• Construction Engineering and Management
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• v.4 no.1 s.13
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• pp.48-54
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• 2003

An automated system for scanning and characterizing unbound aggregates, called the 'Laser-based Aggregate Scanning System'(LASS), has been developed at the University of Texas at Austin. The system uses a laser profiler to acquire and analyze true three-dimensional data on aggregate particles to measure various morphological properties. Tests have demonstrated that the system can rapidly and accurately measure grain size distribution and dimensional ratios, and can objectively quantify particle shape, angularity, and texture in a size invariant manner. In its present state of development, the LASS machine is a first-generation, laboratory testing device. With additional development, this technology is expected to provide high-quality, detailed information for laboratory and on-line quality control during aggregate production.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1357-1366
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• 1998

In this paper, we propose the method that extract shape features using the DCT(Discrete Cosine Transform) via simple invariant normalization. To retrieve effectively, we used measures, circularity and eccentricity, as filters to reduce the number of retrieved images. The experimental results show that our method is better than the methods of Fourier Descriptors and Moment Invariant for various leaf images.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.227-230
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• 2006

A content-based image retrieval system using MPEG-7 is designed and implemented in this thesis. The implemented system uses existing MPEG-7 Visual Descriptors. In addition, a new descriptor for efficient retrieval of bio images is proposed and utilized in the developed content-based image retrieval system. Comparing proposed CBSD(Compact Binary Shape Descriptor) with Edge Histogram Descriptor(EHD) and Region Shape Descriptor(RSD), it shows good retrieval performance in NMRR. The proposed descriptor is robust to large modification of brightness and contrast and especially improved retrieval performance to search images with similar shapes. Also proposed system adopts distributed architecture to solve increased server overload and network delay. Updating module of client efficiently reduces downloading time for metadata. The developed system can efficiently retrieve images without causing server's overload.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1902-1911
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• 1999

In this paper we present a content-based image retrieval algorithm using the visual feature vectors which describe the spatial characteristics of objects. The proposed technique uses the Gaussian mixture model(GMM) to represent multi-colored objects and the expectation maximization(EM) algorithm is employed to estimate the maximum likelihood(ML) parameters of the model. After image segmentation is performed based on GMM, the shape and color features are extracted from each object using Fourier descriptors and color histograms, respectively. Image retrieval consists of two steps: first, the shape-based query is carried out to find the candidate images whose objects have the similar shapes with the query image and second, the color-based query is followed. The experimental results show that the proposed algorithm is effective in image retrieving by using the spatial and visual features of segmented objects.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1509-1518
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• 2015

Because a logo is a medium that connects between consumers and corporations or brands, designing memorable logo images is vital. Although predicting logo's memorability for brand marketing is essential, there have been only few researches that deal with memorability of logo images. In this paper, we analyze the memorability characteristics in logo images by performing experiments based upon our proposed prediction method for logo image's memorability. Our proposed research consists of three phases: crowdsourcing for memorability computing, computational phase for logo image's memorability, and development of a prediction model. Using computed memorability of logo images by "Visual Memory Game," we analyze the different characteristics of logo's memorability. We first developed a novel computational method that reflects logo image's color and shape. Each computational method on color and shape are selected by comparing the correlations between result values and ground truth memorability. Selected computational value is then converged with generic image feature descriptors such as SIFT and HoG to make a prediction model of logo's memorability. Using our method, we obtain reasonable performances in predicting logo image's memorability.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.801-810
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• 2005

The lag time is one of the most important factors for estimating a flood runoff from streams. It is well known to be under the influence of the morphometric properties of basins which could be expressed by catchment shape descriptors. In this paper, the notion of the geometric characteristics of an equivalent ellipse proposed by Moussa(2003) is applied for calculating the lag time of geomorphological instantaneous unit hydrograph(GIUH) at the basin outlet. The lag time is obtained from the observed data of rainfall and runoff by using the method of moments suggested by Nash(1957), and the procedure based on geomorphology is used for GIUH. The relationships between the basin morphometric properties and the hydrological response are discussed as applied to 3 catchments In Korea. Additionally, the shapes of equivalent ellipse are examined how then are transformed from upstream area to downstream one. As a result, the relationship between the hydrological response and descriptors is shown to be comparatively good, and the shape of ellipse is presented to approach a circle along the river downwards. These results may be expanded to the estimation of hydrological response of ungauged catchment.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.