Video Script Identification Using a Combination of Textural Features

2015 ◽  
Author(s):  
Zumra Malik ◽  
Ali Mirza ◽  
Akram Bennour ◽  
Imran Siddiqi ◽  
Chawki Djeddi
Keyword(s):  
Textural Features ◽  
Script Identification

Metode Diplomatik dalam Mengidentifikasi Kandungan Isi Naskah Gulungan Berbahan Daluang Koleksi Candi Cangkuang

Panggung ◽  
2012 ◽  
Vol 22 (4) ◽  
Author(s):  
Tedi Permadi
Keyword(s):  
Direct Observation ◽  
Relevant Literature ◽  
Script Identification ◽  
Identification Techniques

ABSTRACTThis paper presents the results of the identification of rolled manuscripts made of daluang using diplomatic method. This method aims at getting the authenticity of the script based on the information that accompanies the text with the internal evidence contained in the manuscript. In terms of script identification techniques, diplomatic method utilizes direct observation techniques, assisted by other descriptions of contemporary manuscript as an evidence and support of the relevant literature. The use of diplomatic method in identifying rolled manuscripts produces the characteristics of the material, the literacy/language used in the text, and the editorial lapses contained in the text, but the identity of the author or the copyist and the time of the writing or copying manuscripts could not be found.Keywords: Manuscript identification, daluang, diplomatic method ABSTRAKTulisan ini menyajikan hasil identifikasi naskah gulungan berbahan daluang dengan menggunakan metode diplomatik. Metode diplomatik bertujuan untuk mendapatkan keaslian naskah berdasarkan informasi yang ada di dalam teks dengan bukti internal yang terkandung dalam naskah tersebut. Dalam hal teknik identifikasi naskah, metode diplomatik memanfaatkan teknik observasi langsung, dibantu dengan deskripsi dari naskah kontemporer lain sebagai bukti dan pendukung literatur yang relevan. Penggunaan metode diplomatik dalam mengidentifikasi naskah gulungan menghasilkan karakteristik material, huruf/bahasa yang digunakan dalam teks, dan penyimpangan editorial yang terkandung dalam teks, tetapi tidak bisa menemukan identitas penulis atau penyalin dan waktu penulisan atau penyalinan naskah.Kata kunci: Identifikasi naskah, daluang, metode diplomatik

scholarly journals Artistic multi-script identification at character level with extreme learning machine

2020 ◽  
Vol 167 ◽  
pp. 496-505
Author(s):  
Mridul Ghosh ◽  
Himadri Mukherjee ◽  
Sk. Md. Obaidullah ◽  
K.C. Santosh ◽  
Nibaran Das ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Script Identification ◽  
Learning Machine

scholarly journals Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

2013 ◽  
Vol 54 (10) ◽  
pp. 1703-1709 ◽  
Author(s):  
N.-M. Cheng ◽  
Y.-H. Dean Fang ◽  
J. Tung-Chieh Chang ◽  
C.-G. Huang ◽  
D.-L. Tsan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Fdg Pet ◽  
Prognostic Significance ◽  
Ct Images ◽  
Textural Features ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

scholarly journals Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Zhu ◽  
Yingfan Mao ◽  
Jun Chen ◽  
Yudong Qiu ◽  
Yue Guan ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Ct Images ◽  
Textural Features ◽  
Mutation Status ◽  
Portal Venous Phase ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Venous Phase ◽  
Ct Texture Analysis

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

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