Image Recognition Software
Image Recognition Software - Related Videos
11:55
Physiological Correlates of Emotion Recognition
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California The autonomic nervous system (ANS) controls the activity of the body's internal organs and regulates changes in their activity depending on the current environment. The vagus nerve, which innervates many of the internal organs, is an important part of the system. When our brain senses danger, vagal tone is inhibited, leading to a set of changes in the body designed to make us more prepared to fight or...
08:32
Imaging C. elegans Embryos using an Epifluorescent Microscope and Open Source Software
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Cited by 7 •
2011
The C. elegans embryo is a powerful system for studying cell biology and development. We present a protocol for live imaging of C. elegans embryos utilizing DIC optics or fluorescence using readily available epifluorescent microscopes and open-source software.
08:27
Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
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Cited by 1 •
2024
The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.
08:39
High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately
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Cited by 117 •
2014
We present a high-throughput image analysis software application to measure the size of three-dimensional tumor spheroids imaged with bright-field microscopy. This application provides a fast and effective way to examine the effects of therapeutic drugs on spheroids, which is beneficial for researchers who wish to use spheroids in drug screens.
04:18
Use of Three-Dimensional Imaging Reconstruction Software as a Training Tool for Cranial Vena Cava Venipuncture in the Ferret
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2025
We propose that a three-dimensional virtual anatomy reconstructive software tool adds beneficial adjunctive training in teaching ferret cranial vena cava venipuncture to laboratory animal medicine trainees.
FAQs
The software first works with pixel data, then preprocessing prepares that data for analysis before feature extraction identifies relevant visual characteristics. These stages help present images in a form that machine learning models can evaluate for patterns, objects, scenes, or technical elements. Their output supports more consistent classification or detection in subsequent engineering analysis.
Classification assigns an image to a recognized category, whereas detection identifies specific elements within the image. This distinction matters in engineering because a system may need to judge an image overall or locate a particular defect, object, or component. Selecting the appropriate task determines what information the model returns and how engineers can use the result.
Neural networks are among the machine learning models that can analyze visual patterns after image data has been prepared and relevant features extracted. They support automated classification and detection of elements in technical imagery. In engineering systems, this capability can reduce reliance on repeated manual assessment and contribute to faster, more consistent decisions.
A typical workflow begins with digital image data, followed by preprocessing and feature extraction. A machine learning model then analyzes the prepared information to classify the image or detect specified elements. Engineers can interpret those results for inspection, monitoring, or design-related decisions. The workflow connects raw pixel data with an actionable technical assessment.
Engineering applications include quality inspection, defect detection, and equipment monitoring. In these settings, the software can help assess technical imagery rapidly and consistently, supporting decisions about manufactured output or equipment condition. Its use can reduce the burden of manual assessment while contributing to manufacturing efficiency and improved system reliability.
Robotics can use visual analysis to identify objects or other relevant elements, helping automated systems interpret their surroundings or technical imagery. Within increasingly autonomous engineering systems, these capabilities support rapid, data-driven decisions rather than relying solely on manual review. The resulting visual information can contribute to more efficient operation and design development.