scholarly journals Research on Sports Training Action Image Recognition Based on SDN

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Dianhai Wang ◽  
Lianmei Shen
Keyword(s):  
Image Denoising ◽  
Image Recognition ◽  
Network Architecture ◽  
Image Data ◽  
Image Features ◽  
Test Results ◽  
Application Layer ◽  
Sports Training ◽  
Detection Range ◽  
Image Edge

Current image recognition methods cannot combine the transmission of image data with the interaction of image features, so the steps of image recognition are too independent, and the traditional methods take longer time and cannot complete the image denoising. Therefore, a recognition method of sports training action image based on software defined network (SDN) architecture is proposed. The SDN architecture is used to integrate the image data transmission and interactive process and to optimize the image processing centralization. The network architecture is composed of application layer, control layer, and infrastructure layer. Based on this, the dimension of image sample set is reduced, and the edge detection operator in any direction is constructed. The image edge filter is realized by calculating the response and threshold of image edge by using lag threshold and nonmaximum suppression (NMS). The Hough transform algorithm is improved to optimize the detection range. Extracting the neighborhood feature of sports training action, the recognition of sports training action image based on SDN architecture is completed. Simulation results show that the proposed method takes less time and the image denoising effect is better. In addition, the F1 test results of the proposed method are higher than those of the literature, and the convergence is better. Therefore, the performance of the proposed method is better.

scholarly journals Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2203
Author(s):  
Antal Hiba ◽  
Attila Gáti ◽  
Augustin Manecy
Keyword(s):  
Optical Sensors ◽  
Inertial Sensor ◽  
Image Data ◽  
Flight Test ◽  
Image Features ◽  
Wide Field ◽  
Test Results ◽  
Wide Field Of View ◽  
Final Approach ◽  
Marker Detection

Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors.

CUDA-Based Parallel Prewitt Algorithm Implementation and its Application on GPR

2013 ◽  
Vol 380-384 ◽  
pp. 4002-4006 ◽  
Author(s):  
Tu You Peng ◽  
Yang Zhao
Keyword(s):  
High Performance ◽  
Field Data ◽  
High Efficiency ◽  
Target Identification ◽  
Image Data ◽  
Image Features ◽  
Important Means ◽  
Image Edge ◽  
Computationally Intensive ◽  
Algorithm Implementation

The edge of image is one of the important features of the image, edge detection is an important means to extract image features. As the most popular high-performance processing technology, GPU parallel technology is on of the best choices for parallel Prewitt algorithm implementation. Since conventional Prewitt algorithm based upon CPU is computationally intensive, time-consuming, its application is very restricted. In order to improve the efficiency of Prewitt algorithm, CUDA-based parallel Prewitt algorithm and fast imaging algorithm are applied to get higher speedup. Finally, an effective method is proposed by turning the GPR field data into gray-scale image data, then implementation of GPR field data processing with the Prewitt algorithm based upon CUDA. Numerical results on GPR field data have shown that the algorithm is not only of high efficiency, but effective to improve target identification capability based upon GPR.

scholarly journals Pengenalan Citra Tanda Tangan Menggunakan Metode 2D-LDA dan Euclidean Distance

2016 ◽  
Vol 3 (4) ◽  
pp. 269
Author(s):  
Danar Putra Pamungkas ◽  
Fajar Rohman Hariri
Keyword(s):  
Computer System ◽  
Image Recognition ◽  
Euclidean Distance ◽  
Image Data ◽  
Image Features ◽  
Recognition Method ◽  
Distance Method ◽  
Matching Process ◽  
Signature Matching ◽  
Average Accuracy

Kelemahan sistem manual dalam identifikasi tanda tangan adalah si pemeriksa tanda tangan harus teliti dalam melakukan pencocokan untuk menghindari kesalahan. Oleh karena itu untuk mengatasi kelemahan pencocokan tanda tangan secara manual, proses pencocokan tanda tangan perlu dilakukan secara otomastis dengan sistem komputer sehingga diharapkan mempermudah dalam identifikasi tanda tangan seseorang. Pada penelitian ini peneliti menggunakan metode 2DLDA dan Euclidean Distance untuk pengenalan tanda tangan dengan sistem komputer. Metode 2DLDA untuk ektraksi fitur citra tanda tangan dan metode Euclidean Distance untuk klasifikasi citra tanda tangan. Data citra tanda tangan yang digunakan berukuran 50x50, 100x100, 150x150, 200x200 dan 250x250 piksel. Hasil dari uji coba penelitian ini adalah akurasi pengenalan citra tanda tangan menggunakan metode 2DLDA mencapai 88% dan rata-rata akurasi 81%. Akurasi optimal pengenalan citra tanda tangan dengan metode 2DLDA terjadi pada penggunaan data citra berukuran 50x50 piksel dengan akurasi 88% dan kecepatan 0.20126 detik.The manual system in the identification of the examiner's signature is the signature must be meticulous in doing matching to avoid mistakes. Therefore, to overcome the disadvantages of signature matching manually, signature matching process needs to be done should automatically with a computer system that is expected to facilitate the identification of a person's signature. In this study, researchers used a method 2DLDA and Euclidean Distance to the introduction of a signature with a computer system. 2DLDA methods to extract image features the signature and Euclidean Distance method for image classification signature. The image data signature used measuring 50x50, 100x100, 150x150, 200x200 and 250x250 pixels. The results of this research trial is a signature image recognition accuracy using 2DLDA reaches 88% and an average accuracy of 81%. Optimum accuracy signature image recognition method 2DLDA occurs in the use of image data size of 50x50 pixels with 88% accuracy and speed 0.20126 seconds

