scholarly journals A sea-sky-line detection method based on Gaussian mixture models and image texture features

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141989211
Author(s):  
Wanhui Yang ◽  
Hengyu Li ◽  
Jingyi Liu ◽  
Shaorong Xie ◽  
Jun Luo
Keyword(s):  
Gaussian Mixture ◽  
Obstacle Detection ◽  
Distribution Model ◽  
Line Detection ◽  
Detection Accuracy ◽  
Gray Level ◽  
Data Set ◽  
Unmanned Surface Vehicles ◽  
Public Data ◽  
Occurrence Matrix

This article presents a sea-sky-line detection algorithm in a sea-sky environment for unmanned surface vehicles. Obstacle detection is a vital branch for unmanned surface vehicles on the ocean. Because of the specificity and complexity of the marine navigation environment, we first apply semantic segmentation for marine images. The complete marine scene is divided into sky area, middle mixture area, and seawater area before sea-sky-line detection. Segmenting the marine environment is beneficial for narrowing the obstacle search area, accelerating the rate of obstacle detection, and improving detection accuracy. Therefore, a fast, robust, and accurate sea-sky image segmentation method is urgently required. Therefore, we present a method that lies in a probabilistic graphical model for segmenting marine images. The Gaussian mixture model is introduced as the probability distribution model for the marine image. The sky, middle mixture, and seawater areas are generated by three Gaussian models. The expectation–maximization algorithm is utilized to maximize the log-likelihood function, and the parameters of the Gaussian mixture probability density function that recover the marine image distribution are available after several iterations. Furthermore, to solve the problem of incorrect convergence direction caused by unsatisfactory initialization conditions, the gray level co-occurrence matrix is referenced to initialize the Gaussian components. The coarse segmentation results rely on the gray level co-occurrence matrix and are used to calculate the prior initialization parameters of Gaussian components and obtain the prior distribution information of marine images, which mitigates the harmful influence of poor initialization. The algorithm is tested on a data set consisting of the marine obstacle detection dataset (MODD) public data set and our collected images. The results on this data set demonstrate that the proposed method is more robust and that a superior initialization condition can effectively accelerate the convergence velocity of the iterative process for Gaussian components.

scholarly journals An Efficient Image Retrieval System using GLCM Features and Kullback-Leibler Divergence

2020 ◽  
Vol 9 (3) ◽  
pp. 3180-3184
Keyword(s):  
Retrieval System ◽  
Gaussian Mixture ◽  
Gray Level ◽  
Training Set ◽  
Relative Probability ◽  
Data Set ◽  
Model Based ◽  
Leibler Divergence ◽  
Image Retrieval System ◽  
Occurrence Matrix

Image processing is a process of extracting features from an image. The features of the image are extracted using the correlation model, based on Gray-Level Co-Occurrence Matrix (GLCM). Each of the images considered for data set are converted into gray level before applying Gaussian Mixture Model (GMM). The features extracted from GLCM are given as an input to the model-based technique so that the relative Probability Density Functions (PDF) are extracted. The comparison is carried out in the same manner by identifying the relative PDF of the training set and test data by using KullbackLeibler divergence method (KL-Divergence). In this paper an attempt is made for developing an effective model to retrieve the images based on features by considering GLCM and GMM. The results derived show that the proposed methodology is able to retrieve images more effectively.

scholarly journals Intrusion Detection Based on Gray-Level Co-Occurrence Matrix and 2D Dispersion Entropy

2021 ◽  
Vol 11 (12) ◽  
pp. 5567
Author(s):  
Gianmarco Baldini ◽  
Jose Luis Hernandez Ramos ◽  
Irene Amerini
Keyword(s):  
Intrusion Detection ◽  
Network Flows ◽  
Learning Algorithm ◽  
Detection System ◽  
Detection Algorithm ◽  
Entropy Measure ◽  
Detection Accuracy ◽  
Gray Level ◽  
Data Set ◽  
Occurrence Matrix

The Intrusion Detection System (IDS) is an important tool to mitigate cybersecurity threats in an Information and Communication Technology (ICT) infrastructure. The function of the IDS is to detect an intrusion to an ICT system or network so that adequate countermeasures can be adopted. Desirable features of IDS are computing efficiency and high intrusion detection accuracy. This paper proposes a new anomaly detection algorithm for IDS, where a machine learning algorithm is applied to detect deviations from legitimate traffic, which may indicate an intrusion. To improve computing efficiency, a sliding window approach is applied where the analysis is applied on large sequences of network flows statistics. This paper proposes a novel approach based on the transformation of the network flows statistics to gray images on which Gray level Co-occurrence Matrix (GLCM) are applied together with an entropy measure recently proposed in literature: the 2D Dispersion Entropy. This approach is applied to the recently public IDS data set CIC-IDS2017. The results show that the proposed approach is competitive in comparison to other approaches proposed in literature on the same data set. The approach is applied to two attacks of the CIC-IDS2017 data set: DDoS and Port Scan achieving respectively an Error Rate of 0.0016 and 0.0048.

scholarly journals Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

2019 ◽  
Vol 8 (3) ◽  
pp. 6069-6076
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Visual Surveillance ◽  
Detection Accuracy ◽  
Data Set ◽  
Moving Vehicle ◽  
Detection Techniques

