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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and the...
Chromatographic Resolution01:15

Chromatographic Resolution

In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...
IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...

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Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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Published on: June 18, 2021

Shape recognition with spectral distances.

Michael M Bronstein1, Alexander M Bronstein

  • 1Institute of Computational Science, Faculty of Informatics, Università della Svizzera Italiana, Lugano 6900, Switzerland. michael.bronstein@usi.ch

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 8, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces spectral shape distance, a new framework for comparing shapes based on diffusion geometry. It unifies existing methods for nonrigid shape analysis and distribution-based shape similarity.

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Area of Science:

  • Computer Vision
  • Geometric Deep Learning
  • Pattern Recognition

Background:

  • Diffusion geometry is increasingly used in pattern recognition.
  • Nonrigid shape analysis is a key application area.
  • Existing shape similarity methods lack a unified framework.

Purpose of the Study:

  • Introduce spectral shape distance as a general framework.
  • Demonstrate its applicability to distribution-based shape similarity.
  • Unify existing shape similarity techniques.

Main Methods:

  • Developed a novel framework based on diffusion geometry.
  • Utilized spectral properties for shape comparison.
  • Showcased existing methods as special cases of the new framework.

Main Results:

  • Spectral shape distance provides a general approach.
  • Established connections between diffusion geometry and shape similarity.
  • Unified Rustamov and Mahmoudi's and Sapiro's methods.

Conclusions:

  • Spectral shape distance offers a powerful tool for nonrigid shape analysis.
  • The framework generalizes distribution-based shape similarity.
  • Provides a theoretical foundation for comparing complex shapes.