• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.808-816
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• 2012

Face recognition has been actively studied and developed in various fields. In recent years, interest point extraction algorithms mainly used for object recognition were being applied to face recognition. The SURF(Speeded Up Robust Features) algorithm was used in this paper which was one of typical interest point extraction algorithms. Generally, the interest points extracted from human faces are less distinctive than the interest points extracted from objects due to the similar shapes of human faces. Thus, the accuracy of the face recognition using SURF tends to be low. In order to improve it, we propose a face recognition algorithm which performs interest point extraction by SURF and the Gabor wavelet transform to extract descriptors from the interest points. In the result, the proposed method shows around 23% better recognition accuracy than SURF-based conventional methods.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.33-41
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• 2011

In the information communication technology, it is world-widely apparent that trend movement from internet web to smartphone app by users demand and developers environment. So it needs kinds of appropriate technological responses from geo-spatial domain regarding this trend. However, most cases in the smartphone app are the map service and location recognition service, and uses of geo-spatial contents are somewhat on the limited level or on the prototype developing stage. In this study, app for extraction of corner point features using geo-spatial imagery and their linkage to database system are developed. Corner extraction is based on Harris algorithm, and all processing modules in database server, application server, and client interface composing app are designed and implemented based on open source. Extracted corner points are applied LOD(Level of Details) process to optimize on display panel. Additional useful function is provided that geo-spatial imagery can be superimposed with the digital map in the same area. It is expected that this app can be utilized to automatic establishment of POI (Point of Interests) or point-based land change detection purposes.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.359-364
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• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.940-947
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• 2015

This paper presents the region of interest pattern image extraction method on a display printed matrix pattern. Proposed method can not use conventional method such as laser, ultrasonic waves and touch sensor. It searches feature point and rotation angle using luminance and pattern reliable feature points of input image, and then it extracts region of interest. In order to extract region of interest, we simulate proposed method using pattern image written various angles on display panel. The proposed method makes progress using the OpenCV and the window program, and was designed using Verilog-HDL and was verified through the FPGA Board(xc6vlx760) of Xilinx.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.56-60
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• 2024

In this paper, we propose a SIFT feature-based dynamic stitching algorithm for image calibration and correction of a 360-degree surround view system. The existing surround view system requires a lot of processing time and money because in the process of image calibration and correction. The traditional marker patterns are placed around the vehicle and correction is performed manually. Therefore, in this study, images captured with four fisheye cameras mounted on the surround view system were distorted and then matched with the same feature points in adjacent images through SIFT-based feature point extraction to enable image stitching without a fixed marker pattern.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.212-216
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• 2024

Feature Selection have turned into the main point of investigations particularly in bioinformatics where there are numerous applications. Deep learning technique is a useful asset to choose features, anyway not all calculations are on an equivalent balance with regards to selection of relevant features. To be sure, numerous techniques have been proposed to select multiple features using deep learning techniques. Because of the deep learning, neural systems have profited a gigantic top recovery in the previous couple of years. Anyway neural systems are blackbox models and not many endeavors have been made so as to examine the fundamental procedure. In this proposed work a new calculations so as to do feature selection with deep learning systems is introduced. To evaluate our outcomes, we create relapse and grouping issues which enable us to think about every calculation on various fronts: exhibitions, calculation time and limitations. The outcomes acquired are truly encouraging since we figure out how to accomplish our objective by outperforming irregular backwoods exhibitions for each situation. The results prove that the proposed method exhibits better performance than the traditional methods.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.23-30
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• 2014

Feature matching using image descriptors is robust method used recently. However, mismatches occur in 3D transformed images, illumination-changed images and repetitive-pattern images. In this paper, we observe that there are a lot of mismatches in the images which have repetitive patterns. We analyze it and propose a method to eliminate these mismatches. MDMF(Multiple Descriptors-based Mismatch Filtering) eliminates mismatches by using descriptors of nearest several features of one specific feature point. In experiments, for geometrical transformation like scale, rotation, affine, we compare the match ratio among SIFT, ASIFT and MDMF, and we show that MDMF can eliminate mismatches successfully.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.51-61
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• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from an inertia sensor to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw angles, pitch angles, roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3
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• pp.452-458
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• 2013

Over the past several years, many studies have been carried out in the field of 3D data inspection systems. Several attempts have been made to improve the quality of manufactured parts. The introduction of laser sensors for inspection has made it possible to acquire data at a remarkably high speed. In this paper, a robust inspection technique for detecting defects in 3D pressed parts using laser-scanned data is proposed. Point cloud data are segmented for the extraction of features. These segmented features are used for shape matching during the localization process. An iterative closest point (ICP) algorithm is used for the localization of the scanned model and CAD model. To achieve a higher accuracy rate, the ICP algorithm is modified and then used for matching. To enhance the speed of the matching process, aKd-tree algorithm is used. Then, the deviation of the scanned points from the CAD model is computed.