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Wide band off-axis reflection imaging system 7

Wide band off-axis reflection imaging system

引用

7th Optics Young Scientist Summit, OYSS 2024

作者： Bo, Zhang Weijun, Chang Gangyang, Xi Zijian, Yang Ani, Wang Laboratory of Optoelectronic Imaging Technology Xi'An Institute of Applied Optics No 9 W Diansi 3rd Rd Shaanxi Xi'an710065 China

ISBN: (数字)9781510688377

ISBN: (纸本)9781510688360

In response to the urgent needs of military optoelectronic systems for field of view range, image quality, and system miniaturization, this article designs a large aperture off-axis tri reflection optical system according to design requirements. the off-axis multi reflection structure is used to eliminate system obstruction and reduce structural size. By introducing complex surface shapes, the large aperture design is achieved. While ensuring sufficient resolution, increasing the field of view angle of the optical system can obtain richer target features and improve the system's target detection and recognition capabilities. the design result is an axial MTF> 0.35@120lp /Mm, RMS wavefront error 0.019 Λ, the maximum distortion in the full field of view is 0.41%. Sensitivity analysis was conducted based on the actual system installation and debugging process, and the RMS wavefront error was 0.053 after the final system installation and debugging was completed Λ, the use of near-infrared and shortwave cameras to image external scenery clearly and with clear details has certain guiding significance for the research of off-axis reflection imaging systems. © 2025 SPIE.

关键词： Sensitivity analysis

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FIR: A Plug-in Feature-to-image Reconstruction Method for Feature Coding for Machines 7th

FIR: A Plug-in Feature-to-Image Reconstruction Method for Fe...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Zhang, Yuan Xue, Junda Wang, Huifen Li, Yunlong Yu, Lu China Telecom Res Inst Shanghai Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9789819786848;9789819786855

With the groundbreaking development of Artificial Intelligence (AI) technology, the volume of video and image consumed by machines has surpassed that consumed by humans. Consequently, video coding for machines technology has grown rapidly, with feature coding as a prominent technique demonstrating exceptional compression and task performance. this technology has developed rapidly and has entered the stages of chip integration and industrialization. Feature coding for machines can only reconstruct feature tensors, not images. However, in typical machine vision application scenarios such as smart cities, industrial quality inspection, intelligent transportation, and automated broadcasting, there still persists a demand for video and image review. these needs are essential for retrospective analysis of abnormal events, confirmation of incidents, and evidentiary purposes. this work attempts to reconstruct images from feature tensors extracted for machine vision tasks to meet the needs of human visual observation. We propose a lightweight and plug-in feature-to-image reconstruction method for feature coding for machines, with low complexity neural network blocks. the proposed method achieves an average Peak Signal-to-Noise Ratio (PSNR) of up to 28.92 dB, meeting the requirements of human visual perception.

关键词： Feature-to-image Reconstruction Feature Coding for Machines Video Coding for Machines

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Straight-Line recognition in a Virtual Hexagonal Grid Using Hough Transform 7th

Straight-Line Recognition in a Virtual Hexagonal Grid Usin...

引用

7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Ouedraogo, Moïse Sere, Abdoulaye Bobo-Dioulasso Burkina Faso

ISBN: (纸本)9783031864926

the Hough transform is a powerful mathematical tool designed for detecting geometric patterns, including straight lines, circles, and other shapes, within images. the fundamental idea behind this transformation is to represent the points of an image in a parameter space specific to the pattern one aims to detect. this paper focuses on analytical straight-line recognition, using standard Hough Transform in a virtual grid. Each cell in the virtual grid is hexagonal. the method consists of superimposing a virtual grid on the image and calculating the transformation of lit pixels in hexagonal cells where the rate of lit pixels exceeds a threshold. the voting threshold is a criterion that determines how many votes are required for a line to be considered detected. It is an important parameter that influences the quality and reliability of line detection in an image. A lower voting threshold includes noise and consequently degrades the detection quality. Our approach takes advantage of this limitation to optimize analytical straight-line recognition. the experimental results show an improvement in execution time compared with the standard Hough transform method. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Hough transforms

