Shot Boundary Detection and Keyframe Extraction Based on Scale Invariant Feature Transform

2009 ◽  
Author(s):  
Gentao Liu ◽  
Xiangming Wen ◽  
Wei Zheng ◽  
Peizhou He
Keyword(s):  
Boundary Detection ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Keyframe Extraction ◽  
Invariant Feature ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

Video Shot Boundary Detection in Sport Video Using the Scale Invariant Feature Transform

2016 ◽  
Vol 850 ◽  
pp. 152-158
Author(s):  
Zaynab El Khattabi ◽  
Youness Tabii ◽  
Abdelhamid Benkaddour
Keyword(s):  
Boundary Detection ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Temporal Sampling ◽  
Invariant Feature ◽  
Sport Video ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

The main purpose of shot boundary detection is to detect visual content changes between consecutives frames of a video. In this paper, a new shot boundary detection algorithm is proposed based on the scale invariant feature transform (SIFT). The first stage consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. A temporal sampling period is used to avoid the frame by frame processing. The overview step provides the changes of matched features ratio all along the video. Secondly, a function is performed to detect the shot boundaries. The proposed method can be used for detecting gradual transitions as well as hard cuts and without requiring any training of the video content in advance. Experiments have been conducted on sports video and show that this algorithm achieves good results in detecting both abrupt and gradual transitions.

scholarly journals Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

2017 ◽  
Vol 7 (5) ◽  
pp. 2565 ◽  
Author(s):  
Zaynab El khattabi ◽  
Youness Tabii ◽  
Abdelhamid Benkaddour
Keyword(s):  
Moving Average ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Scale Invariant Feature Transform ◽  
Shot Boundary Detection ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Shot Boundary ◽  
Feature Transform ◽  
Scale Invariant Feature

<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.</p>

Implementation of SIFT for detection of electronic waste

2018 ◽  
Vol 7 (2.8) ◽  
pp. 353
Author(s):  
A Roshna Meeran ◽  
V Nithya
Keyword(s):  
Image Processing ◽  
Electronic Waste ◽  
Scale Invariant Feature Transform ◽  
Recycling Process ◽  
Scale Invariant ◽  
Object Matching ◽  
Invariant Feature ◽  
Detection And Identification ◽  
Feature Transform ◽  
Scale Invariant Feature

The paper focuses on the investigation of image processing of Electronic waste detection and identification in recycling process of all Electronic items. Some of actually collected images of E-wastes would be combined with other wastes. For object matching with scale in-variance the SIFT (Scale -Invariant- Feature Transform) is applied. This method detects the electronic waste found among other wastes and also estimates the amount of electronic waste detected the give set of wastes. The detection of electronics waste by this method is most efficient ways to detect automatically without any manual means.

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