• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.2
• /
• pp.79-89
• /
• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.

• Journal of Korea Multimedia Society
• /
• v.16 no.8
• /
• pp.914-926
• /
• 2013

Among the recent security systems, security system with fingerprint recognition gets many people's interests through the strengths such as exclusiveness, convenience, etc, in comparison with other security systems. The most important matters for fingerprint recognition system are reliability of matching between the fingerprint in database and user's fingerprint and rapid process of image processing algorithms used for fingerprint recognition. The existing fingerprint recognition system reduces the processing time by removing some processes in the feature extraction algorithms but has weakness of a reliability. This paper realizes the fingerprint recognition algorithm using MAMS(Multi-Access Memory System) for both the rapid processing time and the reliability in feature extraction and matching accuracy. Reliability of this process is verified by the correlation between serial processor's results and MAMS-PP64's results. The performance of the method using MAMS-PP64 is 1.56 times faster than compared serial processor.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.7
• /
• pp.83-88
• /
• 2001

In this paper, matched field processing algorithm robust to environmental mismatches in an ocean waveguide based on feature extraction is summarized. However, in applying this processor to localize a source there are two preliminary issues to be resolved. One is the number of eigenvectors to be extracted and the other is the number of environmental samples to be used. To determine these issues, the relation between the number of dominant modes propagating in a given ocean waveguide and that of eigenvectors to be extracted is analyzed. Then, the analysis results are confirmed by the subspace analysis. This analysis quantifies the similarity between the subspace spanned by the signal vectors and that spanned by the eigenvectors to be extracted. The error index is defined as a relative difference between the location estimated by the current processor and the real source location. It is identified that in the case of extracting the largest eigenvectors equal to the number of dominant modes in a given environment, the processor localizes the source successfully. From the numerical simulations, it is shown that use of at least 30 environmental samples guarantee stable performance of the proposed processor.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.2
• /
• pp.192-197
• /
• 2017

In this paper, image filter optimization method based on common sub-expression elimination is proposed for low-power image feature extraction hardware design. Low power and high performance object recognition hardware is essential for industrial robot which is used for factory automation. However, low area Gaussian gradient filter hardware design is required for object recognition hardware. For the hardware complexity reduction, we adopt the symmetric characteristic of the filter coefficients using the transposed form FIR filter hardware architecture. The proposed hardware architecture can be implemented without degradation of the edge detection data quality since the proposed hardware is implemented with original Gaussian gradient filtering algorithm. The expremental result shows the 50% of multiplier savings compared with previous work.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.978-983
• /
• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.6
• /
• pp.1095-1100
• /
• 2008

In a process of insect foot recognition, footprint segments as basic areas for recognition need to be extracted from scanned insect footprints and appropriate features should be found from the footprint segments in order to discriminate kinds of insects, because the characteristics of the features are important to classify insects. In this paper, we propose methods for automatic footprint segmentation and feature extraction. We use a Trace transform method in order to find out appropriate features from the extracted segments by the above methods. The Trace transform method builds a new type of data structure from the segmented images by functions using parallel trace lines and the new type of data structure has characteristics invariant to translation, rotation and reflection of images. This data structure is converted to Triple features by Diametric and Circus functions, and the Triple features are used for discriminating patterns of insect footprints. In this paper, we show that the Triple features found by the proposed methods are enough distinguishable and appropriate for classifying kinds of insects.

• Nuclear Engineering and Technology
• /
• v.56 no.9
• /
• pp.3740-3749
• /
• 2024

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSOLSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

• The Journal of the Korea Contents Association
• /
• v.19 no.1
• /
• pp.125-131
• /
• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.2
• /
• pp.122-129
• /
• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• Journal of Biosystems Engineering
• /
• v.29 no.1
• /
• pp.71-78
• /
• 2004

Real time and robust algorithm to extract the features of watermelon was developed from the remotely transmitted image of the watermelon. Features of the watermelon at the cultivation site such as size and shape including position are crucial to the successful tole-robotic operation and development of the cultivation data base. Algorithm was developed based on the concept of task sharing between the computer and the operator utilizing man-computer interface. Task sharing was performed based on the functional characteristics of human and computer. Identifying watermelon from the image transmitted from the cultivation site is very difficult because of the variable light condition and the complex image contents such as soil, mulching vinyl, straws on the ground, irregular leaves and stems. Utilizing operator's teaching through the touch screen mounted on the image monitor, the complex time consuming image processing process and instability of processing results in the watermelon identification has been avoided. Color and brightness characteristics were analyzed from the image area specified by the operator's teaching. Watermelon segmentation was performed using the brightness and color distribution of the specified imae processing area. Modified general Hough transform was developed to extract the shape, major and minor axes, and the position, of the watermelon. It took less than 100 msec of the image processing time, and was a lot faster than conventional approach. The proposed method showed the robustness and practicability in identifying watermelon from the wireless transmitted color image of the cultivation site.