Assessment of Combined Shape, Color and Textural Features for Video Duplication

HimaBindu, Gottumukkala; Anuradha, Chinta; Murty, Patnala Chandra

doi:10.18280/ts.360210

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The vehicles could be detected and tracked by sensors and video monitors. Compared with sensors, video monitors are simple and easy to update, and capable of capturing complete information [2][3][4]. The traffic videos can be processed by computer vision and pattern recognition, providing effective solutions for traffic control and planning.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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Vehicle detection and tracking are key aspects of intelligent transportation. However, the accuracy of traditional methods for vehicle detection and tracking is severely suppressed by the complexity of road conditions, occlusion, and illumination changes. To solve the problem, this paper initializes the background model based on multiple frames with a fixed interval, and then establishes a moving vehicle detection algorithm based on trust interval. The established algorithm can easily evaluate the background complexity based on regional information. After that, the authors pointed out that the correlation filtering, a classical vehicle tracking algorithm, cannot adapt to the scale changes of vehicles, due to the weak dependence of background information. Hence, a tracking algorithm that adapts to vehicle scale was designed based on background information. Finally, the proposed algorithms were proved feasible for detection and tracking moving vehicles in complex environments. The research provides a good reference for the application of computer vision in moving target detection.

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Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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“…It was suggested to add first-order texture features to the common stack, which were the following statistical characteristics [25] (calculated on differentiation matrices): Apart from GLCM, so called grey level run length matrix (GLRLM) was studied [26]. The statistical features, mentioned above, were also calculated for this matrix separately for lengths 1, 2, and 3.…”

Section: Groupmentioning

confidence: 99%

Forming the Stack of Texture Features for Liver Ultrasound Images Classification

Hrishko¹,

Настенко²,

Honcharuk³

et al. 2021

HAIT

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This article discusses the use of texture analysis methods to obtain informative features that describe the texture of liver ultrasound images. In total, 317 liver ultrasound images were analyzed, which were provided by the Institute of Nuclear Medicine and Radiation Diagnostics of NAMS of Ukraine. The images were taken by three different sensors (convex, linear, and linear sensor in increased signal level mode). Both images of patients with a normal liver condition and patients with specific liver disease (there were diseases such as: autoimmune hepatitis, Wilson's disease, hepatitis B and C, steatosis, and cirrhosis) were present in the database. Texture analysis was used for “Feature Construction”, which resulted in more than a hundred different informative features that made up a common stack. Among them, there are such features as: three authors’ patented features derived from the grey level co-occurrence matrix; features, obtained with the help of spatial sweep method (working by the principle of group method of data handling), which was applied to ultrasound images; statistical features, calculated on the images, brought to one scale with the help of differential horizontal and vertical matrices, which are proposed by the authors; greyscale pairs ensembles (found using the genetic algorithm), which identify liver pathology on images, transformed with the help of horizontal and vertical differentiations, in the best possible way. The resulting trait stack was used to solve the problem of binary classification (“norma-pathology”) of ultrasound liver images. A Machine Learning method, namely “Random Forest”, was used for this purpose. Before the classification, in order to obtain objective results, the total samples were divided into training (70 %), testing (20 %), and examining (10 %). The result was the best three Random Forest models separately for each sensor, which gave the following recognition rates: 93.4 % for the convex sensor, 92.9 % for the linear sensor, and 92 % for the reinforced linear sensor

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“…To adapt to the latest trend of the market pattern, each brand needs to rely on the new model of WeMedia marketing to expand the audience and look for new ideas and growth points for product sales [11][12][13][14][15][16][17]. Based on the theories of online marketing and online opinion leaders, Kollintza-Kyriakoulia et al [18] determined the evaluation indices for the performance of the trendy live broadcast marketing and constructed a system of 24 indices in five aspects, namely, marketing amount, after-sale service, marketing value, platform performance, and social benefit.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of WeMedia Ads Marketing Based on the Demand of Internet Economy

Bian

2021

Discrete Dynamics in Nature and Society

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To fully understand the root causes for consumer’s trust and preference of we-media marketing, it is important to explore deep into the features and superiorities of the marketing model of we-media marketing platforms, as well as the factors affecting the profit-making of the model. The existing studies on WeMedia ads marketing at home and abroad are mostly descriptive analysis on the relevant concepts, the importance of WeMedia ads marketing, product quality monitoring, and the reasonability of marketing models. There is a lack of quantitative results on deeper layers. To make up for the gap, this paper explores the evaluation of WeMedia ads marketing based on the demand of Internet economy. Firstly, an evaluation index system (EIS) was established for WeMedia ads marketing, and the weight of each index was determined in turn. Then, the authors created an evaluation model, carried out the corresponding confirmatory factor analysis (CFA), and verified the model through the degree of fit verification. Experimental results confirm the correctness and effectiveness of the constructed model. The results provide a reference for the evaluation of other marketing models.

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Assessment of Combined Shape, Color and Textural Features for Video Duplication

Cited by 6 publications

References 14 publications

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Video Sequences

Forming the Stack of Texture Features for Liver Ultrasound Images Classification

Evaluation of WeMedia Ads Marketing Based on the Demand of Internet Economy

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