• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1771-1774
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• 2005

Recently, RFID has become popular in the field of remote sensing applications. Location awareness is one of the most important keys to deploying RFID for advanced object tracking. Generally, multiple reference RF stations or additional sensors are used for the location sensing with RFID, but, particularly in indoor environments, spatial layout and cost problems limit the applicability of those approaches. In this paper, we propose a novel method for location sensing with active RFID systems not requiring the need for reference stations or additional sensors. The system triangulates the position of RF signal source using the signal pattern of the loop antenna connected to the power detector. The power detector consists of a signal strength detector and a signal analysis unit. The signal analysis unit indicates the signal strength and serial number using the signal from the strength detector, and provides the direction of the signal to the application target. We designed three different signal analysis units depending on the threshold type. The developed system can sense the direction to the transponder located over 10 m away within the maximum error of $5^{\circ}$. It falls within a reasonable range in our normal office environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.579-586
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• 2011

It is very critical for image processing of IIR (Imaging Infrared) seekers to achieve improved guidance performance for missile systems to determine appropriate thresholds in various environments. In this paper, we propose automatic threshold determination methods for proper thresholds to extract definite target signals in an EOCM (Electro-Optical Countermeasures) environment with low SNR (Signal-to-Noise Ratios). In particular, thresholds are found to be too low to extract target signals if one uses the Otsu method so that we suggest a Shifted Otsu method to solve this problem. Also we improve extracting target signal by changing Shifted Otsu thresholds according to the TBR (Target to Background Ratio). The suggested method is tested for real IIR images and the results are compared with the Otsu method. The HPDAF (Highest Probabilistic Data Association Filter) which selects the target originated measurements by taking into account of both signal intensity and statistical distance information is applied in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.1
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• pp.32-41
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• 2008

The moving object tracking in vision based observation using video uses difference method between GMM(Gaussian Mixture Model) based background and present image. In the case of racking object using binary image made by threshold, the object is merged not by object information but by Cast-Shadow. This paper proposed the method that eliminates Cast-Shadow using backpropagation Neural Network. The neural network is trained by abstracting feature value form training image of object range in 10-movies and Cast-Shadow range. The method eliminating Cast-Shadow is based on the method distinguishing shadow from binary image, its Performance is better(16.2%, 38.2%, 28.1%, 22.3%, 44.4%) than existing Cast-Shadow elimination algorithm(SNP, SP, DNM1, DNM2, CNCC).

• Journal of the Korea Computer Industry Society
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• v.3 no.9
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• pp.1145-1156
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• 2002

A method to measure the speed and distance of moving object is proposed using the stereo vision system. One of the most important factors for measuring the speed and distance of moving object is the accuracy of object tracking. Accordingly, the background image algorithm is adopted to track the rapidly moving object and the local opening operator algorithm is used to remove the shadow and noise of object. The extraction efficiency of moving object is improved by using the adaptive threshold algorithm independent to variation of brightness. Since the left and right central points are compensated, the more exact speed and distance of object can be measured. Using the background image algorithm and local opening operator algorithm, the computational processes are reduced and it is possible to achieve the real-time processing of the speed and distance of moving object. The simulation results show that background image algorithm can track the moving object more rapidly than any other algorithm. The application of adaptive threshold algorithm improved the extraction efficiency of the target by reducing the candidate areas. Since the central point of the target is compensated by using the binocular parallax, the error of measurement for the speed and distance of moving object is reduced. The error rate of measurement for the distance from the stereo camera to moving object and for the speed of moving object are 2.68% and 3.32%, respectively.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.841-849
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• 2005

Two-phase flows, e.g. sediment-laden flow and bubbly flow, have two different flow profiles; flow velocity and sediment velocity. To measure velocity distributions of two-phase flows, it is necessary to use sophisticated instruments which can separate velocity profiles of two-phases. For bubbly flows, PIV (Particle Image Velocimetry) or PTV (Particle Tracking Velocimetry) has given fairly good velocity profiles of two-phases. However, for sediment-laden flows, the applications of PIV or PTV has not been so successful, because the sediment particles introduced to the flow kept the images from being analyzed. A new algorithm, which consists of several image analysis methods, is proposed to analyze sediment-laden flows. For detection algorithm, threshold method, edge detection method, and thinning method are adapted, and for finding matching pair PIV and PTV routines are combined. The proposed method can (1) detect sediment particles with irregular boundaries, (2) remove reflected images and scattered images, and (3) discriminate tracer particles from reflected images of sediment particles.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.5
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• pp.450-459
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• 2009

RFID technologies, which allow collecting, storing, processing, and tracking information by wirelessly recognizing the inherent ID of object through an attached electronic tag, have a variety of application areas. One of the important parameters in designing such RFID systems is the read range within which the system recognizes the electronic tag. We present a novel method of designing an RFID tag required for long read range of RFID systems. The tag designed by the proposed method is battery-assisted to increase its forward-link read range and simultaneously, has backscattering modulation amplified to increase its reverse-link read range. We experimentally confirm that the minimum threshold power of the tag is - 23 dBm and the backscattering modulation gain is 28 dB, which is consistent with our simulation results. We also verify that the tag in this paper improves more than 2 times in terms of the read range compared to the existing commercial tags.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3782-3796
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• 2020

A three-dimensional (3D) reconstruction is an important research area in computer vision. The ability to detect and match features across multiple views of a scene is a critical initial step. The tracking matrix W obtained from a 3D reconstruction can be applied to structure from motion (SFM) algorithms for 3D modeling. We often fail to generate an acceptable number of features when processing face or medical images because such images typically contain large homogeneous regions with minimal variation in intensity. In this study, we seek to locate sufficient matching points not only in general images but also in face and medical images, where it is difficult to determine the feature points. The algorithm is implemented on an adaptive threshold value, a scale invariant feature transform (SIFT), affine SIFT, speeded up robust features (SURF), and affine SURF. By applying the algorithm to face and general images and studying the geometric errors, we can achieve quasi-dense matching points that satisfy well-functioning geometric constraints. We also demonstrate a 3D reconstruction with a respectable performance by applying a column space fitting algorithm, which is an SFM algorithm.

• Journal of IKEEE
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• v.20 no.2
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• pp.167-170
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• 2016

Replica bit-line technique is used for making enable signal of sense amplifier which accurately tracks bit-line of SRAM. However, threshold voltage variation in the replica bit-line circuit changes the cell current, which results in variation of the sense amplifier enable time, $T_{SAE}$. The variation of $T_{SAE}$ makes the sensing operation unstable. In this paper, in addition to conventional replica bit-line delay ($RBL_{conv}$), dual replica bit-line delay (DRBD) and multi-stage dual replica bit-line delay (MDRBD) which are used for reducing $T_{SAE}$ variation are briefly introduced, and the maximum possible number of on-cell which can satisfy $6{\sigma}$ sensing yield is determined through simulation at a supply voltage of 0.6V with 14nm FinFET technology. As a result, it is observed that performance of DRBD and MDRBD is improved 24.4% and 48.3% than $RBL_{conv}$ and energy consumption is reduced which 8% and 32.4% than $RBL_{conv}$.