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Enhancing Cross-Modal Alignment in Multimodal Sentiment analysis via Prompt Learning 7th

Enhancing Cross-Modal Alignment in Multimodal Sentiment Anal...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Wang, Xiaofan Li, Xiuhong Li, Zhe Zhou, Chenyu Chen, Fan Yang, Dan Xinjiang Univ Sch Informat Sci & Engn Urumqi Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9789819786190;9789819786206

Multimodal sentiment analysis (MSA) aims to predict the sentiment expressed in paired images and texts. Cross-modal feature alignment is crucial for models to understand the context and extract complementary semantic features. However, most previous MSA tasks have shown deficiencies in aligning features across different modalities. Experimental evidence shows that prompt learning can effectively align features, and previous studies have applied prompt learning to MSA tasks, but only in an unimodal context. Applying prompt learning to multimodal feature alignment remains a challenge. this paper employs a multimodal sentiment analysis model based on alignment prompts (MSAPL). Our model generates text and image alignment prompts via the Kronecker Product, enhancing visual modality engagement and the correlation between graphical and textual data, thus enabling a better understanding of multimodal data. Simultaneously, it employs a multi-layer, stepwise learning approach to acquire textual and image features, progressively modeling stage-feature relationships for rich contextual learning. Our experiments on three public datasets demonstrate that our model consistently outperforms all baseline models.

关键词： Prompt learning Multimodal Sentiment analysis Alignment

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Multi-modality Correlation Learning Network for Pediatric Ventricular Septal Defects Identification 7th

Multi-modality Correlation Learning Network for Pediatric Ve...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Jin, Feifei Zhao, Cheng Xiang, Zhuo Chen, Xunyi Zhang, Yu Fan, Shumin Zhou, Luyao Chen, Weiling Wang, Tianfu Lei, Baiying Shenzhen Univ Sch Biomed Engn Guangdong Key Lab Biomed Measurements & Ultrasoun Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Children Hosp Ultrasound Dept Shenzhen Peoples R China

ISBN: (纸本)9789819784981;9789819784998

Comprehensive analysis of abnormal changes in anatomical structures in two-dimensional grey-scale ultrasound images and blood flow change characteristics in color Doppler images can be more conducive to the identification of ventricular septal defect diseases (VSD). Starting from the perspective of multi-modality, this paper designs a multi-modality correlation learning network (MC-Net) for VSD identification. MC-Net performs correlation analysis on multi-modality features from two perspectives: network structure and the image itself. In terms of network structure, this paper first constructs dual-branch feature cross-fusion blocks (CFB) to encode the associated information between different modalities to achieve the fusion of global features and local features and then performs reinforcement learning on the fused features through a series of hybrid learning blocks (HLB). In terms of the image itself, this paper designs a group selection transformer (GST) to capture the correlation between image tokens and their context, prompting the network to focus on the region of interest more effectively. this paper conducts experimental analysis in multi-modality five-chamber and parasternal short-axis views. the experimental results show that the identification performance of the proposed algorithm is better than that of the comparison methods.

关键词： VSD Identification Multi-modality Correlation analysis Group Selection Transformer

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Frequency Adapter and Spatial Prompt Network for All-in-One Blind image Restoration 7th

Frequency Adapter and Spatial Prompt Network for All-in-One ...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Chen, Shuoming Pei, Wenjie Lu, Yao Lu, Guangming Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9789819786848;9789819786855

image restoration aims to obtain a high-quality image from a degraded one. For real-world applications, an increasing number of methods are moving towards addressing multiple degradations using a single model. However, most of these methods still require task-specific training and primarily extract information from the spatial domain. To overcome this challenge, we introduce a novel All-in-one network, FASPNet, which effectively incorporates both frequency and spatial information to handle various degradations, without requiring any degradation priors. Specifically, we propose a Frequency Refiner Module (FRM), which adaptively adjusts frequency representations and captures crucial global frequency information to facilitate better image restoration. Furthermore, to provide essential low-level information related to restoration, we introduce a Spatial Prompt Module (SPM), utilizing prompts to encode restoration-relevant spatial detail representations and abstract degradation patterns. Extensive experiments have demonstrated that our model outperforms other baseline models on multiple datasets for three common and challenging tasks: deraining, dehazing, and denoising.

