Selection of the efficient video data processing strategy based on the analysis of statistical digital images characteristics

2018 ◽  
Vol 91 (3) ◽  
pp. 107-114
Author(s):  
Mykhailo Palamar ◽  
Myroslava Yavorska ◽  
Mykhailo Strembitskyi ◽  
Volodymyr Strembitskyi
Keyword(s):  
Data Processing ◽  
Digital Images ◽  
Video Data ◽  
Processing Strategy ◽  
Efficient Video ◽  
Selection Of

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

scholarly journals Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

2020 ◽  
Vol 7 (4) ◽  
pp. 745
Author(s):  
Rizka Indah Armianti ◽  
Achmad Fanany Onnilita Gaffar ◽  
Arief Bramanto Wicaksono Putra
Keyword(s):  
Feature Extraction ◽  
Video Data ◽  
Clustering Method ◽  
Data Frame ◽  
First Order ◽  
Feature Extraction And Selection ◽  
Different Shapes ◽  
Frame Set ◽  
Selection Of

<p class="Abstrak">Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap <em>frame</em>. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap <em>frame-</em>nya. <em>Frame</em> yang memiliki pola terbaik diantara <em>frame</em> lainnya disebut <em>frame</em> dominan. Penelitian ini bertujuan untuk menyeleksi <em>frame</em> dominan dari rangkaian <em>frame</em> dengan menerapkan metode K-means <em>clustering</em> untuk memperoleh <em>centroid</em> dominan (<em>centroid</em> dengan nilai tertinggi) yang digunakan sebagai dasar seleksi <em>frame</em> dominan. Dalam menyeleksi <em>frame</em> dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu <em>Mean</em>. Data hasil ekstraksi ciri berjumlah 93 data <em>frame</em> yang selanjutnya dikelompokkan menjadi 3 <em>cluster</em> menggunakan metode K-Means. Dari hasil <em>clustering</em>, <em>centroid</em> dominan terletak pada <em>cluster</em> 3 dengan nilai <em>centroid</em> 0.0177 dan terdiri dari 41 data <em>frame</em>. Selanjutnya diukur jarak kedekatan seluruh data <em>cluster</em> 3 terhadap <em>centroid</em>, data yang memiliki jarak terdekat dengan <em>centroid</em> itulah <em>frame</em> dominan. Hasil seleksi <em>frame</em> dominan ditunjukkan pada jarak antar <em>centroid</em> dengan anggota <em>cluster</em>, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai  0.0010 yang dimiliki oleh <em>frame</em> ke-59, ke-36 dan ke-35.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p><p> </p>

scholarly journals Layer contour characterization in additive manufacturing through image binarization

2021 ◽  
Vol 1193 (1) ◽  
pp. 012067
Author(s):  
D Blanco ◽  
A Fernández ◽  
P Fernández ◽  
B J Álvarez ◽  
F Peña
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Threshold Value ◽  
Image Binarization ◽  
Actual Contour ◽  
Filter Size ◽  
Characterization Procedure ◽  
Flat Bed Scanner ◽  
Selection Of ◽  
Excellent Coverage

Abstract On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

scholarly journals Selection of Significant Features of Objects in the Classification Data Processing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3790-3794 ◽  
Keyword(s):  
Data Processing ◽  
Original Description ◽  
Classification Problems ◽  
Characteristic Space ◽  
Initial Description ◽  
Two Stages ◽  
Selection Of

The formation of a characteristic space in classification problems can be divided into two stages: the choice of the initial description of objects and the formation of an informative description of objects on the basis of a reduction in the dimension of the space of the original description

Data Processing and Exchange Challenges in Video-Based Wireless Sensor Networks

2014 ◽  
pp. 196-215
Author(s):  
Dan Pescaru ◽  
Daniel-Ioan Curiac
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Processing ◽  
Data Exchange ◽  
Video Data ◽  
Wireless Sensor ◽  
Localization Algorithms ◽  
Innovative Solutions ◽  
Communication Efficiency ◽  
Specific Localization

This chapter presents the main challenges in developing complex systems built around the core concept of Video-Based Wireless Sensor Networks. It summarizes some innovative solutions proposed in scientific literature on this field. Besides discussion on various issues related to such systems, the authors focus on two crucial aspects: video data processing and data exchange. A special attention is paid to localization algorithms in case of random deployment of nodes having no specific localization hardware installed. Solutions for data exchange are presented by highlighting the data compression and communication efficiency in terms of energy saving. In the end, some open research topics related with Video-Based Wireless Sensor Networks are identified and explained.

scholarly journals Methodology of machine destruction testing

2018 ◽  
Vol 182 ◽  
pp. 01007
Author(s):  
Vladimir Boykov ◽  
Aleksandr Povarecho
Keyword(s):  
Data Processing ◽  
Current Knowledge ◽  
Good Prognosis ◽  
Vibration Method ◽  
Diagnostic Parameters ◽  
Operation And Maintenance ◽  
Technical Systems ◽  
Degradation Degree ◽  
Automated Procedures ◽  
Selection Of

This paper presents selected problems connected with automation of procedures involved in assessment of machine degradation degree using vibration method with special emphasis on the machine state prognosis. The current knowledge of these problems is not sufficient and needs further research on data processing, analysis of efficiency of diagnostic and prognostic procedures, collection and selection of diagnostic parameters and development of automatic procedures for recognition and prognosis of a machine state. New solutions and different aspects of diagnostic prognosis based on the proposed partial procedures focus on factors determining automation of procedures for identification of technical systems states. New automated procedures for acquisition and processing of symptoms indicating the machine state provide better possibilities of control and supervision of technical systems operation and maintenance through identification of their current states, and its good prognosis.

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