In our paper various object’s size will be measured from an input image and also by means of camera. We had chosen open computer vision algorithm and Yolo v5 algorithm for faster and simpler process. Open cv has larg...
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Lung cancer is an abnormal development of cells that are uncontrollably proliferating. When using a system for medical diagnostics, the precise identification of lung cancer is crucial. Magnetic Resonance Imaging (MRI...
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Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scale...
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Eccentricity measurement of annular parts with millimeter scale and micrometer precision requirements is widely used in mechanical engineering applications. To realize accurate eccentricity measurement for large-scaled annular parts, a vision-based and sub-pixel dimensional measurement method is proposed. First, to facilitate the eccentricity measurement, an improved auto focus algorithm is introduced to provide better focused images of the measured parts. Then the traditional Canny operator is modified in gradient direction calculation and a double threshold process to locate the pixel edge more accurately. Next, a model-based sub-pixel edge detection method is studied to extract the sub-pixel edge coordinates. Finally, the eccentricity is calculated according to these sub-pixel edge coordinates. To guarantee measurement accuracy, the pixel equivalent and manual installation error of three degree of freedom (DOF) stages are calibrated, and the verification experiments indicate that the measurement error of the proposed method is better than 1.0 mu m. (C) 2022 Optica Publishing Group
image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downs...
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Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. Ther...
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Pipe inspection is a crucial process in any industry and it must be regularly done for the smooth and safe functioning of industries. Manual Pipe inspection is a sluggish and labour-intensive task therefore it is rece...
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In computer-vision feature extraction algorithms, compressing the image into a sparse set of trackable keypoints, empowers navigation-critical systems such as Simultaneous Localization And Mapping (SLAM) in autonomous...
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In computer-vision feature extraction algorithms, compressing the image into a sparse set of trackable keypoints, empowers navigation-critical systems such as Simultaneous Localization And Mapping (SLAM) in autonomous robots, and also other applications such as augmented reality and 3D reconstruction. Most of those applications are performed in battery-powered gadgets featuring in common a very stringent power-budget. Near-to-sensor computing of feature extraction algorithms allows for several design optimizations. First, the overall on-chip memory requirements can be lessened, and second, the internal data movement can be minimized. This work explores the usage of an Application Specific Instruction Set Processor (ASIP) dedicated to perform feature extraction in a real-time and energy-efficient manner. The ASIP features a very Long Instruction Word (vLIW) architecture comprising one Rv32I RISC-v and three vector slots. The on-chip memory sub-system implements parallel multi-bank memories with near-memory data shuffling to enable single-cycle multi-pattern vector access. Oriented FAST and Rotated BRIEF (ORB) are thoroughly explored to validate the proposed architecture, achieving a throughput of 140 Frames-Per-Second (FPS) for vGA images for one scale, while reducing the number of memory accesses by 2 orders of magnitude as compared to other embedded general-purpose architectures.
In the field of multi-object tracking, this study introduces an innovative framework designed to address the challenges posed by frame loss in image sequences, particularly within the contexts of video surveillance an...
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Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as imageprocessing for security applications. Deep learning, a subset of AI, is particularly useful for tho...
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ISBN:
(纸本)9783031133244;9783031133237
Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as imageprocessing for security applications. Deep learning, a subset of AI, is particularly useful for those imageprocessingapplications. Deep learning methods can achieve state-of-the-art results on computer vision for image classification, object detection, and face recognition applications. This allows to automate video surveillance reducing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Hardware manufacturers have developed powerful co-processors specifically designed to execute deep learning algorithms. But also, new lightweight open-source middleware for constrained resources devices such as EdgeX foundry have emerged to facilitate the collection and processing of data at sensor level, with communication capabilities to cloud enterprise applications. The aim of this work is to show and describe the development of Smart Camera Systems within S4AllCities H2020 project, following the edge approach.
Metasurfaces for edge detection through spatial analog calculations have attracted much attention due to advantages such as a flexible design and small footprint. Up until now, most studies have focused on single-wave...
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Metasurfaces for edge detection through spatial analog calculations have attracted much attention due to advantages such as a flexible design and small footprint. Up until now, most studies have focused on single-wavelength operation in the near-infrared or visible regions, while little work has been done in the ultraviolet band. It is of significance to explore metasurfaces for edge detection in the ultraviolet band for their great potential in highresolution imaging and lithography. Here, we propose a dual-wavelength HfO2 metasurface for edge detection working at 273 nm and 293 nm, with 25% and 72% efficiency, respectively, controlled by the linear polarization of the incident light. The efficient dual-wavelength second-order differential calculation in the ultraviolet band of the metasurface has been confirmed by 1D signal and 2D imageprocessing. It may find applications in the fields of computer vision and bioimaging. (c) 2023 Optica Publishing Group
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