关键词： All-in-One image Restoration Frequency Refiner Module Spatial Prompt Module

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A Comparative Study of Inference Models Based on image recognition 7th

A Comparative Study of Inference Models Based on Image Recog...

引用

7th international conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Zhao, Deling Internet Information Research Institute Communication University of China Beijing China

ISBN: (纸本)9789819624089

Due to the limitation of web-side processing capabilities, the existing mainstream inference models adopt the client-to-server operation mode, and the emergence of WebAssembly has brought opportunities for the client to run the inference model alone for image recognition, and soon, the browser will fully support the standard. therefore, on the basis of image recognition, this paper runs the mainstream inference model on the client and server side, and then compares the inference rate of different inference models at different batch sizes and the inference efficiency of the same inference model on the client and server side, conducts a large number of comparative experiments to record the experimental results, and then uses the method of averaging the experimental data to obtain a set of reliable experimental conclusions. Experimental results show that the performance of server-side TensorRT is better than other models under the same configuration conditions, ONNXRuntime is better than PyTorch in small batches, and PyTorch's performance improvement in accuracy has more room for optimization. the results of web-side experiments show that the inference model has a large difference in GPU usage, ONNXRuntime shows a greater performance advantage in the efficiency of GPU inference, while TensorRT will show better performance with the increase of batch. Compared with server-side inference, web-side inference shows stronger stability because it relies less on network transmission, so it can be preferred to direct inference when the batch is small, and server inference is preferred when the client configuration does not meet the conditions. © the Author(s) 2025.

关键词： Graphics processing unit

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

引用

9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in Medical image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical image Computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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Learning Frequency and Structure in UDA for Medical Object Detection 7th

Learning Frequency and Structure in UDA for Medical Object D...

引用

7th Chinese conference on Pattern recognition and Computer Vision

作者： Wang, Liwen Zhang, Xiaoyan He, Guannan Tan, Ying Li, Shengli Pu, Bin Jin, Zhe Sha, Wen Dong, Xingbo Anhui Univ Anhui Prov Int Joint Res Ctr Adv Technol Med Imag Sch Artificial Intelligence Hefei Peoples R China Hong Kong Univ Sci & Technol Hksar Peoples R China Sichuan Prov Matern & Child Hlth Care Hosp Chengdu Peoples R China Shenzhen Matern & Child Healthcare Hosp Shenzhen Peoples R China

ISBN: (纸本)9789819784950;9789819784967

In medical imaging applications, particularly in cardiac and skeletal analysis, the anatomical structure detection is crucial for diagnosing cardiac disease and other disease. However, the domain gap between images acquired from different sources or modalities poses a significant challenge and impedes model generalization across diverse patient populations and imaging conditions. Bridging this gap is particularly essential in image-based diagnosis, where subtle variations in anatomical structures and imaging characteristics can profoundly impact diagnostic performance. Take fetal cardiac ultrasound images as an example, this paper proposes a novel method for unsupervised domain adaptive fetal cardiac structure detection. the method integrates both the frequency-based distributional properties and anatomical structural information inherent in medical images. Specifically, we introduce a Frequency Distribution Alignment (FDA) module and an Organ Structure Alignment (OSA) module to mitigate detection misalignment across different hospital settings. We demonstrates the effectiveness of these modules through extensive experiments. Our method significantly improves the performance of fetal cardiac structure detection tasks, enabling adaptation to diverse hospital scenarios and showcasing its potential in addressing domain gaps in medical imaging.

关键词： Unsupervised domain adaption Medical image analysis Object detection

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ICIAP-93 - 7th IAPR international-conference ON image-analysis AND PROCESSING - MONOPOLI, ITALY, 20-22 SEPTEMBER 1993

引用

AI COMMUNICATIONS 1994年第2期7卷 126-128页

作者： TRUCCO, E Machine Vision Unit Department of Artificial Intelligence University of Edinburgh 5 Forrest Hill Edinburgh EH1 2QL Scotland UK Email:

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