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4th International Conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Huang, Bin Wang, Sanqian Li, Linjie 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)9781510682528

Considering the challenges associated with robots in optoelectronic imaging applications, typically require real-time and accurate recognition and localization of targets, especially in complex environments. Due to the potential challenges of noise, blurring, or dense targets in the images obtained by optoelectronic imaging systems, traditional object detection algorithms may face issues such as semantic information loss and difficulty in detecting small targets. By introducing residual deformable convolution structures, feature representation can be enhanced in optoelectronic imaging systems, and the shape of the convolution kernel can be dynamically adjusted to adapt to the non rigid deformation of the target, thereby better capturing the detailed features of small targets. Experimental results exhibit that RAFPN enhances the mAP of object detection algorithms RetinaNet, Faster R-CNN, and GFL by 1.2%, 0.9%, and 1.1% respectively on the MS COCO dataset, effectively enhancing the performance of object detection algorithms in optoelectronic imaging. © 2024 SPIE.

关键词： Optoelectronic devices

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Adaptive multi-feature tracking in particle swarm optimization based particle filter framework

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Journal of Systems Engineering and Electronics 2012年第5期23卷 775-783页

作者： Miaohui Zhang Ming Xin Jie Yang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 P. R. China Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475001 P. R. China

This paper proposes a particle swarm optimization（PSO） based particle filter（PF） tracking framework,the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage,and simultaneously incorporates the newest observations into the proposal distribution in the update *** the proposed approach,likelihood measure functions involving multiple features are presented to enhance the performance of model ***,the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking *** are three main ***,the PSO algorithm is fused into the PF framework,which can efficiently alleviate the particles degeneracy ***,an effective convergence criterion for the PSO algorithm is explored,which can avoid particles getting stuck in local minima and maintain a greater particle ***,a multi-feature weight self-adjusting strategy is proposed,which can significantly improve the tracking robustness and *** performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.

关键词： particle filter particle swarm optimization adaptive weight adjustment visual tracking

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Dynamic Action recognition under Simulated Prosthetic Vision

Dynamic Action Recognition under Simulated Prosthetic Vision

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Zhao, Ying Xu, Dantong Wang, Tie Ren, Yanchun Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia

ISBN: (纸本)9781728189543

Purpose. To assess the recognition pattern of dynamic action by visual prosthesis' wearers. Methods. Twenty volunteers (classified by gender and experience) were recruited to carried out action recognition test in simulated prosthetic vision. Twenty-three common dynamic actions video clips usually see in daily life were selected and formed the experimental action set which was divided into three parts including combined actions, simple actions and difficult actions in turn. The action animations made by 3D Studio Max were recorded by Banicam to convert to MP4 video format and processed to phosphene video clips by MATLAB in different resolutions (16 × 16, 24 × 24, 32 × 32, 48 × 48, 64 × 64, 128 × 128). Result. Comparing the recognition results, male group and female group had no significant difference. 48 × 48 resolution was the considerable latent capacity which subjects had stable performance. After a period of study, the performance of prosthesis wearers could be improved significantly. © 2020 IEEE.

关键词： MATLAB

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A new theoretical framework for the steinbuch's lernmatrix

A new theoretical framework for the steinbuch's lernmatrix

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Mathematical Methods in pattern and image Analysis

作者： Sánchez-Garfias, F.A. Díaz-De-León S, J.L. Yáñez-Márquez, C. Pattern Recognition and Image Processing Laboratory Center for Computing Research I. P. N. 07738 Mexico D. F.

ISBN: (纸本)0819459216

This article introduces a new theoretical framework to describe the behavior of the Steinbuch's Lernmatrix. The properties of this old associative memory can be modeled using set theory and order relationships, analogously to morphological associative memories. The obtained results allow the Lernmatrix, four decades before its creation, to be a good alternative for pattern classification and recognition.

