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

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

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.

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

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

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

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

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.

关键词： Interpolation Computer vision Convolution High performance computing Interference Object detection Real-time systems

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Novel Regularization Method for Reduced Biquaternion Neural Network

SSRN

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SSRN 2023年

作者： Gai, Shan Huang, Xiang School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Nanchang330063 China

A reduced biquaternion neural network (RQNN) is a new type of neural network framework that has achieved significant success in machine learning. However, as the reduced biquaternion algebra system contains infinite zero divisors, the RQNN is easily trapped in a local minimum and overfitted. We propose a new regularization scheme for the RQNN to address these issues. First, we propose a new operation in the reduced biquaternion domain—the reduced biquaternion complex modulus (RQCM). Second, we analyse the algebraic properties and obtain the geometric meaning of the RQCM. Finally, we design an improved weight decay method using the RQCM. Unlike conventional weight decay methods with matrix norm constraints, the proposed method can better project the reduced biquaternion in terms of the scale and phase. Colour image classification and denoising tasks are used to demonstrate the superior performance of the RQCM over state-of-the-art weight decay methods. The source code can be found at https://***/tasteimage/Reg4RQNN . © 2023, The Authors. All rights reserved.

关键词： Complex networks

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SiamORPN: Enabling Orthogonality between Object and Background in Siamese Object Tracking

SiamORPN: Enabling Orthogonality between Object and Backgrou...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kai Huang Chaolin Pan Jun Chu Lu Leng Jun Miao Junjiang Wu Lingfeng Wang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Science and Technology Beijing University of Chemical Technology Beijing China

Siamese-based trackers currently are the dominant tracking paradigm due to the balance between speed and performance. However, it is prone to drift and tracking failure when the environment is complex and similar objects interfere. While the Siamese-based trackers perform the correlation operation, the responses of the target object and background appear in different channels, i.e., the feature spaces of the target object and background have some orthogonality. However, when meeting background clutters and similar objects interfere, this orthogonality becomes weaker and the wrong classification contribution of the object and the background reduces the stability of the learned similarity function, leading to many misclassified pixels in the heatmaps. In this work, we proposed a SiamORPN to solve the above issues. It is incorporated at two levels: an Orthogonal Region Proposal Network (ORPN) and an Adaptive Pixel-wise Aggregation (APA) module. Specifically, for ORPN, the orthogonality between the object and the background maximizes the inter-class inertia. Moreover, the ORPN introduces the orthogonal module to enhance this orthogonality. For APA, it introduces two lightweight networks to predict the weights of all pixels in different heatmaps and the weights of all pixels in different regression offsets. Experiments on challenging benchmarks, including OTB2015, VOT2016, VOT2018, GOT-10k test set, UAV123, LaSOT, and TrackingNet, demonstrate the proposed SiamORPN outperforms many SOTA trackers and achieves leading performance. The inference speed at GTX1080Ti can reach about 32 FPS, meeting the real-time requirements.

关键词： Heating systems Target tracking Correlation Adaptive systems Benchmark testing Real-time systems Proposals

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An Improvement to Genetic Algorithms for Multimodal Optimization in Noisy Environments: Re-evaluation of All Individuals per Generation

An Improvement to Genetic Algorithms for Multimodal Optimiza...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Junhua Li Peng Liu Linxia Zhou Ming Li Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hang Kong University Nanchang China School of Information Engineering Nanchang Hang Kong University Nanchang China

ISBN: (纸本)9781479969302

Optimization in noisy environments is regard as a favorite application domains of genetic algorithms. Different methods for reducing the influence of noise are presented and discussed. A new fitness evaluation method is proposed that reevaluates all survival individuals each generation. Compared with re-sampling and population sizing, the new evaluation approach shows higher probability of searching to the global extremum area and precision of convergence. These results demonstrate that the proposed method is effective for reducing noise effects.

关键词： Noise Noise measurement Genetic algorithms Optimization Sociology Statistics Convergence

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution image color analysis Convolution Brightness Lighting Information filters

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Abundant Semantic Information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Remote Sensing image Object Detection Method with Feature Denoising Fusion Module

Remote Sensing Image Object Detection Method with Feature De...

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

作者： Penghui Chen Qishen Li Qiufeng Li Zhongyu Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Remote sensing object detection is an important research area in computer vision, widely applied in both military and civilian domains. However, challenges in remote sensing image object detection such as large image sizes, complex backgrounds, and significant variations in target scales are prevalent. To address these issues, this paper proposes a new Feature Denoising and Fusion Module (FDFM) aimed at enhancing the accuracy and robustness of object detection. This module comprises a Multi-Scale Denoising Submodule(MDS) and an Attention Optimization Submodule(AOS). The Multi-Scale Denoising Module aims to suppress lower-level texture noise by utilizing higher-level semantic features before the fusion process, reducing the impact of lower-level noise on subsequent multi-scale feature fusion. Meanwhile, the Attention Optimization Module seeks to enhance the precision of self-attention computations within the Multi-Scale Denoising Module without increasing the parameter count. The efficacy of this method was evaluated on public datasets DOTA, VisDrone, VOC and COCO, showing improvements in comparison to baseline models.

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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A Dual-Camera Surveillance Video Summarization Generating Strategy for Multi-Target Capturing 19

A Dual-Camera Surveillance Video Summarization Generating St...

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Proceedings of the 3rd International Conference on Video and image processing

作者： Qingyun Shen Cihui Yang Shipin Wen Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781450376822

Traditional surveillance video contains a large amount of information which is too jumbled. Real-time video summarization can solve this problem but also face much challenges. Different from file summarization, real-time video summarization requires higher efficiency. Meanwhile, the validity and quality of a summarization should be ensured. To tackle these problems, we propose a real-time video summarization strategy based on dual-camera. In our strategy, a static camera and a PTZ camera are necessary. The static camera is used to monitor the scene to detect and track moving targets, and the PTZ camera is used to capture the close-up information of moving targets as video summarization, and the collaboration of these two cameras is crucial. Specifically, in order to obtain multi-target summarization efficiently and effectively, the priority of target capturing is determined by its spatial information and historical representation in the scene. Extensive experiments are performed on real-time outdoor scene with our method. Experimental results show that our proposed method is robust enough to capture multiple targets in the same scene at the same time.

关键词： Multi-target priority judgment Dual-camera coordination Real-time video summarization

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