Feature vector clustering molecular pairs in computer simulations

Color is the most widely used visual feature in content based image retrieval. The visual coherence color space, HSV, is adopted to represent image. Hue component is used to denote color. Hue difference statistic is proposed to extract color change information as supplement to color feature. The image is divided into sub images equally. Color and change information is extracted in each region. After feature vector clustering and coding, image content can be expressed as vector codes. The text based analysis technology is used for image retrieval. Experiments show that the proposed method can realize efficient retrieval for unconstrained scene images.

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Video Summarization Using Feature Vector Clustering

SSRN Electronic Journal ◽

10.2139/ssrn.3734732 ◽

2020 ◽

Author(s):

Yash Dhamecha ◽

Swanand Gadekara ◽

Sai Deshmukh ◽

Yashodhara Haribhakta

Keyword(s):

Feature Vector ◽

Video Summarization ◽

Vector Clustering

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A comparison of three feature vector clustering procedures in a speech recognition paradigm

10.1109/icassp.1983.1172085 ◽

2005 ◽

Cited By ~ 4

Author(s):

L. Niles ◽

H. Silverman ◽

N. Dixon

Keyword(s):

Speech Recognition ◽

Feature Vector ◽

Vector Clustering

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HYPERSPECTRAL IMAGE BAND SELECTION BASED ON SUBSPACE CLUSTERING

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v8.i8.2021.1014 ◽

2021 ◽

Vol 8 (8) ◽

pp. 42-51

Author(s):

Zhi-jun ZHENG ◽

Yan-bin PENG

Keyword(s):

Land Cover ◽

Hyperspectral Image ◽

Feature Vector ◽

Computation Time ◽

Subspace Clustering ◽

Small Sample ◽

Hyperspectral Data ◽

Band Selection ◽

Svm Classifier ◽

Vector Clustering

Aiming at the problems in hyperspectral image classification, such as high dimension, small sample and large computation time, this paper proposes a band selection method based on subspace clustering, and applies it to hyperspectral image land cover classification. This method considers each band image as a feature vector, clustering band images using subspace clustering method. After that, a representative band is selected from each cluster. Finally feature vector is formed on behalf of the representative bands, which completes the dimension reduction of hyperspectral data. SVM classifier is used to classify the new generated sample points. Experimental data show that compared with other methods, the new method effectively improves the accuracy of land cover recognition.

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A High Resolution Study of G.P. Zones in Aluminum - 4.2 at % Silver

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100055904 ◽

1982 ◽

Vol 40 ◽

pp. 722-723 ◽

Cited By ~ 1

Author(s):

R. Gronsky

Keyword(s):

Electron Microscopy ◽

Transmission Electron Microscopy ◽

High Resolution ◽

Computer Simulations ◽

Structural Characteristics ◽

X Ray Diffraction ◽

X Ray ◽

Transmission Electron ◽

Resolution Study

The phenomenon of clustering in Al-Ag alloys has been extensively studied since the early work of Guinierl, wherein the pre-precipitation state was characterized as an assembly of spherical, ordered, silver-rich G.P. zones. Subsequent x-ray and TEM investigations yielded results in general agreement with this model. However, serious discrepancies were later revealed by the detailed x-ray diffraction - based computer simulations of Gragg and Cohen, i.e., the silver-rich clusters were instead octahedral in shape and fully disordered, atleast below 170°C. The object of the present investigation is to examine directly the structural characteristics of G.P. zones in Al-Ag by high resolution transmission electron microscopy.

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Multislice calculations for quasi-periodic and non-periodic objects

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100173820 ◽

1990 ◽

Vol 48 (4) ◽

pp. 138-139

Author(s):

R. Herrera ◽

A. Gómez

Keyword(s):

Electron Diffraction ◽

Computer Simulations ◽

Periodic Table ◽

Amorphous Materials ◽

Space Group ◽

Essential Step ◽

Shape Effects ◽

Atomic Scattering ◽

Diffraction Patterns ◽

Periodic Continuation

Computer simulations of electron diffraction patterns and images are an essential step in the process of structure and/or defect elucidation. So far most programs are designed to deal specifically with crystals, requiring frequently the space group as imput parameter. In such programs the deviations from perfect periodicity are dealt with by means of “periodic continuation”.However, for many applications involving amorphous materials, quasiperiodic materials or simply crystals with defects (including finite shape effects) it is convenient to have an algorithm capable of handling non-periodicity. Our program “HeGo” is an implementation of the well known multislice equations in which no periodicity assumption is made whatsoever. The salient features of our implementation are: 1) We made Gaussian fits to the atomic scattering factors for electrons covering the whole periodic table and the ranges [0-2]Å−1 and [2-6]Å−1.

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Computer simulations for the TEM bend contour technique

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100163137 ◽

1996 ◽

Vol 54 ◽

pp. 134-135

Author(s):

Vladimir Yu. Kolosov ◽

Anders R. Thölén

Keyword(s):

Computer Simulations ◽

Real Space ◽

Advantages And Disadvantages ◽

Short Overview ◽

Diffraction Contrast ◽

Beam Method ◽

Convergent Beam ◽

Contour Technique

In this paper we give a short overview of two TEM applications utilizing the extinction bend contour technique (BC) giving the advantages and disadvantages; especially we consider two areas in which the BC technique remains unique. Special attention is given to an approach including computer simulations of TEM micrographs.BC patterns are often observed in TEM studies but are rarely exploited in a serious way. However, this type of diffraction contrast was one of the first to be used for analysis of imperfections in crystalline foils, but since then only some groups have utilized the BC technique. The most extensive studies were performed by Steeds, Eades and colleagues. They were the first to demonstrate the unique possibilities of the BC method and named it real space crystallography, which developed later into the somewhat similar but more powerful convergent beam method. Maybe, due to the difficulties in analysis, BCs have seldom been used in TEM, and then mainly to visualize different imperfections and transformations.

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