• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1775-1781
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• 2016

Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. In addition, various signal processing techniques have been studied to extract feature vectors which are less sensitive to the location of the receiver. In this paper, we synthesized active echo signals using 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to echo signals to extract signal features. For the performance verification, classification experiments were performed using backpropagation and probabilistic neural network classifiers based on single aspect and multi-aspect method. As a result, we obtained a better recognition result using proposed feature extraction and multi-aspect based method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.580-584
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• 2006

Material information for environment may be useful to accomplish mobile robot localization. A procedure to classify a set of indoor materials (glass, steel, wood, aluminum and concrete) with the reflected signal of ultrasonic sensor is proposed in this paper. The main idea is to use material-specific reflection characteristics for the recognition of material type. To achieve the classification task, we modeled reflected signal as a maximum amplitude with respect to distance. In this way, we can generate echo signal models for the given materials and these models are used to compare with the current sensor reading. The experimental results show that the proposed method may give material information during map building task of mobile robot.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.273-281
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• 2002

Determination of sediment type is generally based on ground truthing. This method, however, provides information only for the limited sites. Recent developments of remote classification of seafloor sediments made it possible to obtain continuous profiles of sediment types. QTC View system, which is an acoustic instrument providing digital real-time seabed classification, was used to classify seafloor sediment types in the Suyoung Bay, Pusan. QTC View was connected to 50 kHz echo sounder, All parameters of QTC View and echo sounder are uniformly kept during survey. By ground truthing, the sediments are classified into seven types, such as slightly gravelly sand, slightly gravelly sandy mud, gravelly muddy sand, clayey sand, sandy mud, slightly gravelly muddy sand, and rocky bottom. By the first remote classification using QTC View, four sediment types are clearly identified, such as slightly gravelly sand, gravelly mud, slightly gravelly muddy sand, and rocky bottom. These are similar to the result of the second survey. Also the result of remote classification matches well with that of ground truthing, but for sediment type determined by minor component. Therefore, QTC View can effectively be used for remote classification of seafloor sediments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.5
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• pp.798-807
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• 2021

The directivity characteristics of acoustic transducers for conventional single-beam echo sounders considerably limit the detection of fish-size information in acoustic field surveys. To overcome this limitation, using the split-aperture technique to estimate the direction of arrival of single-echo signals from individual fish distributed within the sound beam represents the most reliable method for fish-size classification. For this purpose, we design and develop a split-beam data acquisition and processing system to obtain fish-size information in conjunction with a 50-kHz single-beam echo sounder. This split-beam data acquisition and processing system consists of a notebook PC, a field-programmable gate array board, an external single-transmitter module with a matching network, and four-channel receiver modules operating at a frequency of 50-kHz. The functionality of the developed split-beam data processor is tested and evaluated. Acoustic measurements in an experimental water tank showed that the developed data acquisition and processing system can be used as a fish-sizing echo sounder to estimate the size distribution of individual fish, although an external single-transmitter module with a matching network is required.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.665-670
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• 2014

A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.170-179
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• 2003

In order to classify Land Use/Land Cover using multispectral images, we have to give consequence to defining proper classes and selecting training sample with higher class separability. The process of satellite hyperspectral image which has a lot of bands is difficult and time-consuming. Furthermore, classification result of hyperspectral image with noise is often worse than that of a multispectral image. When selecting training fields according to the signatures in the study area, it is difficult to calculate covariance matrix in some clusters with pixels less than the number of bands. Therefore in this paper we presented an overview of feature extraction methods for classification of Hyperion data and examined effectiveness of feature extraction through the accuracy assesment of classified image. Also we evaluated the classification accuracy of optimal meaningful features by class separation distance, which is also a method for band reduction. As a result, the classification accuracies of feature-extracted image and original image are similar regardless of classifiers. But the number of bands used and computing time were reduced. The classifiers such as MLC, SAM and ECHO were used.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1637-1645
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• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1089-1098
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• 2018

In this paper, we propose an autonomous feeding robot and its obstacle classification system using ultrasonic sensors to secure the driving safety of the robot and efficient feeding operation. The developed feeding robot is verified by operation experiments in a cattle shed. In the proposed classification algorithm, not only the maximum amplitude of the ultrasonic echo signal but also two gradients of the signal and the variation of amplitude are considered as the feature parameters for object classification. The experimental results show the efficiency of the proposed classification method based on the Support Vector Machine, which is able to classify objects or obstacles such as a human, a cow, a fence and a wall.