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.

THE DETECTION OF THE BOUNDARY OF IMAGE OF WOODANATOMICAL STRUCTURE MOLECULAR

Basing on a lot of examinations, according to the fundamental inage processing theories and methods, getting touch with the property of wood anatomical structure image,we put forward the optimum method and theory which are suitable for the binary processing of the wood anatomical structure image. After the wood image has been processed binary, with the help of computer vision technology, the boundary of wood anatomical structure molecular binary image was sought This kind of theory and method lay a solid foundaion on the collection of feature and the pottern recognition and other high level processing of wood anatomical structure molecular image.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy,a sen-sitive method for evaluating the AD has to be developed yet.Due to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the AD.Hence,en enhanced method named Enhanced Long Short Term Memory(E-LSTM)has been proposed in this work which aims atfinding the severity of AD from ocular biomarkers.Tofind the level of disease severity,the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients rapidly.To avoid the problem of overfitting,a dropout has been added to LSTM.In the existing work,boundary detection of retinal layers was found to be inaccurate during the seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been utilized.To the best of our understanding,this is thefirst paper to use Particle Swarm Optimization.When compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems.

Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter

The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out.Then the two-dimensional enhancement is performed upon the sound spectrogram according to the difference between the determinacy distribution characteristic of speech and the random distribution characteristic of noise.Finally a decision for endpoint was made by the PSSB parameter.Experimental results show that,in a low SNR environment from-10 dB to 10 dB,the algorithm proposed in this paper may achieve higher accuracy than the extant endpoint detection algorithms.The detection accuracy of 75.2%can be reached even in the extremely low SNR at-10 dB.Therefore it is suitable for speech endpoint detection in low-SNRs environment.

Exploring the tidal effect of urban business district with large-scale human mobility data

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and entertainment.Due to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business districts.Indeed,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a city.However,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business districts.To this end,we propose a data-driven and multi-dimensional framework for dynamic business district analysis.Specifically,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time periods.Then,we detect and forecast the functional changes in business districts.Experimental results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business districts.Moreover,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business districts.For example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

SOLUTE TRANSPORT IN NATURAL FRACTURES BASED ON DIGITAL IMAGE TECHNOLOGY

A method of fracture boundary extraction was developed using the Gaussian template and Canny boundary detection on the basis of the collected digital images of natural fractures. The roughness and apertures of the fractures were briefly discussed from the point of view of digital image analysis. The extracted fractured image was translated into a lattice image which can be directly used in numerical simulation. The lattice Boltzmann and modified moment propagation mixed method was then applied to the simulation of solute transport in a natural single fracture, and this mixed method could take the advantages of the lattice Boltzmann method in dealing with complex physical boundaries. The obtained concentrations was fitted with the CXTFIT2.1 code and compared with the results obtained with the commercial software Feflow. The comparison indicates that the simulation using the mixed method is sound.