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

scholarly journals Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
Alexey Semenkov ◽  
Dmitry Bragin ◽  
Yakov Usoltsev ◽  
Anton Konev ◽  
Evgeny Kostuchenko
Keyword(s):  
Image Recognition ◽  
Random Number ◽  
Statistical Test ◽  
The Other ◽  
Test Results ◽  
Random Generation ◽  
Facial Image ◽  
Random Number Generators ◽  
Graphic Image ◽  
High Level

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

scholarly journals Creating Color Image Features Using Local Contrast Method

2018 ◽  
Vol 7 (3) ◽  
pp. 367-376
Author(s):  
Ayman Al-Rawashdeh ◽  
Ziad Al-Qadi
Keyword(s):  
Image Recognition ◽  
Color Image ◽  
Digital Processing ◽  
Image Features ◽  
Local Contrast ◽  
Image Size ◽  
Data Types ◽  
Memory Space ◽  
Contrast Method ◽  
Image Pixels

Digital color images are now one of the most popular data types used in the digital processing environment. Color image recognition plays an important role in many vital applications, which makes the enhancement of image recognition or retrieval system an important issue. Using color image pixels to recognize or retrieve the image, but the issue of the huge color image size that requires accordingly more time and memory space to perform color image recognition and/or retrieval. In the current study, image local contrast was used to create local contrast victor, which was then used as a key to recognize or retrieve the image. The proposed local contrast method was properly implemented and tested. The obtained results proved its efficiency as compared with other methods.

scholarly journals Design of multilayer microfluidic paper chip and its visual detection of pesticide residues

2020 ◽  
Vol 11 ◽  
pp. 170-182
Author(s):  
Li zhi Li ◽  
Han Ping Mao
Keyword(s):  
Pesticide Residues ◽  
Visual Detection ◽  
Channel Structure ◽  
Test Results ◽  
Mixed Effect ◽  
Detection Range ◽  
Long Time ◽  
Paper Chip ◽  
Channel Structures ◽  
High Repeatability

With the increase of China's grain production, the use of pesticides is gradually increasing. Traditional pesticide detection takes a long time and requires expensive experimental instruments, which is not conducive to the rapid and accurate detection of pesticide residues in the field. To solve this problem, this paper proposes a visual detection method of pesticide residues based on multi-layer microfluidic paper chips. The internal channel structure of paper chip is designed from the perspective of efficient mixing. Through the simulation of the mixed effect of three kinds of staggered channel structures, which are arc type, triangle type, and ladder type, the "ladder-type h-0.3, s-2.6" is selected as the best-staggered structure, and the mixing strength is 0.91534. The best simulation structure was tested by a colored reagent, and the image processing of 15 test results was carried out with MATLAB. The average mixing strength was 0.84, and the and the standard deviation was 0.022. The visual detection experiment of acetamiprid and profenofos in cabbage samples was carried out by using the device，The detection range of acetamiprid was 4~72 μg/kg, and the detection range of profenofos was 3~54 μg/kg . The recovery of acetamiprid was 75%~85%, and the recovery of profenofos was 80%~90%. The detection range and recovery rate indicate that the device has high repeatability and accuracy in the actual sample detection

scholarly journals Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 186
Author(s):  
Fayeem Aziz ◽  
Aaron S.W. Wong ◽  
Stephan Chalup
Keyword(s):  
Manifold Learning ◽  
Network Architecture ◽  
Image Data ◽  
High Dimensional ◽  
Double Pendulum ◽  
Manifold Alignment ◽  
Series Of Experiments ◽  
Underlying Manifold ◽  
Low Dimensional ◽  
Pendulum Dynamics

The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data.

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