scholarly journals Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image

2020 ◽  
Vol 10 (10) ◽  
pp. 3371 ◽  
Author(s):  
Jun Fu ◽  
Haikuo Yuan ◽  
Rongqiang Zhao ◽  
Zhi Chen ◽  
Luquan Ren
Keyword(s):  
Image Processing ◽  
Theoretical Basis ◽  
Detection System ◽  
Post Processing ◽  
Recognition Method ◽  
Damage Recognition ◽  
Working Parameters ◽  
Corn Kernels ◽  
Rgb Image

Corn ear damage caused by peeling significantly influence the output and quality of corn harvest. Ear damage recognition is the basis to adjust working parameters and to reduce damage. Image processing is attracting increasing attentions in the field of agriculture. Conventional image processing methods are difficult to be used for recognizing corn ear damage caused by peeling in field harvesting. To address the this problem, in this paper, we propose a peeling damage recognition method based on RGB image. For our method, we develop a dictionary-learning-based method to recognize corn kernels and a thresholding method to recognize ear damage regions. To obtain better performance, we also develop the corroding algorithm and the expanding algorithm for the post-processing of recognized results. The experimental results demonstrate the practicality and accuracy of the proposed method. This study could provide the theoretical basis to develop online peeling damage detection system for corn ear harvesters.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

scholarly journals The object detection system of balinese script on traditional Balinese manuscript with findcontours method

2021 ◽  
Vol 11 (3) ◽  
pp. 177-184
Author(s):  
Putra Manuaba ◽  
◽  
Komang Ayu Triana Indah ◽  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Edge Detection ◽  
Object Detection ◽  
Detection System ◽  
Main Issue ◽  
Age Factor

Lontar is a traditional Balinese manuscript with a Balinese script in it. Balinese traditional manuscripts can be more than 100 years old. The age factor of the Balinese manuscript has an impact on the Balinese script in it. Balinese script that has been written more than 10 years tends to be darker. This makes Balinese script not visible well, and this affects the image quality of the manuscript. This thing becomes the main issue in this research, Balinese script detection on Balinese manuscript images. the first of all is image processing using edge detection, canny and Sobel becomes the main algorithm of this process. After image processing, the Balinese manuscript will be processed with the findcontour method to detect an object that contains in it. The final process of this detection system is to separate detected objects into three main groups namely noise object, Balinese script object, and hole object. Application (Balinese script object detection system) is more accurate in detecting Balinese script objects in Balinese script under 1 year (new script), it tends to be more likely to find noise/dirt. This is because the writing of the lontar using a pencil first before using the knife media. This adds to the noise or dirt detected by the application The findcontour method can detect Balinese script objects with a detection result of 30% - 70% Balinese script objects.

scholarly journals Sistem Deteksi Cepat Mutu Organoleptik Beras Berbasis Android

2020 ◽  
Vol 9 (4) ◽  
pp. 167-174
Author(s):  
Mulyana Hadipernata ◽  
Agus Supriatna Somantri ◽  
Maulida Hayuningtyas ◽  
Nikmatul Hidayah ◽  
Hoerudin Hoerudin
Keyword(s):  
Image Processing ◽  
Rapid Detection ◽  
Amylose Content ◽  
Detection System ◽  
Android Application ◽  
Rice Varieties ◽  
Organoleptic Quality ◽  
Detection Tool ◽  
Image Characteristics

Penelitian ini bertujuan untuk mengembangkan alat deteksi cepat mutu organoleptik beras berbasis pada pemanfaatan aplikasi Android agar pengujian mutu organoleptik beras dapat dilakukan secara cepat dan akurat. Bahan penelitian yang digunakan adalah beras varietas Ciherang dan Tarabas. Metode yang digunakan adalah dengan menggunakan realtime image processing berbasis Android dan Java. Hasil penelitian menunjukkan bahwa lamanya penyimpanan beras sangat mempengaruhi citra beras (Red Green Blue/RGB). Selama penyimpanan beras, nilai Blue menghasilkan nilai perubahan yang nyata dibandingkan nilai Red dan Green. Nilai Blue ini berkorelasi positif terhadap perubahan kadar amilosa selama penyimpanan dan mutu organoleptiknya. Aplikasi deteksi cepat mutu organoleptik beras juga telah berhasil dibuat dan dapat diuji validitasnya dengan memperhatikan perubahan karakateristik citra, perubahan amilosa, dan mutu organoleptiknya. Kesimpulannya, aplikasi deteksi cepat ini berhasil dikembangkan dengan berbasis Android yang dapat digunakan sebagai alat uji mutu organoleptik berasRapid Detection System for Organoleptic Quality of Rice using the Android ApplicationAbstractThe research was aimed at developing rapid detection tool of rice upon organoleptic quality based on the Android application, so the testing may be done quickly and accurately. Ciherang and Tarabas rice varieties were used in this research. Realtime image processing based on Android and Java were used as method in this research. The results showed that the storage affected the rice image value (Red Green Blue/RGB). During storage, the value of the blue (B) produced a proper marked which was positively correlated to the changes in amylose content. Application of rapid detection of organoleptic quality of rice was carried out by observing changes in image characteristics, changes in amylose, and changes in organoleptic properties. As conclusion, the application may functioning properly and can be used as a tool to test the organoleptic quality of rice and its shelf life.

