scholarly journals Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hangsheng Jiang
Keyword(s):  
Image Processing ◽  
Automatic Recognition ◽  
Target Area ◽  
Sampling Point ◽  
Average Accuracy ◽  
Candidate Target ◽  
Image Acquisition System ◽  
Goal Recognition ◽  
Depth Feature ◽  
Improved Algorithm

This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.

scholarly journals Enhanced Clean-In-Place Monitoring Using Ultraviolet Induced Fluorescence and Neural Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3742 ◽  
Author(s):  
Alessandro Simeone ◽  
Bin Deng ◽  
Nicholas Watson ◽  
Elliot Woolley
Keyword(s):  
Image Processing ◽  
Digital Camera ◽  
Computation Time ◽  
Cleaning Process ◽  
Food Manufacturing ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Potential Benefits ◽  
Image Acquisition System ◽  
Nonlinear Autoregressive

Clean-in-place (CIP) processes are extensively used to clean industrial equipment without the need for disassembly. In food manufacturing, cleaning can account for up to 70% of water use and is also a heavy user of energy and chemicals. Due to a current lack of real-time in-process monitoring, the non-optimal control of the cleaning process parameters and durations result in excessive resource consumption and periods of non-productivity. In this paper, an optical monitoring system is designed and realized to assess the amount of fouling material remaining in process tanks, and to predict the required cleaning time. An experimental campaign of CIP tests was carried out utilizing white chocolate as fouling medium. During the experiments, an image acquisition system endowed with a digital camera and ultraviolet light source was employed to collect digital images from the process tank. Diverse image segmentation techniques were considered to develop an image processing procedure with the aim of assessing the area of surface fouling and the fouling volume throughout the cleaning process. An intelligent decision-making support system utilizing nonlinear autoregressive models with exogenous inputs (NARX) Neural Network was configured, trained and tested to predict the cleaning time based on the image processing results. Results are discussed in terms of prediction accuracy and a comparative study on computation time against different image resolutions is reported. The potential benefits of the system for resource and time efficiency in food manufacturing are highlighted.

AN OBJECT LOCATION SPECIFYING METHODOLOGY USING ONE CAMERA

2001 ◽  
Vol 09 (06) ◽  
pp. 685-702 ◽  
Author(s):  
WIRAT KESRARAT ◽  
THOTSAPON SORTRAKUL
Keyword(s):  
Image Processing ◽  
Image Plane ◽  
Video Camera ◽  
Object Location ◽  
Target Point ◽  
Target Area ◽  
Digital Video Camera ◽  
The World ◽  
The Difference ◽  
Point Center

This research proposed a methodology for specifying the location of an object with image processing. The objectives of this methodology are to capture the target area, and specify the location of the object by using image. In order to locate the dropping object on the image plane efficiently, consecutive images are analyzed and a threshold operation is proposed. Because the accuracy of the dropping objects location on the difference of consecutive images image plane is usually influenced by noise. Moreover, transformation unit is adopted to map the XY coordinate on image plane into the world coordinate for an accuracy of the dropping objects position. After we get the actual XY coordinate of the dropping object, we can find the distance from the target point (center) and clock direction of the dropping object related to the center also. In addition, by using one digital video camera set on the tower and pan to capture the image on the target area to detect the dropping object from the air to the ground. It made the proposed methodology provide easier portability to detect the dropping object in any area.

scholarly journals PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

2018 ◽  
Vol 1 (3) ◽  
pp. 92
Author(s):  
Kadek Oki Sanjaya ◽  
Gede Indrawan ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
Image Processing ◽  
Detection System ◽  
High Accuracy ◽  
Special Study ◽  
Cascade Classifier ◽  
Integral Image ◽  
Main Method ◽  
Average Accuracy ◽  
Haar Features ◽  
Haar Cascade

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object

An improved algorithm of image processing technique for film thickness measurement in a horizontal stratified gas-liquid two-phase flow

2016 ◽  
Author(s):  
Hadiyan Yusuf Kuntoro ◽  
Akhmad Zidni Hudaya ◽  
Okto Dinaryanto ◽  
Akmal Irfan Majid ◽  
Deendarlianto
Keyword(s):  
Image Processing ◽  
Film Thickness ◽  
Two Phase Flow ◽  
Thickness Measurement ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Phase Flow ◽  
Two Phase ◽  
Film Thickness Measurement ◽  
Improved Algorithm

