Automatic model-based semantic object extraction algorithm

2001 ◽  
Vol 11 (10) ◽  
pp. 1073-1084 ◽  
Author(s):  
Jianping Fan ◽  
Xingquan Zhu ◽  
Lide Wu
Keyword(s):  
Object Extraction ◽  
Model Based ◽  
Extraction Algorithm ◽  
Semantic Object

Model-based semantic object extraction for content-based video representation and indexing

2001 ◽  
Author(s):  
Jianping Fan ◽  
David K. Y. Yau ◽  
Mohand-Said Hacid ◽  
Ahmed K. Elmagarmid
Keyword(s):  
Object Extraction ◽  
Video Representation ◽  
Model Based ◽  
Semantic Object

KPCA for semantic object extraction in images

2008 ◽  
Vol 41 (10) ◽  
pp. 3244-3250 ◽  
Author(s):  
Jing Li ◽  
Xuelong Li ◽  
Dacheng Tao
Keyword(s):  
Object Extraction ◽  
Semantic Object

A Hybrid Model for Emotion Detection from Text

2017 ◽  
Vol 7 (1) ◽  
pp. 32-48 ◽  
Author(s):  
Samar Fathy ◽  
Nahla El-Haggar ◽  
Mohamed H. Haggag
Keyword(s):  
Semantic Similarity ◽  
Hybrid Model ◽  
Semantic Analysis ◽  
Main Idea ◽  
Object Extraction ◽  
Emotion Detection ◽  
Suggested Approach ◽  
Matching Process ◽  
Extraction Algorithm ◽  
Input Sentence

Emotions can be judged by a combination of cues such as speech facial expressions and actions. Emotions are also articulated by text. This paper shows a new hybrid model for detecting emotion from text which depends on ontology with keywords semantic similarity. The text labelled with one of the six basic Ekman emotion categories. The main idea is to extract ontology from input sentences and match it with the ontology base which created from simple ontologies and the emotion of each ontology. The ontology extracted from the input sentence by using a triplet (subject, predicate, and object) extraction algorithm, then the ontology matching process is applied with the ontology base. After that the emotion of the input sentence is the emotion of the ontology which it matches with the highest score of matching. If the extracted ontology doesn't match with any ontology from the ontology base, then the keyword semantic similarity approach used. The suggested approach depends on the meaning of each sentence, the syntax and semantic analysis of the context.

Novel tracking-based moving object extraction algorithm

2002 ◽  
Vol 11 (3) ◽  
pp. 393 ◽  
Author(s):  
Jianping Fan ◽  
Essam A. El-Kwae ◽  
Mohand-Said Hacid ◽  
Feng Liang
Keyword(s):  
Moving Object ◽  
Object Extraction ◽  
Extraction Algorithm

Research on TV Goniometer Object Extraction Algorithm Based on Threshold Segmentation

2014 ◽  
Vol 889-890 ◽  
pp. 1093-1098
Author(s):  
He Chen ◽  
Nan Li ◽  
Tian Chen Huang ◽  
Rong Xia Duan
Keyword(s):  
Optimal Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Field Of View ◽  
Object Extraction ◽  
Gray Level ◽  
Line Extraction ◽  
Standard Signal ◽  
Extraction Algorithm ◽  
Target Extraction

In the TV goniometer detection system, to play the signal and field of view points line extraction is a key link in the process of parameter detection. Combination of target processing requirements, this article will target extraction algorithm based on gray level threshold and edge detection algorithm is studied, and through the experimental analysis to select the optimal algorithm was applied to the detection of TV goniometer; According to the characteristics of the standard signal and view points, lines, and put forward the corresponding methods of target recognition, and is verified through experiments

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