• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.43-51
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• 1999

Producing the relevant information (features) from the CAD models of CAM, called feature recognition or extraction, is the essential stage for the integration of CAD and CAM. Most feature recognition methods, however, have problems in the recognition of intersecting features because they do not handle the intersection geometry properly. In this paper, we propose a machining feature recognition algorithm, which has a solid model consisting of orthogonal primitives as input. The algorithm calculates candidate features and constitutes the Intersection Geometry Matrix which is necessary to represent the spatial relation of candidate features. Finally, it recognizes machining features from the proposed candidate features dividing and growing systems using half space and Boolean operation. The algorithm has the following characteristics: Though the geometry of part is complex due to the intersections of design primitives, it can recognize the necessary machining features. In addition, it creates the Maximal Feature Volumes independent of the machining sequences at the feature recognition stage so that it can easily accommodate the change of decision criteria of machining orders.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.52-57
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• 2007

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.544-548
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• 1999

This study is about development of Feature-based Solid Modeling system in integrated CAD/CAM environment. Parasolid modeling kernel and HOOPS/3D graphics library was used to develop this system in PC level. System feature library was defined using both procedural and declarative approach method. The raw stock is created by boolean operator using design primitives, and a part is designed that pre-defined feature is removed from the raw stock. This method is called "DSG(Destructive Solid Geometry)" and basic constructive operator of this system. This is not complete system and only the first step to develop Feature-based Solid Modeling System using Parasolid. We will add more powerful functionality and flexible GUI in Windows.n Windows.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.6
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• pp.121-126
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• 2006

Snap-fit is being used in manufacture of plastic products. Integral features using snap-fit are classified as locks, locators and enhancements. Locking features complete the process of attachment by providing physical interference to prevent separation. Looking feature pairs consist of two components, i.e., a flexible latch and a rigid catch and require particular care and attention for their selection. We can make several locking feature pairs by selecting latch and catch, but some parts restrict freedom of selection. Therefore, part designers must know the characteristic properties of generic locking feature forms as considering a specific design problem. In this paper, it has been presented about problem of small size products using locking feature and then introduced new locking feature applicable to small parts.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.591-600
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• 2013

This paper addresses a feature-based robust watermarking scheme for digital images using a local invariant features of SURF (Speeded-Up Robust Feature) descriptor. In general, the feature invariance is exploited to achieve robustness in watermarking schemes, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are not considered carefully in security perspective. We propose embedding and detection methods where the watermark is bound with circular areas and inserted into extracted circular feature regions. These methods enhance the robustness since the circular watermark is inserted into the selected non-overlapping feature regions instead of entire image contents. The evaluation results for repeatability measures of SURF descriptor and robustness measures present the proposed scheme can tolerate various attacks, including signal processing and geometric distortions.

• Korean Journal of Computational Design and Engineering
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• v.4 no.4
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• pp.295-311
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• 1999

An increasing attention is paid to STEP-NC, the next generation CNC controller interfacing STEP-compatible data. In this paper, we first propose an Architecture for the STEP-NC (called FBCC: Feature Baled CNC Controller) accepting feature code compatible with STEP-data, and executing NC motion feature by feature while monitoring the execution status. The main thrust of the paper has been to develop an automatic on-line tool path generation and modification scheme for milling operation. The tool path it generated iota each feature by decomposing into a finite number of primitive features. The key function in the new scheme is haw to accommodate unexpected execution results. In our scheme, the too1 path plinker is designed to have a tracing capability iota the tool path execution so that a new path can be generated from the point where the operation is stopped. An illustrative example is included to show the capability of the developed algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.151-153
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• 2001

Feature Simplification is an essential method for multiple representations of spatial features in GIS. However, spatial features re various, complex and a alrge size. Among spatial features which describe spatial information. linear feature is the msot common. Therefore, an efficient linear feature simplification method is most critical for spatial feature simplification in GIS. This paper propose an original method, by which the problem of linear feature simplification is mapped into the signal processing field. This method avoids conventional geometric computing in existing methods and exploits the advantageous properties of wavelet transform. Experimental results are presented to show that the proposed method outperforms the existing methods and achieves the time complexity of O(n), where n is the number of points of a linear feature. Furthermore, this method is not bound to two-dimension but can be extended to high-dimension space.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.77-85
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• 2010

This paper proposes an efficient feature vector processing technique to guard the Speech Emotion Recognition (SER) system against a variety of noises. In the proposed approach, emotional feature vectors are extracted from speech processed by comb filtering. Then, these extracts are used in a robust model construction based on feature vector classification. We modify conventional comb filtering by using speech presence probability to minimize drawbacks due to incorrect pitch estimation under background noise conditions. The modified comb filtering can correctly enhance the harmonics, which is an important factor used in SER. Feature vector classification technique categorizes feature vectors into either discriminative vectors or non-discriminative vectors based on a log-likelihood criterion. This method can successfully select the discriminative vectors while preserving correct emotional characteristics. Thus, robust emotion models can be constructed by only using such discriminative vectors. On SER experiment using an emotional speech corpus contaminated by various noises, our approach exhibited superior performance to the baseline system.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.