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Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

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Multispectral Food Classification and Caloric Estimation Using Convolutional Neural Networks.

Foods (Basel, Switzerland)·2023

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Updated: May 6, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

使用预测图像进行多光谱食品分类和热量估计.

¹Department of Electrical and Electronic Engineering, Konkuk University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, Republic of Korea.

Foods (Basel, Switzerland)

|February 24, 2024

概括

此摘要是机器生成的。

这项研究引入了一种新的食品识别方法,仅使用标准的红绿蓝 (RGB) 图像. 它准确地分类食物和估计卡路里,模仿多波长成像,不那么复杂.

关键词:

热量估计的热量估计.卷积神经网络是一种卷积神经网络.食品的认可食品的认可图像转换图像转换图像转换多光谱成像技术的使用.

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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

相关实验视频

Last Updated: May 6, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

科学领域:

营养科学营养科学
计算机视觉计算机视觉计算机视觉
图像处理图像处理

背景情况:

在营养科学中,以最小的用户输入进行持续的食品识别至关重要.
多波长成像 (UV,NIR) 提高了食品分类和热量估计的准确性.
由于分析时间和光源的增加,多波长成像存在实际挑战.

研究的目的:

开发一种方法,用于准确识别食品和仅使用标准红绿蓝 (RGB) 图像进行热量估计.
为了接近多波长成像技术的性能,而不需要多个光源.
为了减少与先进的食品识别方法相关的复杂性和分析时间.

主要方法:

提出了一种利用深度神经网络 (DNN) 来从标准RGB图像中预测多波长图像 (包括UV和NIR) 的新方法.
采用DNN来生成预测的光谱图像,绕过实际多波长数据采集的需要.
通过对101种不同食品的可行性测试,验证了该方法的有效性.

主要成果:

获得了高的食物识别率:实际多波长图像的99.45%和预测图像的98.24%.
与仅使用RGB图像 (86.3%率) 相比,显著改善了识别.
获得了可比的热量估计准确度,最低平均绝对百分比误差 (MAPE) 为11.67% (实际) 和12.13% (预测).

结论:

拟议的方法有效地使用标准RGB图像,以实现与复杂的多波长成像相似的性能,用于食品识别和热量估计.
这种方法为多波长技术提供了实用和高效的替代方案,减少了实施挑战.
这些发现支持基于RGB的深度学习模型在高级营养分析中的潜力.