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Accelerated Real-Time Local Maxima Detection in Video Streams Using FPGA Technology

Accelerated Real-Time Local Maxima Detection in Video Stream...

引用

2024 Asia Pacific signal and Information processing Association Annual Summit and conference

作者： Sukarno, Anindhita Nayazirly Salomo, Yahwista Adiono, Trio Syafalni, Infall Sutisna, Nana Mulyawan, Rahmat Inst Teknol Bandung Bandung Indonesia

ISBN: (纸本)9798350367331;9798350367348

In this paper, we present a successful implementation of a local maxima filter on a Zybo Z7-20 and PYNQ Z1 FPGA using their two HDMI ports in real-time. The proposed system uses the HDMI ports to capture video frames with a resolution of 640x480 pixels. The local maxima filter is then applied to the captured frames in real-time, allowing for the detection of peaks in the image data. The filter uses a sliding window approach to determine the local maxima, and a threshold value is set to identify and retain only the most significant peaks in the image. The system was implemented using SystemVerilog Hardware Description Language (HDL). The system was developed in the xilinx Vivado design suite and the results show that the proposed system is able to process video frames at a rate of 60 frames per second with high accuracy and low latency. The proposed implementation using SystemVerilog presents a more efficient and flexible solution for image processing applications on FPGA, making it a promising approach for real-time image processing.

关键词： image processing

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Automatic Instantaneous Attitude Estimation of Satellite Based on Optical and ISAR image Fusion

Automatic Instantaneous Attitude Estimation of Satellite Bas...

引用

9th International conference on signal and image processing (ICSIP)

作者： Lu, xue Zhang, Shuanghui Liu, Afei Li, Ruize Natl Univ Def Technol Coll Elect Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350350920

The instantaneous attitude estimation of satellite becomes a focal topic in the increasingly significant space situation awareness area. Both the optical, inverse synthetic aperture radar (ISAR) and their fusion way have been widely applied. Due to the domain gap between two different types of data, it is difficult to make use of them for automatic attitude estimation. In view of this, this paper proposes an automatic instantaneous attitude estimation method based on optical and ISAR image fusion. The method deduced mathematical expressions of target's projected features and state parameters. Take inspiration from human joint position extraction network, a key points extraction network is proposed for automatic points extraction on both the optical and ISAR image. These extracted points are utilized to determine the projected features of target's typical structures which then be used for attitude estimation. By this means, the attitude of satellite can be estimated through optimization solved by the particle swarm optimization (PSO) algorithm. Some experiments are present to validate the feasibility and superiority of the proposed method.

关键词： attitude estimation colocated optical and ISAR observation multimodal images fusion satellite surveillance

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Video motion and Decryption. A Comparative Study for Compressed Videos. 13

Video motion and Decryption. A Comparative Study for Compres...

引用

13th International conference on image processing Theory Tools and applications

作者： Voitenko, Anastasiia Oussalah, Mourad Univ Oulu Fac ITEE Ctr Machine Vis & Signal Proc Oulu Finland

ISBN: (纸本)9798331541859;9798331541842

Transmission of video data is known to cause high privacy leak risk due to effect of data compression rates and disparate standards, which challenge encryption/decryption methods. This paper presents a comparison of AES cipher, Arnold Cat chaotic map and Logistic chaotic map in the context of various video motion types (slow, medium and fast). The study aims to distinguish the video motion types according to decryption result. For this purpose, several performance metrics were tested, which include unified averaged changed intensity (UACI), number of changing pixel rate, peak signal-to-noise ratio, correlation coefficient (CC) and histogram of intensity. A simulation setting is devised where each video undergoes encryption phase, followed by lossy compression using discrete cosine transform and followed by image decryption phase. Simulation results demonstrate that AES and Logistic chaotic map present relatively close performance outperformance that of Arnold cat map. On the other hand, correlation coefficient appears to be the only performance metric that presents interesting capabilities to distinguish slow-motion video from medium and fast-motion types.

关键词： Video Encryption AES Algorithm Arnold Cat Map Logistic Chaotic Map Video motion type

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L-MFFN: Lightweight Multiscale Feature Fusion Network with Limited Samples for HSI Classification

L-MFFN: Lightweight Multiscale Feature Fusion Network with L...

