Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

Shi, Tianyi; Boutry, Nicolas; Xu, Yongchao; Géraud, Thierry

doi:10.1109/tip.2022.3155954

Cited by 39 publications

(1 citation statement)

References 74 publications

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“…This parameter allows for adding custom fully connected layers on top of the convolutional base. The input shape is set to (64, 64, 3), which is the shape of the dataset images [20], [22].…”

Section: Neural Network Architecturementioning

confidence: 99%

Application of Ai-Enhanced Image Processing Methods for Educational Applied Physics Experiments

Laukhammer,

Mačerauskas,

Lučun

et al. 2024

ETR

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This article examines the use of an AI-powered automated image analysis system. The system's purpose is to enhance the workflow of students during applied physics laboratory experiments, helping them analyze images and perform accurate microobject counting. On the software side, the system incorporates machine learning algorithms for visual processing applications using Python and its’ extension libraries – CV2, Tensorflow, Keras, SkLearn etc.. The hardware consists of a camera and microprocessor, which, in conjunction with the image processing software, perform microobject recognition and counting in real-time. The goal is to automate applied physics laboratory experiments in which the counting of microobjects, be it organic or human-made, is usually done manually. During these applied physics laboratory experiments and with the aid of this system, students are exposed to a modern workflow, further preparing them for future work environments, teaching them about process automation, and further increasing their interest in micro-scale related science subjects. Automation using image processing technology combined with automatic data logging from images allows for fast and accurate micro-object counting.

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Section: Neural Network Architecturementioning

confidence: 99%