scholarly journals Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5833
Author(s):  
Ching-Han Chen ◽  
Guan-Wei Lan ◽  
Ching-Yi Chen ◽  
Yen-Hsiang Huang
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Imaging System ◽  
Object Segmentation ◽  
Hierarchical Architecture ◽  
Absolute Difference ◽  
Stereo Imaging ◽  
Som Neural Network ◽  
Self Organizing

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

Auto Focusing Method of Imaging System of Digital PCR Instrument Based on BP Neural Network

2019 ◽  
Vol 33 (12) ◽  
pp. 1954031 ◽  
Author(s):  
Shanxiong Chen ◽  
Xueqing Xie ◽  
Fangyuan Zheng ◽  
Sheng Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Good Accuracy ◽  
Imaging System ◽  
Digital Pcr ◽  
Imaging Device ◽  
Focusing Effect ◽  
Electronic Imaging ◽  
Som Neural Network ◽  
Auto Focusing

The digital PCR instrument is a digital instrument for amplifying specific DNA fragments. The problem studied in this paper is the autofocus problem of its electronic imaging device. Based on the analysis of existing SOM neural network autofocus scheme, we propose an improved scheme-BP neural network for autofocus. It directly takes the SOM input and the actual focus position as the input and output of the BP neural network, which eliminates the process of prior classification and then corresponding to the focus matrix in the original SOM scheme, saving time. The experimental results show that the traditional autofocus method has good focusing effect, but the speed is slow, and the universality of the BP neural network autofocus scheme is not good enough, but within a good accuracy range, the speed is faster. Compared to traditional focusing methods, the autofocus scheme designed in this paper successfully achieves faster focusing speed for biochips.

Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

2019 ◽  
Vol 23 (1) ◽  
Author(s):  
J. M. Barrón Adame ◽  
M. S. Acosta Navarrete ◽  
J. Quintanilla Domínguez ◽  
R. Guzmán Cabrera ◽  
M. Cano Contreras ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Maps ◽  
Som Neural Network ◽  
Self Organizing

Task Allocation of Multiple Robotic Fishes Based on Self-Organizing Map Neural Network

2014 ◽  
Vol 563 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Task Allocation ◽  
Robotic Fish ◽  
The Self ◽  
Self Organizing Map ◽  
Ball Game ◽  
Target Task ◽  
Water Polo ◽  
Som Neural Network ◽  
Self Organizing

For a water polo ball game there are multiple water polos and multiple robotic fishes in each team, seeking a reasonable task allocation plan is the key point to win the game. To resolve the problem, this paper proposed a multi-target task allocation method based on the Self-organizing map (SOM) neural network. This method takes the position of the water polos as the input vector, competes and compares the position of the water polos and robotic fishes, outputs the corresponding robotic fish of each water polo. The robotic fish will move toward the target water polo when the weight was adjusted, and will finally reach the target water polo. Simulations show that the score of the team using this method is higher than another team. The results prove the correctness and reliability of this method.

FAULT IMMUNIZATION CONCEPT FOR SELF-ORGANIZING MAPPING NEURAL NETWORKS

2001 ◽  
Vol 09 (06) ◽  
pp. 781-790 ◽  
Author(s):  
R. TALUMASSAWATDI ◽  
C. LURSINSAP
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Optimization Problem ◽  
Transmission Reliability ◽  
Real Value ◽  
Som Neural Network ◽  
Compression Area ◽  
Winner Take All ◽  
Take All ◽  
Self Organizing

Self-Organizing Mapping (SOM) neural network has been widely used in pattern classification, vector quantization, and image compression. We consider the problem of strengthening the reliability of a SOM neural network by the technique of fault immunization of the synaptic links of each neuron which is similar to the concept of biological immunization. Instead of assuming the stuck-at-0 and stuck-at-1 as in those studies, we consider a general case of stuck-at-a, where a is a real value. The only assumption that we consider is only one neuron can be faulty at any time. No restriction on the number of faulty links of the neuron. Let wi,j be the weight of synaptic link j of neuron i obtained after the winner-take-all classification. Weight wi,j is immunized by adding a constant ∊i,j, either positive or negative, to wi,j. A neuron reaches its maximum fault immunization if the value of wi,j + ∊i,j can be either increased or decreased as much as possible without creating any misclassification. Thus, the fault immunization problem is formulated as an optimization problem on finding the value of each ∊i,j. A technique to find the value of wi,j + ∊i,j is proposed and its application to enhance the transmission reliability in image compression area is introduced.

scholarly journals Simultaneous measurements of three-dimensional trajectories and wingbeat frequencies of birds in the field

2018 ◽  
Vol 15 (147) ◽  
pp. 20180653 ◽  
Author(s):  
Hangjian Ling ◽  
Guillam E. Mclvor ◽  
Geoff Nagy ◽  
Sepehr MohaimenianPour ◽  
Richard T. Vaughan ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Imaging System ◽  
Three Dimensional ◽  
Bird Species ◽  
Three Dimensions ◽  
Natural Environments ◽  
Flight Performance ◽  
Stereo Imaging ◽  
Wingbeat Frequency ◽  
Wide Range

