Video Shot Boundary Detection in MPEG Compressed Sequences Using SVM Learning

A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't accurately detected because it involves the camera operation and objectmovement.In this paper,a method based on support vector machine (SVM) is proposed to detect thedissolve shot boundary in MPEG compressed sequence.The problem of detection between the dissolveshot boundary and other boundaries is considered as two-class classification in our method.Featuresfrom the compressed sequences are directly extracted without decoding them,and the optimal classboundary between two classes are learned from training data by using SVM.Experiments,whichcompare various classification methods,show that using proposed method encourages performance ofvideo shot boundary detection.

Video Shot Boundary Detection Using Normalized Periodogram Distance Metric

Video shot boundary detection is the primary task for content based video management and retrieval system. This paper proposes a shot boundary detection strategy by exploiting the pros of Normalized Periodogram for efficiently representing the content of the video. A Normalized Periodogram based distance metric to detect the key frames using shot boundary, namely Distance- Left-Right (DLR), is addressed, which is computed on a sliding sub-window basis. The DLR sequence is used to detect the suspected shot boundary frames and a transition type detection procedure is adapted to these suspected frames for discriminating the abrupt and gradual transitions. The proposed shot boundary detection methodology yields Precision—95.02%, Recall—93.15% and F1 score—94.07% for cut, Precision—86.57%, Recall—86.67% and F1 score—86.61% for gradual, Precision—90.6%, Recall—90.02% and F1 score—90.3% for overall transitions. Experimental results show that the proposed approach is superior to the recently available shot boundary detection techniques because of its robustness and simplicity, and presents an effective distance metric to detect the shot boundary.

A unifying approach in simulating the shot peening process using a 3D random representative volume finite element model

Using a modified 3D random representative volume（RV）finite element model,the effects of model dimensions（impact region and interval between impact and representative regions）,model shapes（rectangular,square,and circular）,and peening-induced thermal softening on resultant critical quantities（residual stress,Almen intensity,coverage,and arc height）after shot peening are systematically examined.A new quantity,i.e.,the interval between impact and representative regions,is introduced and its optimal value is first determined to eliminate any boundary effect on shot peening results.Then,model dimensions are respectively assessed for all model shapes to reflect the actual shot peening process,based on which shape-independent critical shot peening quantities are obtained.Further,it is found that thermal softening of the target material due to shot peening leads to variances of the surface residual stress and arc height,demonstrating the necessity of considering the thermal effect in a constitutive material model of shot peeing.Our study clarifies some of the finite element modeling aspects and lays the ground for accurate modeling of the SP process.