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arXiv 2024年

作者： Guo, Xiao-Yu Li, Yi-Fan Liu, Yuan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained framework capable of designing proteins from any descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages an innovative multi-modal cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Experimental results demonstrate that ProtDAT achieves the state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. On 20,000 text-sequence pairs from Swiss-Prot, it improves pLDDT by 6%, TM-score by 0.26, and reduces RMSD by 1.2 Å, highlighting its potential to advance protein design. © 2024, CC BY-NC-SA.

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A robust approach to the detection and tracking of small targets with low contrast

A robust approach to the detection and tracking of small tar...

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IEEE International Workshop on VLSI Design and Video Technology, IWVDVT

作者： Xinyu Wang Guilin Zhang Ying Chu State Key Laboratory for Image Processing and lntelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780390059

An effective approach to the detection and tracking of small moving targets with low contrast is proposed. In our application, small moving targets are detected and tracked in a low quality video sequence captured from an airborne platform. The detection and tracking system includes three modules. First, the global motion parameters are estimated using fast least trimmed squares (FAST-ITS) regression and hierarchical processing. Once the global motion is robustly estimated, relatively stationary background can be eliminated almost completely through image warping. Then a motion-based fast target detector selects candidate pixels of the moving targets with motions statistically different from that of the background, and a particle filtering tracker examines the temporal consistency of targets to identify them and update their states. Information in the system flows in a closed loop form, in which the tracker instructs the detector where to look for a target, and the detector returns what it has found. Experimental results prove that the proposed method can reliably and effectively detect and track the real small moving target in real-time even if there is strong clutter influence.

关键词： Robustness Target tracking Detectors Motion estimation Video sequences Parameter estimation Motion detection Filtering Particle tracking Tracking loops

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An improved image segmentation algorithm based on MRF and Sobel operator 12

An improved image segmentation algorithm based on MRF and So...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Gao, Yao Tian, keyun Zhu, Cheng Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In response to the issues of over-segmentation, excessive noise, and suboptimal segmentation results commonly encountered in existing image segmentation algorithms based on Markov Random Fields (MRF), this paper proposes an enhanced image segmentation algorithm that integrates MRF with the Sobel operator. The algorithm begins by performing an initial segmentation of the image using a Markov Random Field (MRF)-based method. Subsequently, an enhanced Sobel operator is employed to eliminate noise points and extract fine edge details from the image. Finally, the segmentation result is refined through pixel-wise operations with the edge detection result, resulting in the ultimate segmentation output. The evaluation of segmentation performance is conducted using the Dice coefficient and Mean Hausdorff Distance as assessment metrics. Through experimental analysis, the method in this paper can improve the segmentation effect of the traditional MRF segmentation algorithm, and has better performance and higher adaptivity. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image segmentation

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Photovoltaic panel string detection based on prior knowledge and feature learning 12

Photovoltaic panel string detection based on prior knowledge...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Li, Bolin Cheng, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

image-based photovoltaic panel inspection has become one of the important tasks of photovoltaic power generation. Due to the repeated patterns of photovoltaic string numbers and different specifications, it is easy to cause false detection by traditional template matching or model training methods. In order to solve this problem, this paper proposes a photovoltaic panel string detection method based on prior knowledge and feature learning. First, the infrared inspection image is segmented for the first time by using the difference in radiation characteristics between the photovoltaic string and the environmental background. Subsequently, according to the local features of the border around the PV string template image, feature matching is performed in the segmented foreground area, and then determine the exact position of the string through the closure and integrity of the matching area. Experiments show that this method has high detection accuracy and has good adaptability to complex and diverse string specifications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Inspection

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Multi-Task Learning with Deep Dual-Path Network for Facial Attribute recognition 2020

Multi-Task Learning with Deep Dual-Path Network for Facial A...

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Proceedings of the 2020 9th International Conference on Computing and pattern recognition

作者： Xinyu Lai Si Chen Da-Han Wang Shunzhi Zhu School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods.

