scholarly journals Sugarcane stem nodes based on the maximum value points of the vertical projection function

2020 ◽  
Vol 50 (12) ◽  
Author(s):  
Jiqing Chen ◽  
Hu Qiang ◽  
Guanwen Xu ◽  
Jiahua Wu ◽  
Xu Liu ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Color Image ◽  
Recognition Rate ◽  
Binary Image ◽  
Projection Function ◽  
Vertical Projection ◽  
Stem Node ◽  
Simultaneous Identification ◽  
Sugarcane Stem ◽  
Component Image

ABSTRACT: In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.

Research on License Plate Recognition System Based on Computer Vision

2011 ◽  
Vol 65 ◽  
pp. 536-541
Author(s):  
Ye Qin Wang ◽  
Liang Hai Chen ◽  
Li Yun Zhuang
Keyword(s):  
Interface Design ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Binary Image ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Projection ◽  
Differential Operation ◽  
Program Transfer

In order to achieve the exact location and character recognition of license plate, firstly, this paper got binary image of license plate and done edge detection with differential operation. Secondly, it searched the license plate binary image after difference for the horizontal and vertical cut point, and determined the best cutting threshold through the experiment. Finally, it made the character segmentation by vertical projection, the recognition of license plate characters with the use of BP neural network, whose overall recognition rate is at 95.3%, and the display interface design for program transfer and results display. The experimental results showed that the location of license plate was exact and the character recognition rate was high.

Technique for Removing Unnecessary Superimposed Patterns from Image using Generative Network

2021 ◽  
Author(s):  
Kazutake Uehira ◽  
Hiroshi Unno
Keyword(s):  
Color Image ◽  
Depth Map ◽  
Experimental Results ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Blue Component ◽  
Component Image

A technique for removing unnecessary patterns from captured images by using a generative network is studied. The patterns, composed of lines and spaces, are superimposed onto a blue component image of RGB color image when the image is captured for the purpose of acquiring a depth map. The superimposed patterns become unnecessary after the depth map is acquired. We tried to remove these unnecessary patterns by using a generative adversarial network (GAN) and an auto encoder (AE). The experimental results show that the patterns can be removed by using a GAN and AE to the point of being invisible. They also show that the performance of GAN is much higher than that of AE and that its PSNR and SSIM were over 45 and about 0.99, respectively. From the results, we demonstrate the effectiveness of the technique with a GAN.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching

2019 ◽  
Vol 34 (06) ◽  
pp. 2050016
Author(s):  
Zhongli Wang ◽  
Xiping Ma ◽  
Wenlin Huang
Keyword(s):  
Wavelet Transform ◽  
Road Traffic ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Vertical Projection ◽  
Edge Matching ◽  
Vehicle License Plate Recognition

With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system.

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

HISTOGRAM OF TRANSITION FOR HUMAN HEAD RECOGNITION

2016 ◽  
Vol 78 (5-9) ◽  
Author(s):  
Panca Mudjirahardjo ◽  
M. Fauzan Edy Purnomo ◽  
Rini Nur Hasanah ◽  
Hadi Suyono
Keyword(s):  
Feature Extraction ◽  
Color Image ◽  
Recognition Rate ◽  
Orientation Angle ◽  
Human Head ◽  
Input Image ◽  
Foreground Extraction ◽  
Multi Scale ◽  
Novel Method ◽  
Main Component

The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. This feature is relied on foreground extraction. In this paper we evaluate some pre-processing to get foreground extraction before we calculate the histogram of transition.We evaluate the performance of recognition rate in related with preprocessing of input image, such as color, size and orientation. We evaluate for Red-Green-Blue (RGB) and Hue-saturation-Value (HSV) color image; multi scale of 10×15 pixels, 20×30 pixels and 40×60 pixels; and multi orientation angle of 315o, 330o, 345o, 15o, 30o, and 45o.For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradients (HOG) and Linear Binary Pattern (LBP). The experimental results show Histogram of Transition robust for changing of color, size and orientation angle.

scholarly journals Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion

2021 ◽  
Vol 11 (18) ◽  
pp. 8663
Author(s):  
Wen Chen ◽  
Chengwei Ju ◽  
Yanzhou Li ◽  
Shanshan Hu ◽  
Xi Qiao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Natural Environment ◽  
Stem Node ◽  
Combining Data ◽  
Real Time Detection ◽  
Data Expansion ◽  
Sugarcane Stem ◽  
The Impact ◽  
Detection Speed

