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関連する概念動画

Computed Tomography01:10

Computed Tomography

8.3K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Design Example: Traverse Angle Computations01:25

Design Example: Traverse Angle Computations

341
Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
341
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

625
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
625
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

365
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
365
Social Foundations of Self IV: Self in Digital Communication01:30

Social Foundations of Self IV: Self in Digital Communication

197
Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
197
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.9K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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普通のデジタルカメラによる計算ペリスコーピ

Charles Saunders1, John Murray-Bruce1, Vivek K Goyal2

  • 1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.

Nature
|January 25, 2019
PubMed
まとめ
この要約は機械生成です。

この研究は,普通のカメラから撮影した単一の写真を用いた新しい計算ペリスコープ方法を提示しています. 特殊な装置なしで障害物の周りの非視界イメージングを可能にします.

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科学分野:

  • 光学について
  • コンピュータ・ビジョン
  • コンピュータ画像

背景:

  • 伝統的な非視線画像 (NLOS) は,専門的で高価な超高速光学システムに依存しています.
  • 既存の方法はしばしば光の移動時間に依存し,アクセシビリティを制限し,コストを増やす.

研究 の 目的:

  • 新しい二次元計算ペリスコープの技術を紹介する
  • 単一の標準デジタル写真を使用して非視線画像を可能にします.

主な方法:

  • 普通のデジタルカメラから撮った写真です
  • 線形光学を用いて,物体の可視半影と隠されたシーンの間の線形依存性をモデル化します.
  • 制御された照明や時間変動の照明を必要としない.

主要な成果:

  • 不透明なオブジェクトの位置とその背後にあるシーンを成功裏に復元します.
  • 視線のない障害物の周りの非視線イメージングを達成します.
  • 普通の写真機器で 計算式ペリスコピーの実現可能性を示しています

結論:

  • この技術は低コストでアクセス可能な 非視界イメージングのソリューションを提供します.
  • 危険な環境のモニタリング,ナビゲーション,セキュリティなどの応用が可能です.
  • NLOSシナリオのハードウェア要件を簡素化することで,コンピューティングイメージングを進める.