Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, lo...
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ISBN:
(纸本)9781510673977;9781510673960
Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, low-cost systems require simpler approaches. We developed a fast detector-agnostic tracker for improving situational awareness using electro-optical video data. Our approach uses low computational techniques such as YOLO, match filters, and shape transforms to segment objects of interest in an image. From two or more successive detections, we initialize an alphabeta filter that predicts the location of the target of interest in the image. Next, we segment subsequent frames to a search area around the predicted region. This increases the sensitivity of the detector by improving the average signal-to-noise ratio and it also decreases the false alarm rate. The reduction in the size of the processing area can improve the detection speed per frame by an order of magnitude relative to a full-sized frame. By using algorithms with input from variablesize object a such as YOLO, this algorithm can be adapted to track virtually any object captured in a video.
In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, oft...
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ISBN:
(纸本)9798400717024
In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, often fall short of achieving the high degree of accuracy and precision necessary for today's complex PCB designs. This research presents a novel approach that significantly enhances measurement accuracy through the application of optimized imageprocessing techniques. By leveraging advanced algorithms within the OpenCV library, we introduce a methodology that accurately transforms pixel coordinates into real-world measurements, crucial for precise PCB alignment. Our technique employs edge detection algorithms such as Canny, Sobel, and Prewit filters, combined with a machine learning model that adapts to variations in real-time imaging conditions. The study delineates the development of a user-friendly graphical interface that streamlines the measurement process, making it accessible for practical industrial application. Results from experimental validations indicate a substantial improvement in measurement precision, with a demonstrable reduction in alignment errors compared to conventional methods. This leap forward not only promises to elevate the standards of PCB manufacturing but also opens avenues for similar advancements in other domains where image measurement is essential. The implications of this work are far-reaching, with the potential to significantly boost the efficiency and reliability of electronic manufacturing processes globally.
In this study, we comprehensively examine the potential of deep learning algorithms in the domain of medical imageprocessing. Through a systematic analysis of existing literature, we explore the applications, methodo...
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Computer blockchain technology provides a possible means to enhance data security, transparency, and overall efficiency. This study focuses on the design and optimization ideas of using blockchain technology in studen...
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Computer image simulation is used in the development of optical measurement systems and processingalgorithms. It allows the performance of a system or algorithm to be verified and its measurement uncertainty to be as...
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Efficient imageprocessing architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-perfor...
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ISBN:
(纸本)9798400709586
Efficient imageprocessing architectures are consistently in demand across a multitude of applications, particularly those customized for resource-constrained systems-on-chip (SoC). The increasing need for high-performance imageprocessing in various sectors has driven the development of specialized architectures. However, deploying such architectures on platforms with limited resources, such as SoCs, poses significant challenges. Furthermore, the implementation of complex algorithms to handle large datasets using software solutions often leads to slower response times, prompting exploration into hardware implementations. Field-Programmable Gate Arrays (FPGAs) are becoming popular for hardware implementations because of their attributes: low latency, connectivity, parallel computing capabilities, and flexibility. Consequently, the utilization of FPGA-based implementations has resulted in faster and more efficient performance of unique architectures tailored to specific requirements. This paper presents a novel hardware/software co-design approach to implement erosion, dilation, and neighborhood imageprocessing operations on the FPGA development board, "Zedboard". In this approach, the FPGA is programmed by connecting it to a PC via USB, facilitating the transfer of an image pixel by pixel. The pixels are temporarily stored in on-chip DDR and accessed through DMA (Direct Memory Access) until they are requested by an interrupt signal from the imageprocessing IP, at which point they are moved to line buffers for faster processing. Once processed, the image is transmitted back to the PC via UART, facilitating pixel-by-pixel transfer for verification, where it is compared with a reference image generated using Python. This comparison confirms a 99.22% match between the processed image and the reference image, with the discrepancy occurring at the image's edges due to initial padding. Additionally, the time required to process the entire image was measured and displayed
image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom ...
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ISBN:
(纸本)9798350388350;9798350388343
image stabilization plays a crucial role in providing accurate and reliable visual information for machine vision applications. In maritime applications, such as unmanned ship navigation, where six degrees of freedom (DOF) motion and harsh maritime conditions prevail, the efficacy of image stabilization technology is vital for robust imageprocessingalgorithms. This paper offers a comprehensive review of image stabilization techniques tailored for maritime environments, developed over the past two decades. We analyzed a total of 39 research articles on the subject, sourced from Web-of-Science, SCOPUS, and the Engineering Index databases, discussing potential research directions to address the limitations of current image stabilization methods, with special consideration for the unique requirements of ship-borne cameras. It provides an up-to-date overview of the techniques, limitations, and algorithms of ship-borne cameras for maritime applications, identifying current knowledge gaps and areas requiring further research. This review aims to guide the development of new technologies and methods to improve the performance of image stabilization systems in maritime contexts.
In order to actively promote the construction of new power systems and the digital transformation and upgrading of power grid companies, and explore the application of artificial intelligence technology, the power gri...
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ISBN:
(纸本)9798400707032
In order to actively promote the construction of new power systems and the digital transformation and upgrading of power grid companies, and explore the application of artificial intelligence technology, the power grid company has built artificial intelligence "two libraries and one platform", that is, artificial intelligence platform, sample library, model library, and has carried out a lot of work in the application of artificial intelligence technology in equipment management, safe operation, marketing customer service and other business fields, and has precipitated rich samples, algorithms, and model achievements. With the increasing demand for intelligent application services and the increasing demand for samples and models in various business departments, it is urgent to introduce capabilities such as intelligent identification of electrical components and defect detection in scarce scenes on top of the existing artificial intelligence basic support capabilities to realize model training, iteration, optimization and improvement of accuracy, and deeply integrate the basic capabilities of artificial intelligence with the core business applications of the power grid. Further improve the ability to empower the profession and serve the grassroots.
Together with dairy and wheat, soybeans are a major agricultural import into the Philippines. Historically, imports accounted for 99% of the country's supply of soybeans from 1995 to 2014, with local manufacturers...
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ISBN:
(纸本)9798350372113;9798350372106
Together with dairy and wheat, soybeans are a major agricultural import into the Philippines. Historically, imports accounted for 99% of the country's supply of soybeans from 1995 to 2014, with local manufacturers making up the remaining 1%. The emergence of novel technologies has enabled the classification of diverse agricultural commodities, such as soybean cultivars, by merging computer vision and machine learning methodologies that utilize edge detection algorithms. Precise categorization of seed variants is essential for farmers and seed manufacturers to maintain variety purity, which in turn affects crop productivity and the quality of soybeans provided to nearby retailers. Morphological features were retrieved from pre-processed soybean pictures using the regionprops function, which made use of edge detection methods. After the extraction of features, the data was subjected to pre-processing and machine learning analysis. The KNearest Neighbors (KNN) model classified the data using Euclidean distance. A 75:25 split of the dataset was made into training and testing subsets, with five neighbors being used for categorization. The CL1 and PSB SY2 soybean varieties were classified by the KNN model with an accuracy rate of 85%, indicating a first step in variety classification research.
Developing healthcare systems for disease diagnosis and treatment require medical image segmentation as a vital prerequisite. U-Net, which is a U-shaped architecture, has become the standard in the segmentation of int...
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