引用

9th International conference on signal and image processing (ICSIP)

作者： Ali, Aamir Mu, Caihong Wang, Yafeng Liu, Yi Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9798350350920

Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional Neural Networks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.

关键词： hyperspectral image classification small sample multiscale feature fusion spectral-spatial attention

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Forward error correction for low-latency transmission of JPEG xS video streams 47

Forward error correction for low-latency transmission of JPE...

引用

conference on applications of Digital image processing xLVII

作者： Richter, Thomas Foessel, Siegfried Fraunhofer Inst Integrated Circuits IIS D-91058 Erlangen Germany

ISBN: (纸本)9781510679344;9781510679351

JPEG xS is a lightweight, low-latency image coding standard for transmission of video streams over IP;similar to many other coding standards, it does only define the codestream syntax and the decoding algorithm, but not the transport mechanism. For this, IETF defines in RFC 9134 an RTP payload format for JPEG xS for transmission over UDP packets. As long as encoder and decoder are part of the same local area network, error-free transmission can be assumed, but this does not hold for wide-area or wireless networks were packet loss may disrupt the transmission. While multiple protocols exist to ensure error-free transmission in such networks, many of them depend on packet retransmission which impacts the latency of the overall system and by that one of the unique design aspects of JPEG xS. In this work, we equip a JPEG xS encoder and decoder with foreward error correction according to SMPTE ST 2022-5 which is able to mitigate such errors while also keeping the latency under control. We report on the outcome of an experiment where we measured image quality as a function of error rate and also provide estimates on the additional latency due to the error correction layer.

关键词： Forward error correction wavelets Data transmission Video Video coding

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A Robust Large-scale Dataset for Assessing Light Field image Quality

A Robust Large-scale Dataset for Assessing Light Field Image...

引用

9th International conference on signal and image processing (ICSIP)

作者： Ma, Jian Liu, Zhuochi xu, Yaozong Yu, Jiajun Wang, Yang Liu, Yuhao Anhui Univ Sch Internet Hefei Peoples R China

ISBN: (纸本)9798350350920

In recent years, light field imaging has gained significant attention in the scientific community due to its ability to provide a more immersive representation of the 3D world. However, ensuring the quality of light field images is crucial for their subsequent processing and applications. Deep learning methods, leveraging neural networks, have shown promising performance in image Quality Assessment (IQA). However, the unique characteristics of light field data pose a challenge for existing IQA methods. To address this challenge, we propose a Robust Large-scale Dataset for Assessing Light Field image Quality, named RLSD, specifically designed for evaluating the quality of light field images. The dataset comprises both real and synthetic scenes, covering a wide range of key low attributes and including three representative distortions: compression, noise, and blur. To obtain subjective evaluations, we adopt the single stimulus continuous quality evaluation (SSCQE) method and compute the Mean Opinion Score (MOS). We performed statistical analysis on the dataset and experimental results indicate that our proposed RLSD dataset includes various common scenes and distortion levels, making it suitable for designing and evaluating LF-IQA algorithms. The dataset is publicly available at the following link: "https://***/s/1kJmx4qsy8ywLPba-HwGCEg" (password: xY28).

关键词： Light field image quality assessment RLSD distortion Single stimulus continuous quality evaluation

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SAR Ship Classification based on Second-order Synchrosqueezing Transform and HOG Feature

SAR Ship Classification based on Second-order Synchrosqueezi...

引用

9th International conference on signal and image processing (ICSIP)

作者： Li, Dongrui Zhao, Zhichun Shenzhen MSU BIT Univ Fac Engn Shenzhen Peoples R China

ISBN: (纸本)9798350350920

Accurate classification and identification of vessels in remote sensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.

关键词： Synthetic aperture radar (SAR) ship classification non-stationary signal processing time-frequency analysis (TFA) histogram of oriented gradient (HOG) convolutional neural network (CNN)

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Deep learning-based ECG signal processing algorithms and their applications in cardiac health monitoring 4

Deep learning-based ECG signal processing algorithms and the...

