• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1152-1157
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• 2021

This paper proposes a face recognition method for access control of apartment building. Different from most existing face recognition methods, the proposed one does not require any manual process for face enrollment. When a person is exiting through the main entrance door, his/her face data (i.e., face image and face feature) are automatically extracted from the captured video and registered in the database. When the person needs to enter the building again, the face data are extracted and the corresponding face feature is compared with the face features registered in the database. If a matching person exists, the entrance door opens and his/her access is allowed. The face data of the matching person are immediately deleted and the database has the latest face data of outgoing person. Thus, a higher recognition accuracy could be expected. To verify the feasibility of the proposed method, Python based face recognition has been implemented and the cloud service provided by a web portal.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.10-21
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• 2008

The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.728-741
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• 2015

Stereo vision has become an important technical issue in the field of 3D imaging, machine vision, robotics, image analysis, and so on. The depth map extraction from stereo video is a key technology of stereoscopic 3D video requiring stereo correspondence algorithms. This is the matching process of the similarity measure for each disparity value, followed by an aggregation and optimization step. Since it requires a lot of computational power, there are significant speed-performance advantages when exploiting parallel processing available on processors. In this situation, multi-core CPU may allow many parallel programming technologies to be realized in users computing devices. This paper proposes parallel implementations for calculating disparity map using a shared memory programming and exploiting the streaming SIMD extension technology. By doing so, we can take advantage both of the hardware and software features of multi-core processor. For the performance evaluation, we implemented a parallel SAD algorithm with OpenMP and SSE2. Their processing speeds are compared with non parallel version on stereoscopic streaming video. The experimental results show that both technologies have a significant effect on the performance and achieve great improvements on processing speed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1131-1135
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• 2005

Since human is vulnerable to emotional, physical and environmental distractions, most human inspectors cannot sustain a consistent 8-hour inspection in a day specifically for small components like door locking levers. As an alternative for human inspection, presented in this study is the development of a machine vision inspection system (MVIS) purposely for door locking levers. Comprises the development is the structure of the MVIS components, designed to meet the demands, features and specifications of door locking lever manufacturing companies in increasing their production throughput upon keeping the quality assured. This computer-based MVIS is designed to perform quality measures of detecting missing portions and defects like burr on every door locking lever. NI Vision Builder software for Automatic Inspection (AI) is found to be the optimum solution in configuring the needed quality measures. The proposed software has measurement techniques such as edge detecting and pattern-matching which are capable of gauging, detecting missing portion and checking alignment. Furthermore, this study exemplifies the incorporation of the optimized NI Builder inspection environment to the pre-inspection and post-inspection subsystems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.2
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• pp.3-11
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• 2010

In this paper we propose a new algorithm to detect the irises of both eyes from a human face. Using the separability filter, the algorithm first extracts blobs(intensity valleys) as the candidates for the irises. Next, for each pair of blobs, the algorithm computes a cost using Hough transform and separability filter to measure the fit of the pair of blobs to the image. And then, the algorithm selects a pair of blobs with the smallest cost as the irises of both eyes. As the result of the experiment using 150 faces images without spectacles, the success rate of the proposed algorithm was 97.3% for the best case and 95.3% for the worst case.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.11
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• pp.1637-1644
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• 2003

Stereoscopic particle image velocimetry is a measurement technique to acquire three dimensional velocity field by two cameras. With a laser sheet illumination, the third velocity component can be deduced from out-of$.$plane velocity components using a stereoscopic matching method. Most industrial fluid flows are three dimensional turbulent flows, so it is necessary to use the stereoscopic PIV measurement method. However the existing stereoscopic PIV system seems hard to use since it is very expensive and complex. In this study we have developed a Miniature Stereo-PIV(MSPIV) system based on the concept of the Miniature PIV system which we have already developed. In this paper, we address the design and some primitive experimental results of the Miniature Stereo-PIV system. The Miniature Stereo-PIV system features relatively modest performances, but is considerably smaller, cheaper and easy to handle. The proposed Miniature Stereo-PIV system uses two one-chip-only CMOS cameras with digital output. Only two other chips are needed, one for a buffer memory and one for an interfacing logic that controls the system. Images are transferred to a personal computer (PC) via its standard parallel port. No extra hardware is required (in particular, no frame grabber board is needed).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1126-1139
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• 1994

This paper proposes an effective recognition system which recognizes the mixed document consisting of handwritten korean/alphanumeric texts and graphic images. In the preprocessing step, an input image is binarized by the proposed thresholding scheme, then graphic and character regions are separated by using connected components and chain codes. Separated Korean characters are merged based on partial recognition and their character types and sized. In the character recognition step, we use the branch and bound algorithm based on DP matching costs to recognize Korean characters. Also we recognize alphanumeric characters using several robust features. Finally we use a dictionary and information of a recognition step to correct wrong recognition results. Computer simulation with several test documents shows what the proposed algorithm recognized effectively handwritten mixed texts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.526-534
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• 2006

In this paper we propose a new algorithm to detect the irises of both eyes from a human face. Using the separability filter, the algorithm first extracts blobs(intensity valleys) as the candidates for the irises. Next, for each pair of blobs. the algorithm computes a cost usings Hough transform and separability later to measure the fit of the pair of blobs to the image. And then, the algorithm selects a pair of blobs with the smallest cost as the irises of both eyes. As the result of the experiment using 150 faces images without spectacles, the success rate of the proposed algorithm was 97.3% for the best case and 95.3% for the worst case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.1000-1009
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• 2007

This paper presents a content adaptive interpolation (CAI) for intra deinterlacing. The CAI consists of three steps: pre-processing, content classification, and adaptive interpolation. There are also three main interpolation methods in our proposed CAI, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different do-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the CAI method performs better than previous techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.