The rapid and accurate identification of sugarcane stem nodes in the complex natural environment is essential for the development of intelligent sugarcane harvesters. However, traditional sugarcane stem node recognition has been mainly based on image processing and recognition technology, where the recognition accuracy is low in a complex natural environment. In this paper, an object detection algorithm based on deep learning was proposed for sugarcane stem node recognition in a complex natural environment, and the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The impact of the data expansion and lighting condition in different time periods on the results of sugarcane stem nodes detection was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparing it with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The comparison results showed that the AP (average precision) of the sugarcane stem nodes detected by YOLO v4 was 95.17%, which was higher than that of the other four algorithms (78.87%, 88.98%, 90.88% and 92.69%, respectively). Meanwhile, the detection speed of the YOLO v4 method was 69 f/s and exceeded the requirement of a real-time detection speed of 30 f/s. The research shows that it is a feasible method for real-time detection of sugarcane stem nodes in a complex natural environment. This research provides visual technical support for the development of intelligent sugarcane harvesters.

scholarly journals Color image steganography in YCbCr space

2020 ◽  
Vol 10 (1) ◽  
pp. 202 ◽  
Author(s):  
Zena Ahmed Alwan ◽  
Hamid Mohammed Farhan ◽  
Siraj Qays Mahdi
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Binary Image ◽  
Adaptive Procedure ◽  
Secret Data ◽  
Hide Data ◽  
Main Color ◽  
Different Color ◽  
Exclusive Or ◽  
Module Space

Steganography is a best method for in secret communicating information during the transference of data. Images are an appropriate method that used in steganography can be used to protection the simple bits and pieces. Several systems, this one as color scale images steganography and grayscale images steganography, are used on color and store data in different techniques. These color images can have very big amounts of secret data, by using three main color modules. The different color modules, such as HSV-(hue, saturation, and value), RGB-(red, green, and blue), YCbCr-(luminance and chrominance), YUV, YIQ, etc. This paper uses unusual module to hide data: an adaptive procedure that can increase security ranks when hiding a top secret binary image in a RGB color image, which we implement the steganography in the YCbCr module space. We performed Exclusive-OR (XOR) procedures between the binary image and the RGB color image in the YCBCR module space. The converted byte stored in the 8-bit LSB is not the actual bytes; relatively, it is obtained by translation to another module space and applies the XOR procedure. This technique is practical to different groups of images. Moreover, we see that the adaptive technique ensures good results as the peak signal to noise ratio (PSNR) and stands for mean square error (MSE) are good. When the technique is compared with our previous works and other existing techniques, it is shown to be the best in both error and message capability. This technique is easy to model and simple to use and provides perfect security with unauthorized.

An Approach to Tone Recognition of Mandarin Speech Based-On Two-Stage Model

2011 ◽  
Vol 145 ◽  
pp. 297-303
Author(s):  
Wei Chih Hsu ◽  
Jung Nan Sun ◽  
Huai I Wang
Keyword(s):  
Standard Deviation ◽  
Recognition Rate ◽  
Threshold Value ◽  
Recognition Algorithm ◽  
Training Data ◽  
Pitch Contour ◽  
Two Stage ◽  
Second Stage ◽  
Fundamental Frequencies ◽  
Tone Recognition

After inspecting the pitch contours of tone 1 of Mandarin speech, we found that the pitch contour of tone 1 consists of upward and downward line segments, while it is supposed that the contour of tone 1 is flat. Our study also found that tone 1 tends to be recognized as other three tones if the recognition algorithm used is based on the tone contour slope or shape. According to our experiments, we conclude that the recognition rate of the tones would be improved if two stage tone recognition scheme is conducted. At the first stage, tone one is recognized out and then the other three tones are identified at the second stage. The fundamental frequencies of input Mandarin speech of tone 1 are first retrieved from the training data and then a threshold value relating to standard deviation of fundamental frequencies is determined. In the first recognition stage, if the statistic standard deviation of fundamental frequencies is less than the determined threshold, the Mandarin speech is recognized as tone one. The input Mandarin speech which is not classified as tone 1 are the recognition targets of the second recognition stage. In the second stage, a so-called linear gradient analysis is conducted, and the tones are identified according to the derived positive or negative linear gradients. Our proposed recognition method is superior to traditional methods of Mandarin tone recognition in terms of effectiveness and recognition rate. Some experiments to prove the necessity of conducting two recognition stages will be described in detail.

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