引用

4th International conference on Advanced Algorithms and signal image processing, AASIP 2024

作者： Zhang, Zhiguo Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

This paper explores deep learning-based algorithms for electrocardiogram (ECG) signal processing and their applications in cardiac health monitoring. Initially, we provide an overview of the fundamental principles of ECG signals and the suitability of deep learning for ECG signal processing. Subsequently, we discuss data preprocessing techniques and the selection and construction of deep learning models aimed at enhancing the accuracy of ECG signal processing. Finally, the focus is on the application of these algorithms in the detection of arrhythmias and the diagnosis of myocardial ischemia, validated through experimental results. The findings of this study offer new technological approaches for cardiac health monitoring and foster the application of deep learning in the field of ECG signal processing. © 2024 SPIE.

关键词： Electronic health record

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S-ACMT: Enhancing Semi-supervised Medical image Segmentation with SAM as a Cross-Supervised Secondary Teacher

S-ACMT: Enhancing Semi-supervised Medical Image Segmentation...

引用

9th International conference on signal and image processing (ICSIP)

作者： Lin, Can Liu, Yuanzhe Wang, Dahao Lin, Li City Univ Macau Fac Data Sci Macau Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9798350350920

With the swift evolution of deep learning technologies, an array of training paradigms for machine learning models has emerged, spanning fully supervised, semi-supervised, and unsupervised methodologies. Semi-supervised learning, distinguished for its capability to exploit the rich semantic content in unlabeled data, mitigates the demand for extensively annotated datasets by domain experts. This attribute is particularly beneficial in medical image segmentation, where precise expert annotations are crucial, thus attracting considerable attention from the research community. Despite the promising outcomes of semi-supervised learning, achieving consistent and robust performance in scenarios with limited annotations remains a considerable *** response, we introduce an innovative approach that integrates SAM-Med2D-a state-of-the-art 2D medical image segmentation model-as an auxiliary supervision tool to refine semi-supervised learning processes for medical image segmentation. Our methodology entails initial finetuning of SAM-Med2D with available annotated data to embed domain-specific knowledge. Subsequently, during the training process, pseudo-labels generated by SAM-Med2D for unlabeled data are employed in a cross-pseudo-label learning scheme alongside a student model. This arrangement, meticulously supervised by the comprehensive SAM-Med2D and designated teacher models, has cultivated an exceptional ability in the student models to represent *** evaluations of our framework demonstrate a significant enhancement in performance metrics over several existing semi-supervised learning models, underscoring the efficacy of our proposed strategy.

关键词： Semi-supervised Learning Medical image Segmentation SAM Auxiliary Supervision

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Motion Estimation and signal Integration Method for High-Speed Target Detection With Airborne Bistatic Radar

Motion Estimation and Signal Integration Method for High-Spe...

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9th International conference on signal and image processing (ICSIP)

作者： Zhang, Zerui Li, xiaolong Yang, Fan Wang, Mingxing Gu, Jie Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Univ Elect Sci & Technol China Natl Key Lab Electromagnet Space Secur Chengdu Peoples R China Southwest China Res Inst Elect Equipment Chengdu Peoples R China

ISBN: (纸本)9798350350920

This paper addresses the motion estimation and signal integration problem for high speed target detection with airborne bistatic radar, where the range migration (RM) and Doppler frequency migration (DFM) induced by target's motion, and the motion error of the airborne bistatic radar are all taken into account. We establish the signal model for airborne bistatic radar system with high-speed target, along with motion error model which simultaneously encompasses both translational error and rotational error. Besides, we propose a motion estimation and signal integration method. In particular, the proposed method applies the generalized Radon transform (GRT) to the estimate target's equivalent motion parameters (i.e., range, velocity and acceleraiton). Subsequently, the proposed method utilizes the 3-dimensional (3-D) generalized Radon Fourier transform (GRFT) to achieve the motion error parameters estimation of airborne platform, where the signal coherent integation results could also be obtained. Numerical simulations and performance analysis verify the effectiveness of proposed approach, which can obtain better integration detection and estimation performance than the typical existed methods.

关键词： airborne bistatic radar motion estimation high-speed target coherent integration

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