关键词： Matrix algebra

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MambaMIM: Pre-training Mamba with state space token interpolation and its application to medical image segmentation

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Medical image Analysis 2025年 103卷 103606页

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Jiang, Zihang Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Suzhou Institute for Advanced Research USTC Jiangsu Suzhou215123 China Department of Automation Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology Jiangsu Suzhou215123 China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM. © 2025

关键词： image segmentation

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Comparative studies of feature extraction methods with application to face recognition

Comparative studies of feature extraction methods with appli...

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2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Jiang, Yunfei Guo, Ping Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)1424409918

In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones. © 2007 IEEE.

关键词： Feature extraction

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Texture Segmentation Based on image Model and Neural Network

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Journal of Earth Science 1999年第4期18卷 333-335页

作者： Chen Zhenyu Sheng Wen Wang Guangjun Li Dehua(State Education Commission laboratory for image processing and Intelligence Control, Institute for pattern recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wu han 430074) State Education Commission Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhan 430074

This paper presents a texture segmentation approach which is based on the Markov random field model (MRF) and feed forward neural *** texture is modeled by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To perform texture segmentation, we introduced an improved BP algorithm to get faster learning speed. Experiment shows that better segmentation results can be obtained than the traditional Euclidean distance method.

关键词： texture analysis Markov random field neural network

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High performance word level sequential and parallel coding methods and architectures for bit plane coding

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Science in China(Series F) 2008年第4期51卷 337-351页

作者： XIONG ChengYi TIAN JinWen LIU Jian College of Electronic Information Engineering South-Central University for Nationalities Wuhan 430074 China Institute of Pattern Recognition & Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan 430074 China

This paper introduced a novel high performance algorithm and VLSI architectures for achieving bit plane coding （BPC） in word level sequential and parallel mode. The proposed BPC algorithm adopts the techniques of coding pass prediction and parallel ＆ pipeline to reduce the number of accessing memory and to increase the ability of concurrently processing of the system, where all the coefficient bits of a code block could be coded by only one scan. A new parallel bit plane architecture （PA） was proposed to achieve word-level sequential coding. Moreover, an efficient high-speed architecture （HA） was presented to achieve multi-word parallel coding. Compared to the state of the art, the proposed PA could reduce the hardware cost more efficiently, though the throughput retains one coefficient coded per clock. While the proposed HA could perform coding for 4 coefficients belonging to a stripe column at one intra-clock cycle, so that coding for an NxN code-block could be completed in approximate N2/4 intra-clock cycles. Theoretical analysis and experimental results demonstrate that the proposed designs have high throughput rate with good performance in terms of speedup to cost, which can be good alternatives for low power applications.

关键词： bit plane coding high performance word-level sequential multi-word parallel

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Fast DCT-I, DCT-III, and DCT-IV via Moments

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Eurasip Journal on Applied Signal processing 2005年第12期2005卷 1902-1909页

作者： Liu, J.G. Liu, Y.Z. Wang, G.Y. Key Laboratory of Education Huazhong University of Science and Technology Ministry for Image Processing and Intelligence Control Wuhan 430074 China Department of Computer Science and Engineering China University of Geosciences Wuhan 430074 China Key Laboratory of State Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

This paper presents a novel approach to compute DCT-I, DCT-III, and DCT-IV. By using a modular mapping and truncating, DCTs are approximated by linear sums of discrete moments computed fast only through additions. This enables us to use computational techniques developed for computing moments to compute DCTs efficiently. We demonstrate this by applying our earlier systolic solution to this problem. The method can also be applied to multidimensional DCTs as well as their inverses. © 2005 Hindawi Publishing Corporation.

关键词： Method of moments

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Face recognition under variable lighting using local qualitative representations

Face recognition under variable lighting using local qualita...

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MIPPR 2007: pattern recognition and Computer Vision

作者： Zhang, Yi Chu, Ying Mou, Xingang Zhang, Guilin State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819469526

In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for face recognition based on Local Binary pattern descriptor, which extracts the local variance features of an image. For the 'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial database B when all images in subset 1 are used as gallery.

关键词： Face recognition

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