• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.49 no.2
• /
• pp.214-223
• /
• 2016

Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features. The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases. In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.9
• /
• pp.3721-3737
• /
• 2015

3D reconstruction based on binocular data is significant to machine vision. In our method, we propose a new and high efficiency 3D reconstruction approach by using a consumer camera aiming to: 1) address the configuration problem of dual camera in the binocular reconstruction system; 2) address stereo matching can hardly be done well problem in both time computing and precision. The kernel feature is firstly proposed in calibration stage to rectify the epipolar. Then, we segment the objects in the camera into background and foreground, for which system obtains the disparity by different method: local window matching and kernel feature-based matching. Extensive experiments demonstrate our proposed algorithm represents accurate 3D model.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.56 no.1
• /
• pp.51-55
• /
• 2007

In this paper, a portable rotor bar fault diagnosis system for small 3 phase induction motors is suggested. For portable real-tine diagnosis system, an USB-DAQ board for collecting the 3 phase current data, three current probes, and a notebook computer are used. The LabVIEW graphical language is used for filtering, analysis, storing, and monitoring the current data. The three phase stator current are filtered and transformed to frequency level by FIT. An analysis window programed by LabVIEW is located in front panel to show the FIT results and this suggested window has a zooming function to detect the fault feature more easily near the feature frequency range which is varying by the slip frequency. To show the possibility of portable rotor bar diagnosis system, three types(healthy, one rotor bar fault, two rotor bar fault) of rotor bar are intentionally prepared and compared by the suggested window of front panel. Experimental results are shown that a suggested diagnosis system is applicable to portable diagnosis system and the rotor bar fault is detected by the frequency window in front panel programed in LabVIEW graphical language.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.3
• /
• pp.163-172
• /
• 2003

A stereo matching has been an important tool for the reconstruction of 3D terrain. The current state of stereo matching technology has reached the level where a very elaborate DEM(Digital Elevation Map) can be obtained. However, there still exist many factors causing DEM error in stereo matching. This paper propose a new method to reduce the error caused by the lack of significant features in the correlation window The proposed algorithm keeps the correlation window as small as possible, as long as there is a significant feature in the window. Experimental results indicate that the proposed method increases the DEM accuracy by $72.65\%$ in the plain area and $41.96\%$ in the mountain area over the conventional scheme. Comparisons with Kanade's result show that the proposed method eliminates spike type of errors more efficiently than Kanade's adaptive window technique and produces reliable DEM.

• The KIPS Transactions:PartB
• /
• v.14B no.7
• /
• pp.473-480
• /
• 2007

Feature tracking in video frame sequences has suffered from the instability and the frequent failure of feature matching between two successive frames. In this paper, we propose a robust 2D feature tracking method that is stable to long video sequences. To improve the stability of feature tracking, we predict the spatial movement in the current image frame using the state variables. The predicted current movement is used for the initialization of the search window. By computing the feature similarities in the search window, we refine the current feature positions. Then, the current feature states are updated. This tracking process is repeated for each input frame. To reduce false matches, the outlier rejection stage is also introduced. Experimental results from real video sequences showed that the proposed method performs stable feature tracking for long frame sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.855-870
• /
• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.7
• /
• pp.2332-2338
• /
• 2011

In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as a sensitive diagnostic tool to detect the presence of peak position shift. In this approach, the moving window is constructed from a small data segment along the wavenumber axis. For each window bound by a narrow wavenumber region, separate PCA analysis was applied. Simulated spectra with complex spectral feature variations were analyzed to explore the possibility of MWPCA technique. This MWPCA-based detection of the peak shift, potentially coupled with 2D correlation analysis to provide additional verification, may offer an attractive solution.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.22 no.2
• /
• pp.5-25
• /
• 2011

This study aims to identify the most effective statistical feature selecting method and context window size for word sense disambiguation using supervised methods. In this study, features were selected by four different methods: information gain, document frequency, chi-square, and relevancy. The result of weight comparison showed that identifying the most appropriate features could improve word sense disambiguation performance. Information gain was the highest. SVM classifier was not affected by feature selection and showed better performance in a larger feature set and context size. Naive Bayes classifier was the best performance on 10 percent of feature set size. kNN classifier on under 10 percent of feature set size. When feature selection methods are applied to word sense disambiguation, combinations of a small set of features and larger context window size, or a large set of features and small context windows size can make best performance improvements.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.1227-1232
• /
• 2015

Several works help make sliding window object detection fast, nevertheless, computational demands remain prohibitive for numerous applications. This paper proposes a fast object detection method based on three strategies: cascade classifier, selective window search and fast feature extraction. Experimental results show that the proposed method outperforms the compared methods and achieves both high detection precision and low computation cost. Our approach runs at 17ms per frame on 640×480 images while attaining state-of-the-art accuracy.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.44 no.4
• /
• pp.43-54
• /
• 2007

This paper presents design and verification of a face detection hardware for real time application. Face detection algorithm detects rough face position based on already acquired feature parameter data. The hardware is composed of five main modules: Integral Image Calculator, Feature Coordinate Calculator, Feature Difference Calculator, Cascade Calculator, and Window Detection. It also includes on-chip Integral Image memory and Feature Parameter memory. The face detection hardware was verified by using S3C2440A CPU of Samsung Electronics, Virtex4LX100 FPGA of Xilinx, and a CCD Camera module. Our design uses 3,251 LUTs of Xilinx FPGA and takes about 1.96${\sim}$0.13 sec for face detection depending on sliding-window step size, when synthesized for Virtex4LX100 FPGA. When synthesized on Magnachip 0.25um ASIC library, it uses about 410,000 gates (Combinational area about 345,000 gates, Noncombinational area about 65,000 gates) and takes less than 0.5 sec for face realtime detection. This size and performance shows that it is adequate to use for embedded system applications. It has been fabricated as a real chip as a part of XF1201 chip and proven to work.