scholarly journals Video Segmentation and Structuring for Indexing Applications

2011 ◽  
Vol 2 (4) ◽  
pp. 38-58 ◽  
Author(s):  
Ruxandra Tapu ◽  
Titus Zaharia
Keyword(s):  
Extraction Method ◽  
Video Segmentation ◽  
Computationally Efficient ◽  
Interest Points ◽  
Grouping Strategy ◽  
Color Histograms ◽  
Different Types ◽  
Temporal Video Segmentation ◽  
Shot Boundary ◽  
Complete Framework

This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot, the authors construct a static storyboard by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing techniques for similar performances. Finally, the authors propose a shot grouping strategy that iteratively clusters visually similar shots under a set of temporal constraints. Two different types of visual features are exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.

scholarly journals Video Segmentation and Structuring for Indexing Applications

2013 ◽  
pp. 205-225
Author(s):  
Ruxandra Tapu ◽  
Titus Zaharia
Keyword(s):  
Extraction Method ◽  
Video Segmentation ◽  
Shot Boundary Detection ◽  
Computationally Efficient ◽  
Interest Points ◽  
Grouping Strategy ◽  
Color Histograms ◽  
Temporal Video Segmentation ◽  
Shot Boundary ◽  
Complete Framework

This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot, the authors construct a static storyboard by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing techniques for similar performances. Finally, the authors propose a shot grouping strategy that iteratively clusters visually similar shots under a set of temporal constraints. Two different types of visual features are exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.

scholarly journals An Efficient and Robust Temporal Video Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 4332-4337
Keyword(s):  
Video Segmentation ◽  
Video Retrieval ◽  
Boundary Detection ◽  
Detection Methods ◽  
Shot Boundary Detection ◽  
Feature Descriptor ◽  
Local Contrast ◽  
Temporal Video Segmentation ◽  
Shot Boundary ◽  
Content Based Video Retrieval

Temporal video segmentation is the primary step of content based video retrieval. The whole processes of video management are coming under the focus of content based video retrieval, which includes, video indexing, video retrieval, and video summarization etc. In this paper, we proposed a computationally efficient and discriminating shot boundary detection method, which uses a local feature descriptor named local Contrast and Ordering (LCO) for feature extraction. The results of the experiments, which are conducted on the video dataset TRECVid, analyzed and compared with some existing shot boundary detection methods. The proposed method has given a promising result, even in the cases of illumination changes, rotated images etc.

scholarly journals Temporal Video Segmentation Using Optical Flow Estimation

2021 ◽  
pp. 4181-4194
Author(s):  
Eman Hato
Keyword(s):  
Optical Flow ◽  
Video Segmentation ◽  
Computational Cost ◽  
Superior Performance ◽  
Detection Accuracy ◽  
Motion Feature ◽  
Detection Process ◽  
Shot Detection ◽  
Temporal Video Segmentation ◽  
Shot Boundary

Shot boundary detection is the process of segmenting a video into basic units known as shots by discovering transition frames between shots. Researches have been conducted to accurately detect the shot boundaries. However, the acceleration of the shot detection process with higher accuracy needs improvement. A new method was introduced in this paper to find out the boundaries of abrupt shots in the video with high accuracy and lower computational cost. The proposed method consists of two stages. First, projection features were used to distinguish non boundary transitions and candidate transitions that may contain abrupt boundary. Only candidate transitions were conserved for next stage. Thus, the speed of shot detection was improved by reducing the detection scope. In the second stage, the candidate segments were refined using motion feature derived from the optical flow to remove non boundary frames. The results manifest that the proposed method achieved excellent detection accuracy (0.98 according to F-Score) and effectively speeded up detection process. In addition, the comparative analysis results confirmed the superior performance of the proposed method versus other methods.

Shot boundary detection and label propagation for spatio-temporal video segmentation

2015 ◽  
Author(s):  
Sankaranaryanan Piramanayagam ◽  
Eli Saber ◽  
Nathan D. Cahill ◽  
David Messinger
Keyword(s):  
Video Segmentation ◽  
Boundary Detection ◽  
Label Propagation ◽  
Shot Boundary Detection ◽  
Temporal Video Segmentation ◽  
Shot Boundary ◽  
Spatio Temporal

scholarly journals Stiffness Modeling of Robotic-Manipulators Under Auxiliary Loadings

2012 ◽  
Author(s):  
Alexandr Klimchik ◽  
Anatol Pashkevich ◽  
Stéphane Caro ◽  
Damien Chablat
Keyword(s):  
Robotic Manipulators ◽  
Computationally Efficient ◽  
End Effector ◽  
Efficient Procedure ◽  
Stiffness Modeling ◽  
Cartesian Stiffness Matrix ◽  
Different Types ◽  
Mapping Equation ◽  
Linear Force ◽  
Cartesian Stiffness

The paper focuses on the extension of the virtual-joint-based stiffness modeling technique for the case of different types of loadings applied both to the robot end-effector and to manipulator intermediate points (auxiliary loading). It is assumed that the manipulator can be presented as a set of compliant links separated by passive or active joints. It proposes a computationally efficient procedure that is able to obtain a non-linear force-deflection relation taking into account the internal and external loadings. It also produces the Cartesian stiffness matrix. This allows to extend the classical stiffness mapping equation for the case of manipulators with auxiliary loading. The results are illustrated by numerical examples.

VIDEO CONTENT REPRESENTATION FOR SHOT RETRIEVAL AND SCENE EXTRACTION

2001 ◽  
Vol 01 (03) ◽  
pp. 507-526 ◽  
Author(s):  
TONG LIN ◽  
HONG-JIANG ZHANG ◽  
QING-YUN SHI
Keyword(s):  
Similarity Measure ◽  
Video Content ◽  
Object Based ◽  
Scene Structure ◽  
Color Histograms ◽  
Extraction Algorithm ◽  
Content Representation ◽  
Video Shot ◽  
Shot Boundary ◽  
Spatio Temporal

In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure that serves to shot retrieval. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experimental results on real-world sports video prove that our proposed approach for video shot retrievals achieve the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR), and Experiment on MPEG-7 test videos achieves promising results by the proposed scene extraction algorithm.

Temporal video segmentation: A survey

2001 ◽  
Vol 16 (5) ◽  
pp. 477-500 ◽  
Author(s):  
Irena Koprinska ◽  
Sergio Carrato
Keyword(s):  
Video Segmentation ◽  
Temporal Video Segmentation

Methods for nematode extraction.

2021 ◽  
pp. 12-41
Author(s):  
Nicole Viaene ◽  
Johannes Hallmann ◽  
Leendert P. G. Molendijk
Keyword(s):  
Sample Size ◽  
Water Consumption ◽  
Extraction Method ◽  
Extraction Efficiency ◽  
Plant Parts ◽  
Maximum Sample Size ◽  
Different Types ◽  
Extraction Equipment ◽  
Method Factors

Abstract Nematodes can be present in different matrices. This chapter describes several methods to extract nematodes from soil and plant parts. It is crucial that an appropriate method is chosen for the purpose of the research as different types of nematodes, and even different nematode stages, are extracted depending on the method. Factors to consider for choosing the optimal extraction method are the extraction efficiency of the method, the maximum sample size that can be analysed and costs of the extraction equipment. In addition, water consumption, labour and the time needed before nematodes can be examined can be important factors.

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