scholarly journals Harumanis mango leaf disease recognition system using image processing technique

2021 ◽  
Vol 23 (1) ◽  
pp. 378
Author(s):  
R. A. JM. Gining ◽  
S. S. M. Fauzi ◽  
N. M . Yusoff ◽  
T. R. Razak ◽  
M. H. Ismail ◽  
...  
Keyword(s):  
Image Processing ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Human Errors ◽  
Leaf Disease ◽  
Rgb Image

Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

Research of Recognition Method for Surface Defects of Hot-Rolled Round Steel Based on Image Processing

2015 ◽  
Vol 1090 ◽  
pp. 84-89
Author(s):  
Chang Cheng Shao ◽  
Gong Wen Xu ◽  
Hao Xu ◽  
Ming Hai Liao ◽  
Hong Luan Zhao ◽  
...  
Keyword(s):  
Image Processing ◽  
Surface Defects ◽  
Image Noise ◽  
Recognition Method ◽  
Matlab Software ◽  
The Third ◽  
Segmented Image ◽  
Image Edge ◽  
Hot Rolled

The surface defects is an important factor affecting the quality of hot-rolled round steel, so the recognition of surface defects plays a very important role in the daily usage of the hot-rolled round steel. This paper aims to bring forward an appropriate method to find out the surface defects of the hot-rolled round steel under the help of Matlab software. First of all, the image edge of the round steel was detected, and the image was segmented. Secondly, the segmented image may appear bended, so it would be straightened to make the surface defects recognition easy. The third step is to eliminate image noise. Finally the processed image was analyzed and the appropriate recognition method was figured out. The results show that the method proposed in this paper is effective and accurate.

Design of Automatic Detection Device for Steel Bar Surface Defects

2012 ◽  
Vol 532-533 ◽  
pp. 390-393 ◽  
Author(s):  
Jian Chuan Zhang ◽  
Wu Bin Li ◽  
Chang Hou Lu
Keyword(s):  
Image Processing ◽  
Surface Quality ◽  
Automatic System ◽  
Surface Defects ◽  
Detection System ◽  
High Luminance ◽  
Linear Ccd ◽  
Steel Bar ◽  
And Performance

To inspect the surface quality of steel bar, we designed an automatic system including linear camera and laser. Through the comparison among kinds of cameras, we select linear CCD to our system. The laser is also chosen by us with its high luminance and performance. Through a series of computation, we select the appropriate camera lens to our device. At last, we draw the whole detection system. This device has been used well and provides a good foundation for prospective image processing.

scholarly journals Practical Fall Detection System using Vision and Wearable sensors

2019 ◽  
Vol 8 (4) ◽  
pp. 7968-7972
Keyword(s):  
Image Processing ◽  
Embedded System ◽  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Research Problem ◽  
Real Time System ◽  
Depth Processing ◽  
Software Modelling ◽  
Rgb Image

Fall detection is an important and challenging research problem in healthcare domain. The fall detection system required to operate and give true alert in real time. Many of the existing approaches generates false fall alert which again causes inconvenience for the end users. Hence, there is a need to have robust and accurate fall detection approach with low latency in decision making. In this work, we have proposed and evaluated three different approaches of fall detection system based on a wireless accelerometer based embedded system, RGB Image processing based Software modelling approach and Kinect based depth processing approach. These proposed approaches try to improve on the mentioned drawbacks until we obtain a robust, running in real-time system with high accuracy and low processing time. In all of the demonstrated methods, we do not require any knowledge of the scene and computationally intensive classifiers. The accelerometer based embedded system consists of economic components and is easy to setup. RGB Image processing based Software modelling is simulated on MATLAB have been extensively researched and implemented in real-time. Kinect based depth based techniques are the most recent advancement on the issue and have resolved many discrepancies of the previous methods. The performance of each method is compared against each other. It is shown that our Kinect based depth processing provides promising accuracy of 94% which is better than the other approaches while simultaneously working in real time of 30 frames/second.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

Developing an Inkjet Printer I: RGB Image to CMY Ink Amounts - Image Processing and Color Management

2020 ◽  
Vol 2020 (15) ◽  
pp. 350-1-350-10
Author(s):  
Yin Wang ◽  
Baekdu Choi ◽  
Davi He ◽  
Zillion Lin ◽  
George Chiu ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Experimental Results ◽  
Color Management ◽  
Finger Nail ◽  
Inkjet Printer ◽  
Rgb Image ◽  
Novel Algorithm

In this paper, we will introduce a novel low-cost, small size, portable nail printer. The usage of this system is to print any desired pattern on a finger nail in just a few minutes. The detailed pre-processing procedures will be described in this paper. These include image processing to find the correct printing zone, and color management to match the patterns’ color. In each phase, a novel algorithm will be introduced to refine the result. The paper will state the mathematical principles behind each phase, and show the experimental results, which illustrate the algorithms’ capabilities to handle the task.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

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