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4th International Conference on Intelligent systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra x1 Jetson Nano

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Algorithm for detection and classification of small objects on a complicated background to automated robotic complex 10

Algorithm for detection and classification of small objects ...

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Conference on Optical Metrology and Inspection for Industrial applications x

作者： Semenishchev, Evgenii Urunov, Slavat Ilyukhin, Yuriy Siryakov, Aleksey Voronin, Viacheslav Moscow State Tech Univ STANKIN 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510667877;9781510667884

The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first-order finite difference, as well as the average square of the distance difference between the input implementation and the generated data;parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis;the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machine vision system installed on a robotic complex. Data on the required parameters of the formed machine vision systems are given, recommendations on the required parameters of the algorithms are presented.

关键词： image/signal smoothing edge detection preprocessing multicriteria method objects detection

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ViP: A Hierarchical parallel Vision Processor for Hybrid Vision Chip

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-ExPRESS BRIEFS 2022年第6期69卷 2957-2961页

作者： Zheng, xuemin Cheng, Li Zhao, Mingxin Luo, Qian Li, Honglong Dou, Runjiang Yu, Shuangming Wu, Nanjian Liu, Liyuan Chinese Acad Sci Inst Semicond State Key Lab Superlattices & Microstruct Beijing 100083 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China

Nowadays, the vision chip bridging sensing and processing has been extensively employed in high-speed image processing, owing to its excellent performance, low power consumption, and economical cost. However, there is a dilemma in designing processors to support conventional computer vision algorithms and neural networks since the two algorithms have a non-trivial trade-off in proposing a unified architecture. By analyzing computation properties, we propose a novel hierarchical parallel vision processor (ViP) for hybrid vision chips to accelerate both traditional computer vision (CV) and neural network (NN). The ViP architecture includes three parallelism levels: PE for pixel-centric, computing core (CC) for block, and vision core (VC) for global. PEs contain dedicated computing units and data paths for convolution operations without degrading its flexibility. Each CC is driven by customized SIMD instructions and can be dynamically connected for meeting block parallelism requirements. ViP is fabricated in 65nm CMOS technology and achieves a peak performance of 614.4 GOPS and an energy efficiency of 640 GOPS/W at 200 MHz clock frequency. Notably, several experiments on CV and NN are performed, illustrating an ultra-low latency in executing hybrid algorithms.

关键词： Convolution parallel processing Artificial neural networks Kernel Reduced instruction set computing Computer architecture Hardware Hierarchical parallel vision chip computer vision neural network

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Optimizing a medical image registration algorithm based on profiling data for real-time performance

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MULTIMEDIA TOOLS AND applications 2022年第2期81卷 2603-2620页

作者： Gulo, Carlos A. S. J. Sementille, Antonio C. Tavares, Joao Manuel R. S. Res Grp PIXEL UNEMAT CNPq Natl Sci & Technol Dev Council Brasilia DF Brazil Univ Porto Programa Doutoral Engn Informat Inst Ciencia & Inovacao Engn Mecan & Engn Ind Fac Engn Porto Portugal Univ Estadual Paulista UNESP Dept Ciencias Comp Fac Ciencias Prudente Brazil Univ Porto Fac Engn Dept Engn Mecan Inst Ciencia & Inovacao Engn Mecan & Engn Ind Porto Portugal

image registration is a commonly task in medical image analysis. Therefore, a significant number of algorithms have been developed to perform rigid and non-rigid image registration. Particularly, the free-form deformation algorithm is frequently used to carry out non-rigid registration task;however, it is a computationally very intensive algorithm. In this work, we describe an approach based on profiling data to identify potential parts of this algorithm for which parallel implementations can be developed. The proposed approach assesses the efficient of the algorithm by applying performance analysis techniques commonly available in traditional computer operating systems. Hence, this article provides guidelines to support researchers working on medical image processing and analysis to achieve real-time non-rigid image registration applications using common computing systems. According to our experimental findings, significant speedups can be accomplished by parallelizing sequential snippets, i.e., code regions that are executed more than once. For the selected costly functions previously identified in the studied free-form deformation algorithm, the developed parallelization decreased the runtime by up to seven times relatively to the related single thread based implementation. The implementations were developed based on the Open Multi-processing application programming interface. In conclusion, this study confirms that based on the call graph visualization and detected performance bottlenecks, one can easily find and evaluate snippets which are potential optimization targets in addition to throughput in memory accesses.