Development of Cable Inspection System Based on Image Processing

2014 ◽  
Vol 602-605 ◽  
pp. 2199-2204
Author(s):  
Huan Liu ◽  
Chao Tao Liu
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Host Computer ◽  
Inspection System ◽  
Energy Distributions ◽  
Single Motor ◽  
Image Acquisition System

A stayed cable inspection system was developed which consists of robot, host computer, cameras and image acquisition system. The robot was driven with single motor and could climb cables of various and variable diameters. Pictures of the cables’ were taken by the robot, and the defects and mars were identified automatically with image recognition. The steps of image recognition includes image de-noising, image enhancement, image segmentation, feature extraction, and recognition with the features of the images’ histogram grayscale distributions and energy distributions.

scholarly journals Experiment-Based Synthetic Structural Analysis Combining Digital Image Processing and a Strong Form Meshfree Method

2020 ◽  
Vol 10 (22) ◽  
pp. 8053 ◽  
Author(s):  
Junwon Park ◽  
Kyeong-Hwan Kim ◽  
Young-Cheol Yoon ◽  
Sang-Ho Lee
Keyword(s):  
Image Processing ◽  
Structural Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Region Of Interest ◽  
Meshfree Method ◽  
Strong Form ◽  
Bending Test ◽  
Target Area ◽  
Tracking Point

This paper presents an experiment-based synthetic structural analysis method that combines digital image processing (DIP) and the particle difference method (PDM), which is a strong form-based meshfree method. The proposed method uses images to determine the displacement of deformed specimens, interpolates the displacement onto nodes of the PDM model without meshes or grids, and calculates the kinematic variables. Furthermore, the pixel extraction method for the target area and the method of setting the region of interest for expediting DIP were used during the synthetic structural analysis. A method for effectively expanding the number of tracking points and an improved method for labeling tracking points are also presented. To verify the performance of the analysis, the experimental and numerical analysis results of a three-point bending test on a rubber beam were compared in terms of various mechanical variables as well as with the PDM results of a simulated bending test. It was found that tracking point expansion and adjusting the radius of the domain of influence are advantageous for performing an accurate calculation without losing computational efficiency. It was demonstrated that the synthetic structural analysis effectively overcomes the shortcomings of the conventional experiments and the limitations of pure simulations.

Study on Key Techniques of Image Processing and Automatic Recognition of Tunnel Cracks

ICLEM 2012 ◽  
2012 ◽  
Author(s):  
Lixiang Zhao ◽  
Lifei Niu
Keyword(s):  
Image Processing ◽  
Automatic Recognition ◽  
Key Techniques

scholarly journals Digital Image Based Identification of Rice Variety Using Image Processing and Neural Network

2015 ◽  
Vol 16 (1) ◽  
pp. 182
Author(s):  
Lilik Sumaryanti ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Classification Accuracy ◽  
Rice Variety ◽  
Texture Features ◽  
Rice Varieties ◽  
Food Grains ◽  
Average Accuracy ◽  
Neural Network Algorithm

The increased of consumer concern on the originality of rice  variety and the quality of rice leads to originality certification of rice by existing institutions. Technology helps human to perform evaluations of food grains using images of objects. This study developed a system used as a tool to identify rice varieties. Identification process was performed by analyzing rice images using image processing. The analyzed features for identification consisted of six color features, four morphological features, and two texture features. Classifier used LVQ neural network algorithm. Identification results using a combination of all features gave average accuracy of 70,3% with the highest classification accuracy level of 96,6% for Mentik Wangi and the lowest classification accuracy of 30%  for Cilosari.

scholarly journals An Automatic Recognition Method for Students’ Classroom Behaviors Based on Image Processing

2020 ◽  
Vol 37 (3) ◽  
pp. 503-509
Author(s):  
Wei Huang ◽  
Ning Li ◽  
Zhijun Qiu ◽  
Na Jiang ◽  
Bin Wu ◽  
...  
Keyword(s):  
Image Processing ◽  
Automatic Recognition ◽  
Classroom Behaviors ◽  
Recognition Method
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