Skin Region Extraction and Person-Independent Deformable Face Templates for Fast Video Indexing

2011 ◽  
Author(s):  
Simon Clippingdale ◽  
Mahito Fujii
Keyword(s):  
Video Indexing ◽  
Skin Region ◽  
Region Extraction

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Feature Normalization for Video Indexing and Retrieval

1996 ◽  
Author(s):  
Vikrant Kobla ◽  
David Doermann ◽  
King-Ip Lin ◽  
Christos Faloutsos
Keyword(s):  
Video Indexing ◽  
Indexing And Retrieval ◽  
Feature Normalization ◽  
Video Indexing And Retrieval

Semantic video indexing

1999 ◽  
Vol 28 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Arun Hampapur
Keyword(s):  
Video Indexing

scholarly journals Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

2021 ◽  
Author(s):  
Daichi Kitaguchi ◽  
Nobuyoshi Takeshita ◽  
Hiroki Matsuzaki ◽  
Hiro Hasegawa ◽  
Takahiro Igaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Mesorectal Excision ◽  
Rectal Wall ◽  
Learning Model ◽  
Video Indexing ◽  
Video Data ◽  
Transanal Total Mesorectal Excision ◽  
Mesorectal Excision ◽  
Purse String ◽  
Deep Learning Model

Abstract Background Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. Methods This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. Results Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. Conclusions To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

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