scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

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 A Study on Image Segmentation Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 75-78
Author(s):  
S. Abdul Saleem ◽  
G. Vinitha
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Video Frame ◽  
Challenging Problem ◽  
Digital Form ◽  
Low Level ◽  
Grey Scale ◽  
High Level

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

scholarly journals PLANT LEAF IMAGE RECOGNITION SYSTEM

2021 ◽  
Vol 1 (1) ◽  
pp. 35-44
Author(s):  
Gaurav Kulkarni ◽  
◽  
Chandrashekhar Kumbhar
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Image Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Rare Plant ◽  
Plant Recognition ◽  
Identification Techniques ◽  
Leaf Image ◽  
End Results

Plants play a vital role in our day-to-day life. Hence, a good understanding of plants is needed to help in identifying new or rare plant species. Such identification will in turn improve the drug industry, balance the ecosystem as well as the agricultural productivity and sustainability. We often come across various plants with different variety of leaves and flowers every single day. We try to recognize it, but we fail. So we need some system which can tell us about the leaf/flower instantly. So, to solve such problems, we introduce a plant recognition system (PRS) which tells you the details about a leaf by just uploading the image of the leaf. For this system, we use image processing and some identification techniques which can recognize the leaf by its structure, colour, shape etc and fetch the details about it and provide the details of it to the user. This paper gives a understanding about the different methods used under image processing and various methods and algorithm used to identify that leaf in a short and simple way. Object recognition and detection are techniques with similar end results and implementation approaches. Therefore, it requires heavy pre-processing and implements various processes to obtain the end results.

Experimental Study of Human Fingerprint Image Recognition Analysis

2014 ◽  
Vol 971-973 ◽  
pp. 1616-1619
Author(s):  
Su Ling Zhang
Keyword(s):  
Image Processing ◽  
Experimental Study ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Image Recognition ◽  
Fingerprint Image ◽  
Image Preprocessing ◽  
Advantages And Disadvantages ◽  
Demand Pattern ◽  
Image Segmentation Algorithm

respectively cited the fingerprint image preprocessing for image segmentation , demand pattern, image enhancement and binarization of several algorithms , and each algorithm were compared. Image segmentation algorithm studied in this paper , image enhancement algorithms, can be very good to complete the project requirements. Because each method has its advantages and disadvantages , and therefore use different methods to get different results after image processing .

Improved Color Image Segmentation Algorithm Based on GrabCut

2013 ◽  
Vol 373-375 ◽  
pp. 464-467 ◽  
Author(s):  
Wang Rui ◽  
Jin Ye Peng ◽  
Li Ping Che ◽  
Yu Ting Hou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Color Image ◽  
Implementation Process ◽  
Gaussian Mixture ◽  
Extraction Process ◽  
User Participation ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Realistic Image

In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

Identification of Plant Diseases Using Image Processing and Image Recognition

2021 ◽  
Author(s):  
V Rajesh Kumar ◽  
K Pradeepan ◽  
S Praveen ◽  
M Rohith ◽  
V Vasantha Kumar
Keyword(s):  
Image Processing ◽  
Image Recognition ◽  
Plant Diseases

scholarly journals Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose

2018 ◽  
Vol 28 ◽  
pp. 01036 ◽  
Author(s):  
Bartosz Szulczyński ◽  
Jacek Gębicki ◽  
Jacek Namieśnik
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Electronic Nose ◽  
Sensory Analysis ◽  
Recognition System ◽  
Gas Mixtures ◽  
Pattern Recognition System ◽  
Mlr Model

The paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along with the fuzzy logic pattern recognition system can be successfully used to estimate the odour intensity of tested gas mixtures. The correctness of the results obtained using fuzzy logic was equal to 68%.

Sign in / Sign up

Export Citation Format

Share Document