scholarly journals A Curvelet-SC Recognition Method for Maize Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jing Luo ◽  
Shuze Geng ◽  
Chunbo Xiu ◽  
Dan Song ◽  
Tingting Dong
Keyword(s):  
Recognition Algorithm ◽  
Contour Extraction ◽  
Target Image ◽  
Shape Context ◽  
Recognition Method ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Template Image ◽  
Speed And Accuracy ◽  
Fold Cross Validation

Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to calculate the similarities between the template image and the target image. Finally, we adoptn-fold cross-validation algorithm to recognize diseases on maize leaf disease database. Experimental results show that the proposed algorithm can recognize 6 kinds of maize leaf diseases accurately and achieve the accuracy of 94.446%. Meanwhile this algorithm has guiding significance for other diseases recognition to an extent.

An Image Recognition Method Based on Ant Colony Optimization Neural Network Using in Imaging Fuze

2011 ◽  
Vol 121-126 ◽  
pp. 1886-1890
Author(s):  
Ke Yong Wang ◽  
Shi Kai Xing
Keyword(s):  
Neural Network ◽  
Ant Colony Optimization ◽  
Image Recognition ◽  
Recognition Algorithm ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Target Image ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Training Time

Target image recognition is an important issue in the information processing of imaging fuse system. In the paper, the main frame is proposed which can solve the problem of target image recognition and many computer simulation experiments are carried out. A recognition algorithm based on ant colony optimization and neural network is proposed. It overcomes the shortcomings of traditional BP algorithm and converges fast. The results of experiments prove that the presented algorithm can shorten the training time effectively and increase the accuracy of recognition, so it is very useful in improving the effective destroying ability of the missile.

A Multi-Scale Image Matching Recognition Algorithm Based on Phase Correlation

2011 ◽  
Vol 301-303 ◽  
pp. 1438-1443
Author(s):  
Yong Gang Tian ◽  
Min Gang Wang ◽  
Ying Ping Fan
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Phase Correlation ◽  
Reference Image ◽  
Recognition Method ◽  
Multi Scale ◽  
Template Image ◽  
The Fourier Transform ◽  
Template Size ◽  
Traditional Image

The image matching recognition method of phase correlation is based on the shift characteristics of the Fourier transform. The traditional image matching recognition algorithm has significant influence of the template size upon its matching accuracy and has weak resilience to noises except for the gauss noise. Addressing these shortcomings, we proposed a multi-scale matching recognition method based on phase correlation, combined with wavelet transform and edge detection. The algorithm, processed the reference image and the template image in different scales with such steps: decomposition, denoising, reconstruction, edge detection and the Fourier transform, phase correlation. Hence, it overcome the dependence upon template size effectively and improve the reliability and the resilience of various noises. Finally, we verified the algorithm with a real ground shooting image as the reference image and an intercepted part as the template image. The results have shown that the proposed approach is better than the traditional image matching method.

Contour Extraction of Cerebrovascular Based on Maximum Optimization Cost

2011 ◽  
Vol 204-210 ◽  
pp. 1415-1418
Author(s):  
De Jiang Zhang ◽  
Na Na Dong ◽  
Xiao Mei Lin
Keyword(s):  
Dynamic Programming ◽  
Blood Vessel ◽  
High Efficiency ◽  
High Accuracy ◽  
Differential Method ◽  
Cost Curve ◽  
Contour Extraction ◽  
Target Image ◽  
The Cost ◽  
Conventional Algorithm

By studying the conventional algorithm of contour extraction, a new method of contour extraction in blood vessel of brain is proposed based on the MOC maximum optimization cost. First of all, the theory computes the gray differential of the image by conventional differential method to build the cost space. Then, by using dynamic programming theory, the maximum optimization cost curve in the space is extracted to serve as the specific cerebrovascular profile. The experiments show that this method ensures high efficiency in extracting cerebrovascular contour and a high accuracy in positioning cerebrovascular contour, and it diminishes the target image ambiguity caused by noise to improve the anti-interference ability of Contour extraction.

