A reconfigurable processor array for real-time moment-invariant feature extraction

Author(s):  
D.L. Hung ◽  
M. Kormicki ◽  
A. Schwarz ◽  
H.D. Cheng ◽  
C.Y. Wu
Kursor ◽  
2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Hendro Nugroho ◽  
Eka Prakarsa Mandyartha

In the findings of the statue of Ganesha in Trowulan Mojokerto area is no longer intact, because the statue of Ganesha is found to have been on the surface of soil or underground, so the archaeologist is very difficult to categorize the findings. This research proposes to overcome the above problems it is necessary to the Image Retrieval system (image retrieval) that can provide information about the results of the discovery of such historic objects. For the image taken as Image Retrieval as an example of research trials is the Ganesha Arca. From the Ganesha Statue is searched for feature extraction value by using Moment Invariant method, after which to get the result of image retrieval using Manhattan method. Image Retrieval information system work is image of Ganesa Arca in pre-processing with size 200x260 pixel BMP, then image in edge detection using Roberts method and extraction of Moment Invariant feature and inserted into database as data traning. For data testing the same process with data traning so searched the closest distance using Manhattan method. From the results of 15 image testing statues Ganesha level to the accuracy of the true states there is 62% and stated wrong 38%. Research can be further developed using various methods to improve image retrieval accuracy


2014 ◽  
Vol 721 ◽  
pp. 775-778 ◽  
Author(s):  
Yi Qiang Lai

In recently years, extracting images invariance features are gaining more attention in image matching field. Various types of methods have been used to match image successfully in a number of applications. But in mostly literatures, the rotation moment invariant properties of these invariants have not been studied widely. In this paper, we present a novel method based on Polar Harmonic Transforms (PHTs) which is consisted of a set of orthogonal projection bases to extract rotation moment invariant features. The experimental results show that the kernel computation of PHTs is simple and image features is extracted accurately in image matching. Hence polar harmonic transforms have provided a powerful tool for image matching.


2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.


2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.


2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.


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