关键词： multi-task learning dual-path network Facial attribute recognition attribute groups

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High Performance Reconfigurable Hardware System for real-time image processing

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Journal of Systems Engineering and Electronics 2005年第3期000卷 502-509页

作者： Zhao Guangzhou Zhang Tianxu Wang Yuehuan Cao Zhiguo Zuo Zhengrong State Key Laboratory for Image Processing and Intelligent Control，Inst.for Pattern Recognition and Artificial Intelligence，Huazhong Univ.of Science and Technology，Wuhan 430074，P.R.China

A novel reconfigurable hardware system which uses both multi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-DSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports & serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems (RTOS) by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance *** it is suitable for real-time image processing and can get satisfactory effect of image recognition.

关键词： rmulti-DSP field programmable gate arrays real-time image processing real time operating systems paralld structure

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Defect detection of photovoltaic panel based on morphological segmentation 12

Defect detection of photovoltaic panel based on morphologica...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Cheng, Bolin Li, Bolin Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

The automatic inspection of photovoltaic panels based on infrared images is one of the important tasks in the daily maintenance of photovoltaic panels in photovoltaic power plants. In this paper, a defect detection method of infrared thermal image photovoltaic panel based on morphological segmentation is proposed. First of all, according to the infrared characteristics of the photovoltaic plant station and the morphological characteristics of the photovoltaic panel, the accurate region of each photovoltaic panel is determined in the inspection image. Then, statistical method is used for feature analysis to determine whether there are defects in photovoltaic panels, and then the defective photovoltaic panels are segmented and different defects are classified according to morphological characteristics, and finally the defect detection and classification of photovoltaic panels are realized. Through the experiment in the actual inspection image of photovoltaic plant station, it is verified that the proposed method has a high accuracy of defect detection and recognition. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Morphology

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Motion constraint patterns

Motion constraint patterns

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1993 IEEE Workshop on Qualitative Vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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Answer-supervised question reformulation for enhancing conversational machine comprehension 2

Answer-supervised question reformulation for enhancing conve...

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2nd Workshop on Machine Reading for Question Answering, MRQA@EMNLP 2019

作者： Li, Qian Su, Hui Niu, Cheng Wang, Daling Li, Zekang Feng, Shi Zhang, Yifei School of Computer Science and Engineering Northeastern University Shenyang China Pattern Recognition Center WeChat Ai Tencent Inc China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781950737819

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers. However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers. In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology. ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token. The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension. Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset. © 2019 MRQA@EMNLP 2019 - Proceedings of the 2nd Workshop on Machine Reading for Question Answering. All rights reserved.

关键词： Reinforcement learning

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Accurate extraction of line-structured optical stripe centerlines under low exposure 12

Accurate extraction of line-structured optical stripe center...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhu, Cheng Tian, keyun Gao, Yao Ding, Zhiqiang Hubei Key Laboratory of Optical Information and Pattern Recognition China Hubei Engineering Research Center of Video Image and HD Projection China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The line-structure light bar images acquired in industrial metal parts inspection may suffer from inaccurate extraction of the centerline of the line structure light bar due to the interference of ambient lighting and strong diffuse reflection and weak reflection of the part surface material. For this reason, this study proposes a method based on G-HL (Gauss-Hyper-Laplace Distribution) curve fitting to improve the center extraction accuracy of the line structure light bar in complex environments. First, the method uses Gaussian filtering to denoise the line-structured light image. Then, the laser line target pixel region is localized by finding the upper and lower boundaries of the line-structured light bar using the neighborhood gradient for each column of the line-structured light image, and the width of the line-structured light bar is calculated by the difference of the boundary coordinates, and the fitting interval is determined. Finally, within the fitting interval, the G-HL curve is used to fit the gray scale of the line-structured light cross-section to obtain the G-HL parameter, and the point of the maximum value of the G-HL curve is taken to be the coordinates of the center of the line-structured light. Experiments prove that the proposed method can accurately extract the center of the line structure light in low exposure. Compared with the methods such as the grayscale center of gravity method and the extreme value method, the proposed method has high accuracy and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Curve fitting

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