Tracking the movements of birds in three dimensions is integral to a wide range of problems in animal ecology, behaviour and cognition. Multi-camera stereo-imaging has been used to track the three-dimensional (3D) motion of birds in dense flocks, but precise localization of birds remains a challenge due to imaging resolution in the depth direction and optical occlusion. This paper introduces a portable stereo-imaging system with improved accuracy and a simple stereo-matching algorithm that can resolve optical occlusion. This system allows us to decouple body and wing motion, and thus measure not only velocities and accelerations but also wingbeat frequencies along the 3D trajectories of birds. We demonstrate these new methods by analysing six flocking events consisting of 50 to 360 jackdaws ( Corvus monedula ) and rooks ( Corvus frugilegus ) as well as 32 jackdaws and 6 rooks flying in isolated pairs or alone. Our method allows us to (i) measure flight speed and wingbeat frequency in different flying modes; (ii) characterize the U-shaped flight performance curve of birds in the wild, showing that wingbeat frequency reaches its minimum at moderate flight speeds; (iii) examine group effects on individual flight performance, showing that birds have a higher wingbeat frequency when flying in a group than when flying alone and when flying in dense regions than when flying in sparse regions; and (iv) provide a potential avenue for automated discrimination of bird species. We argue that the experimental method developed in this paper opens new opportunities for understanding flight kinematics and collective behaviour in natural environments.

Gearbox Failure Detection Using Growing Hierarchical Self-Organizing Map

2007 ◽  
Vol 348-349 ◽  
pp. 177-180
Author(s):  
Guang Lan Liao ◽  
Tie Lin Shi ◽  
Zi Rong Tang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Failure Detection ◽  
Operating Conditions ◽  
Hierarchical Architecture ◽  
Self Organizing Map ◽  
Hierarchical Relations ◽  
Unsupervised Neural Network ◽  
Self Organizing

Machine fault diagnosis is essentially an issue of pattern recognition, which heavily depends on suitable unsupervised learning method. The Self-Organizing Map (SOM), a popular unsupervised neural network, has been used for failure detection but with two limitations: needing predefined static architecture and lacking ability for the representation of hierarchical relations in the data. This paper presents a novel study on failure detection of gearbox using the Growing Hierarchical Self-Organizing Map (GHSOM), an artificial neural network model with hierarchical architecture composed of independent growing SOMs. The GHSOM can adapt its architecture during unsupervised training process and provide a global orientation in the individual layers of the hierarchy; hence the original data structure can be described correctly for machine faults diagnosis. Gearbox vibration signals measured under different operating conditions are analyzed using the proposed technique. The results prove that the hierarchical relations in the gearbox failure data can be intuitively represented, and inherent structure can be unfolded. Then gearbox operating conditions including normal, tooth cracked and tooth broken are classified and recognized clearly. The study confirms that GHSOM is very useful and effective for pattern recognition in mechanical fault diagnosis, and provides a good potential for application in practice.

Integration of Self Organizing Map and Date Driven Methods to Predict Oil Formation Volume Factor: North Africa Crude Oil Examples

2021 ◽  
Author(s):  
Gamal Alusta ◽  
Hossein Algdamsi ◽  
Ahmed Amtereg ◽  
Ammar Agnia ◽  
Ahmed Alkouh ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Statistical Prediction ◽  
Support Vector ◽  
Self Organizing Map ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Som Neural Network ◽  
Oil Formation ◽  
Self Organizing

Abstract In this paper we introduce for the first time an innovative approach for deriving Oil Formation Volume Factor (Bo) by mean of artificial intelligence method. In a new proposed application Self-Organizing Map (SOM) technology has been merged with statistical prediction methods integrating in a single step dimensionality reduction, extraction of input data structure pattern and prediction of formation volume factor Bo. The SOM neural network method applies an unsupervised training algorithm combined with back propagation neural network BPNN to subdivide the entire set of PVT input into different patterns identifying a set of data that have something in common and run individual MLFF ANN models for each specific PVT cluster and computing Bo. PVT data for more than two hundred oil samples (total of 804 data points) were collected from the north African region representing different basin and covering a greater geographical area were used in this study. To establish clear Bound on the accuracy of Bo determination several statistical parameters and terminology included in the presentation of the result from SOM-Neural Network solution. the main outcome is the reduction of error obtained by the new proposed competitive Learning Structure integration of SOM and MLFF ANN to less than 1 % compared to other method. however also investigated in this work five independents means of model driven and data driven approach for estimating Bo theses are 1) Optimal Transformations for Multiple Regression as introduced by (McCain, 1998) using alternating conditional expectations (ACE) for selecting multiple regression transformations 2), Genetic programing and heuristic modeling using Symbolic Regression (SR) and cross validation for model automatic tuning 3) Machine learning predictive model (Nearest Neighbor Regression, Kernel Ridge regression, Gaussian Process Regression (GPR), Random Forest Regression (RF), Support Vector Regression (SVM), Decision Tree Regression (DT), Gradient Boosting Machine Regression (GBM), Group modeling data handling (GMDH). Regression Model Accuracy Metrics (Average absolute relative error, R-square), diagnostic plot was used to address the more adequate techniques and model for predicting Bo.

scholarly journals STEREO MATCHING ALGORITHM BASED ON ILLUMINATION CONTROL TO IMPROVE THE ACCURACY

2016 ◽  
Vol 35 (1) ◽  
pp. 39 ◽  
Author(s):  
Rostam Affendi Hamzah ◽  
Haidi Ibrahim ◽  
Anwar Hasni Abu Hassan
Keyword(s):  
Image Quality ◽  
Stereo Vision ◽  
Stereo Matching ◽  
State Of The Art ◽  
Experimental Results ◽  
New Method ◽  
Absolute Difference ◽  
Matching Algorithm ◽  
Disparity Maps

This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

Sign in / Sign up

Export Citation Format

Share Document