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関連する概念動画

Confidence Coefficient01:24

Confidence Coefficient

10.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Econometric Views (EViews)01:29

Econometric Views (EViews)

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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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関連する実験動画

Updated: Feb 6, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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高信頼度ブロック対角線解析による制約のない環境下でのマルチビュー掌紋認識

Shuping Zhao, Lunke Fei, Tingting Cai

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 4, 2026
    PubMed
    まとめ

    本研究では、制御されていない環境下での身元認証を改善するために、マルチビュー掌紋認識（HCBDA MPR）のための高信頼度ブロック対角線解析を導入します。この手法は、堅牢な特徴保持のためにすべてのビューにわたるコンセンサスブロック対角線構造を確保することにより、精度を向上させます。

    キーワード:
    掌紋認識マルチビューブロック対角線高信頼度制約のない環境身元認証特徴抽出パターン認識コンピュータサイエンス生体認証

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    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
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    関連する実験動画

    Last Updated: Feb 6, 2026

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    Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

    Published on: December 11, 2015

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    SpOT the Correct Tissue Every Time in Multi-tissue Blocks
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    Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics
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    科学分野:

    • コンピュータサイエンス
    • 生体認証
    • パターン認識

    背景:

    • 制約のない掌紋認識は、現実世界のシナリオにおける画像品質、照明、ポーズの変動といった課題に直面しています。
    • 既存の手法はしばしば部分空間構造に依存しており、ブロック対角線特性が掌紋データに対して実証されています。

    研究 の 目的:

    • 堅牢なマルチビュー掌紋認識のための統一学習モデルを開発すること。
    • 特徴抽出を改善するために、すべてのビューにわたるコンセンサスブロック対角線特性を確保すること。

    主な方法:

    • マルチビュー掌紋認識のための新しい高信頼度ブロック対角線解析（HCBDA MPR）を提案しました。
    • コンセンサスブロック対角線構造を強制するために、マルチビューブロック対角線正則化器を導入しました。
    • ビュー全体にわたる厳密なブロック対角線構造を学習しながら、判別的特徴を保持しました。

    主要な成果:

    • 提案されたHCBDA MPR手法は、現実世界の制約のない掌紋データベースで優れたパフォーマンスを示しました。
    • 既存の最先端手法と比較して最高の認識精度を達成しました。
    • マルチビュー掌紋認識におけるコンセンサスブロック対角線特性の有効性を検証しました。

    結論:

    • HCBDA MPRは、制約のない掌紋認識において大きな進歩をもたらします。
    • この手法は、制御されていない環境によってもたらされる課題を効果的に解決します。
    • このアプローチは、マルチビュー掌紋を使用した身元認証のための堅牢なフレームワークを提供します。