory. Some of them are very fast, but produce poor quality images, the others can produce high quality images, but the methods in them are slow. In our paper, we proposed a fast statistical image upsampling method based on CUDA, it can obtain high quality images based on reducing the input resolution-grids dependency artifacts. Thus, we can rebuild low resolution images' sharp edges fast and get high-quality upsampled images in real time. We have applied this method in the multi-resolution texture generation of large scale terrain rendering. Experiments prove that our method can receive ideal effects in real time.

关键词： Graphics processing units Mathematical model image edge detection image resolution Markov processes Standards Computational modeling

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image Completion with Automatic Structure Propagation

Image Completion with Automatic Structure Propagation

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International Conference on Computational Intelligence and Security

作者： Peipei Pan Xin Zheng Qingqing Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing P.R. China

In this paper, we propose a novel approach for image completion with automatic structure propagation. This method integrates two stages: Firstly, it extends the salient structure lines from the known regions to the unknown by following a local self-similarity assumption on natural images. Then guided by the structure information, it restores the missing region by patch-based texture synthesis. Experiment results demonstrate a better effect of our method than that of the previous patch-based texture synthesis image completion algorithm.

关键词： image edge detection image segmentation image reconstruction image resolution Filling image restoration Sun

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Object Detector based on Enhanced Multi-scale Feature Fusion Pyramid Network

Object Detector based on Enhanced Multi-scale Feature Fusion...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Luan Zhao Xiaofeng Zhang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and detection of multi-scale objects in the object detection task well, it still has some limitations. Since the feature information of different levels is often not from the same layer of the network, it is difficult to obtain the feature of different objects information at a certain scale from a certain level feature map of the pyramid network. To solve this problem, we present a novel object detection architecture, named Enhanced Multi-scale Feature Fusion Pyramid Network (EMFFPNet). Our network consists of Enhanced Multi-scale Feature Fusion Module (EMFFM) and Predictor Optimization Module (POM). In EMFFM, Features at different levels can be fused into the Enhanced features as outputs, which are more representative and deterministic. In order to enable the enhanced features to play their respective roles in the pyramid network, we assign different weights to fusion features of different levels in POM. We perform the experiments on the COCO detection benchmark. The experimental results indicate that the performance of our model is much better than the state-of-the-art model.

关键词： Object detection Predictive models Feature extraction Task analysis Information technology Optimization Standards

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