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

A Novel Method of Maize Leaf Disease Image Identification Based on a Multichannel Convolutional Neural Network

2018 ◽  
Vol 61 (5) ◽  
pp. 1461-1474 ◽  
Author(s):  
Zhongqi Lin ◽  
Shaomin Mu ◽  
Aiju Shi ◽  
Chao Pang ◽  
Xiaoxiao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Graphics Processing Units ◽  
High Efficiency ◽  
Saliency Detection ◽  
Machine Learning Algorithms ◽  
Brown Spot ◽  
Maize Leaf ◽  
Leaf Disease ◽  
Fully Connected

Abstract. Traditional methods for detecting maize leaf diseases (such as leaf blight, sooty blotch, brown spot, rust, and purple leaf sheaf) are typically labor-intensive and strongly subjective. With the aim of achieving high accuracy and efficiency in the identification of maize leaf diseases from digital imagery, this article proposes a novel multichannel convolutional neural network (MCNN). The MCNN is composed of an input layer, five convolutional layers, three subsampling layers, three fully connected layers, and an output layer. Using a method that imitates human visual behavior in video saliency detection, the first and second subsampling layers are connected directly with the first fully connected layer. In addition, the mixed modes of pooling and normalization methods, rectified linear units (ReLU), and dropout are introduced to prevent overfitting and gradient diffusion. The learning process corresponding to the network structure is also illustrated. At present, there are no large-scale images of maize leaf disease for use as experimental samples. To test the proposed MCNN, 10,820 RGB images containing five types of disease were collected from maize planting areas in Shandong Province, China. The original images could not be used directly in identification experiments because of noise and irrelevant regions. They were therefore denoised and segmented by homomorphic filtering and region of interest (ROI) segmentation to construct a standard database. A series of experiments on 8 GB graphics processing units (GPUs) showed that the MCNN could achieve an average accuracy of 92.31% and a high efficiency in the identification of maize leaf diseases. The multichannel design and the integration of different innovations proved to be helpful methods for boosting performance. Keywords: Artificial intelligence, Convolutional neural network, Deep learning, Image classification, Machine learning algorithms, Maize leaf disease.

Maize Leaf Disease Detection and Classification Using Deep Learning

2021 ◽  
pp. 87-102
Author(s):  
Phani Kumar Singamsetty ◽  
G. V. N. D. Sai Prasad ◽  
N. V. Swamy Naidu ◽  
R. Suresh Kumar
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Maize Leaf ◽  
Leaf Disease

Research of Maize Leaf Disease Identifying Models Based Image Recognition

2009 ◽  
pp. 317-324 ◽  
Author(s):  
Yu-Xia Zhao ◽  
Ke-Ru Wang ◽  
Zhong-Ying Bai ◽  
Shao-Kun Li ◽  
Rui-Zhi Xie ◽  
...  
Keyword(s):  
Image Recognition ◽  
Maize Leaf ◽  
Leaf Disease

A Track-Based Gesture Recognition Algorithm for Kinect

2015 ◽  
Vol 738-739 ◽  
pp. 334-338 ◽  
Author(s):  
Ying Wang ◽  
Ling Zhang
Keyword(s):  
Gesture Recognition ◽  
Golden Section ◽  
Recognition Algorithm ◽  
Depth Image ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Image Information ◽  
Golden Section Search ◽  
Highly Effective

This paper presents a new gesture track recognition method based on the depth image information received from the Kinect sensor. First, a Kinect sensor is used to obtain the coordinates of a moving arm. Then, the gesture tracks corresponding to these coordinates are analyzed. Matching and recognition of gesture tracks are implemented by performing golden section search. The results show that this track-based method is highly effective in gesture recognition.

scholarly journals Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

2021 ◽  
Vol 15 (4) ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Tie Cai ◽  
Wei Cao
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Kriging Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Detection Accuracy ◽  
Recognition Method ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Citrus Huanglongbing ◽  
Recognition Efficiency

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.

scholarly journals Aerobics Action Recognition Algorithm Based on Three-Dimensional Convolutional Neural Network and Multilabel Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qian Wang ◽  
Mingzhe Wang
Keyword(s):  
Three Dimensional ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Multilabel Classification ◽  
Convolutional Networks ◽  
Human Movements ◽  
Video Recognition ◽  
Intelligent Recognition ◽  
Time Information

In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolutional networks and multilabel classification. Since two-dimensional convolutional neural networks (CNNs) lose time information when extracting features, so to overcome this, the proposed research uses three-dimensional convolutional networks to perform video recognition. The feature is taken in time and space, and the extracted features are subjected to multiple binary classifications to achieve the goal of multilabel classification. Various comparison and simulation experiments are conducted for the proposed research, and the experimental results prove the effectiveness and superiority of the approach.

Sign in / Sign up

Export Citation Format

Share Document