Many computer vision applications needs to detect moving object from an input video sequences. The main applications of this are traffic monitoring, visual surveillance, people tracking and security etc. Among these, traffic monitoring is one of the most difficult tasks in real time video processing. Many algorithms are introduced to monitor traffic accurately. But most of the cases, the detection accuracy is very less and the detection time is higher which makes the algorithms are not suitable for real time applications. In this paper, a new technique to detect moving vehicle efficiently using Modified Gaussian Mixture Model and Modified Blob Detection techniques is proposed. The modified Gaussian Mixture model generates the background from overall probability of the complete data set and by calculating the required step size from the frame differences. The modified Blob Analysis is then used to classify proper moving objects. The simulation results shows that the method accurately detect the target

scholarly journals Artificial Neural Network and Gray Level Co-Occurrence Matrix Based Automated Corrosion Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4499-4502
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Crack Detection ◽  
Gaussian Mixture ◽  
Image Processing Technique ◽  
Gray Level ◽  
Corrosion Detection ◽  
Artificial Neural ◽  
Occurrence Matrix

In this paper, we show an image processing algorithm with its capabilities in detecting the corrosion. This algorithm is programmed and requires no parameter modification and no previous knowledge of image acquisition process because function evaluates their parameters. Digital image processing technique proposed to avoid such incident occurrences. Combining Poisson-Gaussian- Mixture distribution with a Fuzzy segmentation framework an algorithm is developed to clutch image information. Artificial neural network and gray level co-occurrence matrix (GLCM) utilized to recognize the corrosion. The developed algorithm can be used in the ROV to detect the corrosion spots. The algorithm results exhibit the sufficiency in perceives corroded spots. Using image processing the corrosion detection process can be automated with a monitoring software setup which can generate an alert based on corrosion severity. Using image processing the infrastructure’s corrosion evaluation effort will be minimized, and presenting the result statistics is easier. In application point of view, we can extend the algorithm capabilities to the fatigue crack detection.

Water Boundary Line Detection for Unmanned Surface Vehicles

2020 ◽  
Vol 13 (8) ◽  
pp. 1145-1152
Author(s):  
Yuejun Liu ◽  
Liyong Ma ◽  
Wei Xie ◽  
Xiaolei Zhang ◽  
Yong Zhang
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Line Detection ◽  
Boundary Line ◽  
Intelligent Environment ◽  
High Brightness ◽  
Unmanned Surface Vehicles ◽  
Environment Perception ◽  
Occurrence Matrix

Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV. Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection. Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas. Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.

Nonlinear gray-level co-occurrence matrix texture analysis for improved seismic facies interpretation

2017 ◽  
Vol 5 (3) ◽  
pp. SJ31-SJ40 ◽  
Author(s):  
Haibin Di ◽  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Amplitude Distribution ◽  
Spatial Arrangement ◽  
Seismic Facies ◽  
Gray Level ◽  
3D Seismic ◽  
Data Set ◽  
Seismic Amplitude ◽  
Analysis Window ◽  
Occurrence Matrix

Seismic texture analysis is a useful tool for delineating subsurface geologic features from 3D seismic surveys, and the gray-level co-occurrence matrix (GLCM) method has been popularly applied for seismic texture discrimination since its first introduction in the 1990s. The GLCM texture analysis consists of two components: (1) to rescale seismic amplitude by a user-defined number of gray levels and (2) to perform statistical analysis on the spatial arrangement of gray levels within an analysis window. Traditionally, the linear transformation is simply used for amplitude rescaling so that the original reflection patterns could be best preserved. However, the seismic features of interpretational interest often cover only a small portion of its amplitude histogram. For representing such features more effectively, it is helpful to perform a nonlinear rescaling of the amplitude distribution between different seismic features. To achieve such an objective, this study proposes a nonlinear GLCM analysis based on four types of nonlinear gray-level transformation (logarithmic, exponential, sigmoid, and logit) and investigates their implications for seismic facies interpretation. Applications to the 3D seismic data set from offshore Angola (West Africa) demonstrate the added values of the generated nonlinear GLCM attributes in better characterizing the channels, fans, and lobes in a deep-marine turbidite system.

scholarly journals Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

2021 ◽  
Vol 12 ◽  
Author(s):  
Peng Wang ◽  
Tong Niu ◽  
Yanru Mao ◽  
Zhao Zhang ◽  
Bin Liu ◽  
...  
Keyword(s):  
Spatial Location ◽  
Activation Function ◽  
Location Information ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Accurate Identification ◽  
Data Set ◽  
Disease Identification ◽  
Public Data ◽  
Spatial Location Information

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

Detection of image region-duplication forgery based on gray level co-occurrence matrix

2012 ◽  
Vol 31 (6) ◽  
pp. 1628-1630
Author(s):  
Jia-jia OU ◽  
Bi-ye CAI ◽  
Bing XIONG ◽  
Feng LI
Keyword(s):  
Gray Level ◽  
Image Region ◽  
Region Duplication ◽  
Occurrence Matrix
Sign in / Sign up

Export Citation Format

Share Document