Expression classification of 3D faces using local deformations

2012 ◽  
Author(s):  
M.C. Ruiz ◽  
J. Illingworth
Keyword(s):  
Expression Classification ◽  
Local Deformations

scholarly journals Testing the gene expression classification of the EMT spectrum

2019 ◽  
Vol 16 (2) ◽  
pp. 025002 ◽  
Author(s):  
Dongya Jia ◽  
Jason T George ◽  
Satyendra C Tripathi ◽  
Deepali L Kundnani ◽  
Mingyang Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Classification ◽  
Expression Classification

Gene expression classification of indeterminate thyroid nodules

2015 ◽  
Vol 221 (4) ◽  
pp. e79
Author(s):  
Neil D. Saunders ◽  
Jennifer Sipos ◽  
Lawrence A. Shirley ◽  
John E. Phay
Keyword(s):  
Gene Expression ◽  
Thyroid Nodules ◽  
Gene Expression Classification ◽  
Expression Classification

scholarly journals Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value

PLoS Medicine ◽  
2013 ◽  
Vol 10 (5) ◽  
pp. e1001453 ◽  
Author(s):  
Laetitia Marisa ◽  
Aurélien de Reyniès ◽  
Alex Duval ◽  
Janick Selves ◽  
Marie Pierre Gaub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Prognostic Value ◽  
Molecular Subtypes ◽  
Gene Expression Classification ◽  
Expression Classification

scholarly journals Classification of facial expressions using SVM and HOG

2020 ◽  
Vol 3 (2) ◽  
pp. 210-215
Author(s):  
Juliansyah Putra Tanjung ◽  
Muhathir Muhathir
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Information Acquisition ◽  
Support Vector ◽  
A Value ◽  
Average Accuracy ◽  
The Face ◽  
Facial Expression Classification ◽  
Expression Classification

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

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