In the research of synchronous location and map construction of indoor mobile robots, lidar is often used to build maps. However, three-dimensional lidar is expensive, two-dimensional lidar can only detect information...
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robotic harvesting of fruits and vegetables is an advanced technology that leverages robotics, Artificial Intelligence, and Machine vision to harvest the fruits autonomously from plants or trees. This technology aims ...
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We investigate generalizable face anti-spoofing (FAS) using information bottleneck theory. As generalizable FAS aims to detect spoofing in unseen scenarios, it has recently gained significant attention. Existing metho...
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Pose estimation with a vision system is a major factor during bin picking on the ability of the system to grasp the object. It helps the robot visualize the position and orientation of each object, through the pose th...
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Classifying multi-attributes is gaining interest in the research and business community, especially for person re-identification (ReID) and fashion trend analysis. However, manual annotation of multi-attributes is qui...
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The recent flourish of deep learning on various tasks is largely accredited to the rich and high-quality labeled data. Nonetheless, collecting sufficient labeled samples is not very practical for many real application...
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The proceedings contain 206 papers. The topics discussed include: synergy of human language processing and artificial intelligence;utilizing ANN in an advanced machine learning framework;CNN and random forest fusion f...
ISBN:
(纸本)9798350371567
The proceedings contain 206 papers. The topics discussed include: synergy of human language processing and artificial intelligence;utilizing ANN in an advanced machine learning framework;CNN and random forest fusion for enhanced steel defect classification;exploring the feasibility of forward algorithm in neural networks;big data: an essential route for creating new business prospects;automated detection and classification of cotton leaf diseases: a computer vision approach;advancements in digital signalprocessing: design and implementation of IIR bandpass optical filter using Optisystem;an analysis of artificial intelligence and bigdata cyber security its applications;advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions;and Parkinson’s disease diagnosis using neutral matrix and machine learning.
Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods ofte...
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Pleural effusion segmentation in computed tomography images is essential to its precise diagnosis and treatment but remains challenging due to blurred boundaries, heterogeneous morphology, and low contrast with adjace...
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This paper is designed to have an optical character recognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity s...
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ISBN:
(纸本)9781665483797
This paper is designed to have an optical character recognition system capable of interpreting captured images of hard disk drive and solid-state drive labels with high accuracy. Manual checking of the disk capacity size and part number found on the labels is time consuming, more prone to errors and utilizes more manpower. Automating the inspection through optical character recognition using image pre-processing and machine vision contributes to an easier inspection process, better management of records and faster cycle time. The images captured using a vision camera went through different stages of image pre-processing via OpenCV-Python and recognition through Google Tesseract. Different categorical variables including exposure time and location of texts in a captured image were used to determine and improve the overall recognition accuracy. By improving the lighting condition through the addition of light sources, the developed OCR system was able to achieve a character recognition accuracy of 99.375%.
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