• Journal of Biosystems Engineering
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• v.22 no.1
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• pp.30-40
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• 1997

A computer vision system was built to automatically grade the leaf tobacco. A color image processing algorithm was developed to extract shape, color and texture features. An improved back propagation algorithm in an artificial neural network was applied to grade the Burley type dried leaf tobacco. The success rate of grading in three-grade classification(1, 3, 5) was higher than the rate of grading in six-grade classification(1, 2, 3, 4, 5, off), on the average success rate of both the twenty-five local pixel-set and the sixteen local pixel-set. And, the average grading success rate using both shape and color features was higher than the rate using shape, color and texture features. Thus, the texture feature obtained by the spatial gray level dependence method was found not to be important in grading leaf tobacco. Grading according to the shape, color and texture features obtained by machine vision system seemed to be inadequate for replacing manual grading of Burely type dried leaf tobacco.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.991-1000
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• 2020

We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.66-77
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• 2011

We propose a framework for grouping photographic advertising images that employs a hierarchical indexing scheme based on efficient feature combinations. The study provides one specific application of effective tools for monitoring photographic advertising information through online and offline channels. Specifically, it develops a preprocessor for advertising image information tracking. We consider both global features that contain general information on the overall image and local features that are based on local image characteristics. The developed local features are invariant under image rotation and scale, the addition of noise, and change in illumination. Thus, they successfully achieve reliable matching between different views of a scene across affine transformations and exhibit high accuracy in the search for matched pairs of identical images. The method works with global features in advance to organize coarse clusters that consist of several image groups among the image data and then executes fine matching with local features within each cluster to construct elaborate clusters that are separated by identical image groups. In order to decrease the computational time, we apply a conventional clustering method to group images together that are similar in their global characteristics in order to overcome the drawback of excessive time for fine matching time by using local features between identical images.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.875-883
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• 2010

In this paper, we present a secure biometric hashing scheme for face recognition by random fusion of global and local features. The Fourier-Mellin transform and Radon transform are adopted respectively to form specialized representation of global and local features, due to their invariance to geometric operations. The final biometric hash is securely generated by random weighting sum of both feature sets. A fourfold key is involved in our algorithm to ensure the security and privacy of biometric templates. The proposed biometric hash can be revocable and replaced by using a new key. Moreover, the attacker cannot obtain any information about the original biometric template without knowing the secret key. The experimental results confirm that our scheme has a satisfactory accuracy performance in terms of EER.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.521-524
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• 2013

In this paper, the algorithm that recognizes the gesture by configuring the feature information obtained through Improved Higher Order Local Correlation Features as low dimensional gesture symbol was described. Since the proposed method doesn't require a lot of computations compared to the existing geometric feature based method or appearance based methods and it can maintain high recognition rate by using the minimum information, it is very well suited for real-time system establishment.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.1
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• pp.23-37
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• 2020

The quadratic error metric (QEM) algorithm has been frequently used for simplification of triangular surface models that utilize the vertex-pair algorithm. Simplified models obtained using such algorithms present the advantage of smaller storage capacity requirement compared to the original models. However, a number of cases exist where significant features are lost geometrically, and these features can generally be preserved by utilizing the advantages of the curvature-weighted algorithm. Based on the vertex-based geometric features, a method capable of preserving the geometric features better than the previous algorithms is proposed in this work. To validate the effectiveness of the proposed method, a simplification experiment is conducted using several models. The results of the experiment indicate that the geometrically important features are preserved well when a local feature is present and that the error is similar to those of the previous algorithms when no local features are present.

• Annual Conference of KIPS
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• 2009.04a
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• Journal of the Korean Society for information Management
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• v.26 no.3
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• pp.213-237
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• 2009

Local culture is a cultural heritage that has come down from generation to generation in the natural environment of a region. It includes history, tradition, natural features, art, and historic relics. The Academy of Korean Studies has complied "The Encyclopedia of Korean Local Culture" using those local culture contents. Local culture content shave the features of documentary, such as authenticating the source, and managing hierarchy structure. Thus, to deal with local culture contents, a "circular knowledge information management system" is sought for that helps basic, fragmentary, and high-level information to circulate to create new knowledge information within the system. A user of this circular knowledge information management system is able not only to collect data directly in it, but also to fetch data from other database. Besides, processing the collected data helps to create new knowledge information. But, it's very difficult to sustain the features of the original hierarchy bearing meaning contained in the various kinds of local culture contents when building a new database. Moreover, this kind of work needs many times of correction over a long period of time. Therefore, a system in which compilation, correction, and service can be done simultaneously is needed. Therefore, in this study, focusing on the case of "The Encyclopedia of Korean Local Culture", I propose a XML-based digital compilation management system that can express hierarchy information and sustain the semantic features of the local culture contents containing lots of ancient documents, and introduce the expanded functions developed to manage contents in the system.

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.185-194
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• 2016

The effect of artificial changes in geographical features on local wind was analyzed at the construction site of bridge and fill-up bank in the southern part of Haui-do. Geographic Information System (GIS) data and Computational Fluid Dynamics (CFD) model were used in this study. Three-dimensional numerical topography based on the GIS data for the target area was constructed for the surface boundary input data of the CFD model. The wind observations at an Automatic Weather Station (AWS) located in Haui-do were used to set-up the model inflows. The seasonal simulations were conducted. The differences in surface wind speed between after and before artificial changes in geographical features were analyzed. The surface wind speed decreases 5 to 20% at the south-western part and below 2% of the spatial average for salt field. There was also marked the effect of artificial changes in geographical features on local wind in the westerly wind case for the target area.

• ETRI Journal
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• v.46 no.4
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• pp.648-659
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• 2024

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.