关键词： Medical image processing and analysis Profiling tools Performance analysis Non-rigid image registration

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Energy Efficient Approximate 3D image Reconstruction

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2022年第4期10卷 1854-1866页

作者： Wu, Yun Asmann, Andreas Stewart, Brian D. D. Wallace, Andrew M. M. Heriot Watt Univ Sch Engn & Phys Sci EPS Edinburgh EH14 4AS Scotland STMicroelectron R&D Ltd Edinburgh EH3 5DA Scotland

We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (compressed sensing (CS)) and approximate computing. Employing data parallelism for rapid image formation, the depth image is reconstructed from sparsely sampled scenes using convex optimization. Coupled with faster imaging, this sparse sampling reduces significantly the projected laser power in active systems such as light detection and ranging (LiDAR) to allow eye safe operation at longer range. We also demonstrate how reduced precision is leveraged to reduce the number of logic units in field-programmable gate array (FPGA) implementations for such sparse imaging systems. It enables significant reduction in logic units, memory requirements and power consumption by over 80% with minimal impact on the quality of reconstruction. To further accelerate processing, pre-computed, important components of the lower-upper (LU) decomposition and other linear algebraic computations are used to solve the convex optimization problems. Our methodology is demonstrated by the application of the alternating direction method of multipliers (ADMM) and proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square reconstruction method ($d$dSparse) is also presented. This demonstrates the feasibility of novel, high resolution, low power and high frame rate LiDAR depth imagers based on sparse illumination for use in applications where resources are strictly limited.

关键词： Approximate computing parallel computing FPGA LiDAR depth reconstruction compressed sensing convex optimisation

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Automatic detection and identification of ground vehicles with YOLO-based deep learning using MuSES-generated OPIR synthetic imagery 2

Automatic detection and identification of ground vehicles wi...

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Artificial Intelligence for Security and Defence applications ii 2024

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Gibbs, Scott C. ThermoAnalytics Inc. 23440 Airpark Blvd. CalumetMI49913 United States

ISBN: (纸本)9781510681200

Space-based sensor platforms, including both current and planned future satellites, are capable of surveilling Earth-based objects and scenes from high altitudes. Overhead persistent infrared (OPIR) is a growing surveillance technique where thermal-waveband infrared sensors are deployed on orbiting satellites to look down and image the Earth. Challenges include having sufficient image resolution to detect, differentiate and identify ground-based objects while monitoring through the atmosphere. Demonstrations have shown machine learning algorithms to be capable of processing image-based scenes, detecting and recognizing targets amongst surrounding clutter. Performant algorithms must be robustly trained to successfully complete such a complex task, which typically requires a large set of training data on which statistical predictions can be based. Electro-optical infrared (EO/IR) remote sensing applications, including OPIR surveillance, necessitate a substantial image database with suitable variation for adept learning to occur. Diversity in background scenes, vehicle operational state, season, times of day and weather conditions can be included in training image sets to ensure sufficient algorithm input variety for OPIR applications. However, acquiring such a diverse overhead image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR) when adversarial vehicles are of interest. In this work, MuSES™ and CoTherm™ are used to generate synthetic OPIR imagery of several ground vehicles with a range of weather, times of day and background scenes. The performance of a YOLO ("you only look once") deep learning algorithm is studied and reported, with a focus on how image resolution impacts algorithm detection/recognition performance. The image resolution of future space-based sensor platforms will surely increase, so this study seeks to understand the sensitivity of OPIR algorithm performance to overhead image resolution.

关键词： image resolution

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Hough Transform-based Correction Method for CCD External Interference of AST3-ii Telescope 4

Hough Transform-based Correction Method for CCD External Int...

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4th International Conference on Optics and image processing, ICOIP 2024

作者： Chen, Jing-Jian Wang, Liang Zheng, Jie Li, xiaoyan Li, Zhengyang Nanjing Institute of Astronomical Optics & Technology Chinese Academy of Sciences Nanjing210042 China University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Astronomical Optics & Technology Nanjing Institute of Astronomical Optics & Technology Chinese Academy of Sciences Nanjing210042 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Chinese Center for Antarctic Astronomy Nanjing210008 China

ISBN: (纸本)9781510682481

CCDs (Charge-Coupled Devices) have high sensitivity and low noise characteristics that offer significant ad- vantages for diverse scientific detection applications. However, CCD imaging systems are susceptible to both intrinsic and extrinsic interference due to multiple charge transfers during the readout process. The CCD used in the AST3-ii astronomical telescope located at Dome A in Antarctica has been severely affected by electromagnetic interference, resulting apparent stripes across the entire CCD images. To address this issue, we propose a novel correction method based on the Hough transform that effectively mitigates such interference effects. By applying the correction, we can significantly reduce such risks and enhance the reliability of exoplanet detection. © 2024 SPIE.

关键词： Hough transforms

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Quantum Computing in image processing 1

Quantum Computing in Image Processing

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1st International Conference on Recent Developments in Electronics and Communication systems, RDECS 2022

作者： Sachdeva, Kumud Sachdeva, Rajan Gupta, Himanshu Department of Computer Science and Engineering Chandigarh University Punjab Gharuan140413 India

ISBN: (纸本)9781643683607

If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types. © 2023 The authors and IOS Press.

关键词： image enhancement

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Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2022年 164卷 40-54页

作者： Barreiros Jr, Willian Melo, Alba C. M. A. Kong, Jun Ferreira, Renato Kurc, Tahsin M. Saltz, Joel H. Teodoro, George Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Georgia State Univ Dept Math & Stat Atlanta GA 30303 USA

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of pathology image analysis applications on modern distributed memory hybrid platforms equipped with both CPUs and GPUs. Hybrid systems offer significant computation capacity, but taking advantage of this computing power is complex. An application developer may have to implement multiple versions of data processing codes targeted for different computing devices. The developer also has to tackle the challenges of efficiently distributing computational load among the nodes of a distributed memory machine and among computing devices on a node. This is particularly difficult in analysis of high resolution images because of irregular computing costs of processing different image regions. In order to address these problems, we have leveraged a high-level image processing language (Halide) and integrated it into our runtime system called Region Templates (RT). The language simplifies the application development while generating code for multiple devices, such as CPU and GPU. The integration with RT allows for efficient multiple node hybrid execution. We also developed a novel cost-aware data partitioning (CADP) strategy that considers the workload irregularity to minimize load imbalance. Our experimental evaluation shows significant performance improvements on hybrid CPU-GPU machines, as compared with using a single processor (CPU or GPU), as well as on multi-GPU systems. CADP resulted in 1.7x better performance than other workload partitioning approaches (e.g., KD-Trees) on a hybrid machine and was up to 2.24x faster in multi-node settings. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： Microscopy imaging Region-templates framework Halide Distributed computing Hybrid computing

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Automatic target detection utilizing an Edge IR vision transformer (EIR-ViT) 33

Automatic target detection utilizing an Edge IR vision trans...

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Conference on Automatic Target Recognition xxxiiI

作者： Adams, Ethan R. Depoian, Arthur C., ii Kurz, Aidan G. Bailey, Colleen P. Guturu, Parthasarathy Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510661561;9781510661578

The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR Vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.

关键词： Automatic Target Detection Vision Transformer Infrared imaging Object Detection FLIR image FLIR dataset Edge Computing

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