• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1229-1237
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• 2011

The volume ray-casting method is one of the direct volume rendering methods that produces high-quality images as well as manipulates semi-transparent object. Although the volume ray-casting method produces high-quality image by sampling in the region of interest, its rendering speed is slow since the color acquisition process is complicated for repetitive memory reference and accumulation of sample values. Recently, the GPU-based acceleration techniques are introduced. However, they require pre-processing or additional memory. In this paper, we propose efficient point-primitive based method to overcome complicated computation of GPU ray-casting. It presents semi-transparent objects, however it does not require preprocessing and additional memory. Our method is fast since it generates point-primitives from volume dataset during sampling process and it projects the primitives onto the image plane. Also, our method can easily cope with OTF change because we can add or delete point-primitive in real-time.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.1-5
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• 2015

With the help of fast growing multimedia technology and high preference for users of large screens, the newest video coding standard, HEVC (High Efficiency Video Coding) high-quality video compression), has been introduced. Therefore, the high definition image services which are four times more clear than conventional HD video, are getting popular. JPEG 2000 also has stated to support 4K and 8K UHD. As a result, it requires fast processing technology to read and write UHD images. This paper introduces a study on fast parallel processing technology for UHD images. For this purpose, first, JPEG 2000 is reviewed and a GPU based parallel implementation is proposed for a preprocessing of color conversion stage. The parallelled algorithm is implemented with OpenCL (Open Computing Language). The simulation results show that the proposed method shows 5 times performance improvements on processing speed for 4K UHD over the method using threads.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.636-642
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• 2017

In this paper, We proposed a novel hand-gesture recognition algorithm using concentric-circle expanding and tracing. The proposed algorithm determines region of interest of hand image through preprocessing the original image acquired by web-camera and extracts the feature of hand gesture such as the number of stretched fingers, finger tips and finger bases, angle between the fingers which can be used as intuitive method for of human computer interaction. The proposed algorithm also reduces computational complexity compared with raster scan method through referencing only pixels of concentric-circles. The experimental result shows that the 9 hand gestures can be recognized with an average accuracy of 90.7% and an average algorithm execution time is 78ms. The algorithm is confirmed as a feasible way to a useful input method for virtual reality, augmented reality, mixed reality and perceptual interfaces of human computer interaction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.481-483
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• 2018

Recently, as the demand for electronic communication equipment increases, the importance of image and signal processing is increasing. However, noise is generated in digital signal due to various causes during transmission and reception, lowering equipment reliability and causing malfunction. Particularly, since AWGN may be found in most electronic equipments, AWGN removal is mandatorily performed as a preprocessing phase in various fields, such as image recognition, extraction, and segmentation. In the present paper, an AWGN removal algorithm which considers high frequency components is proposed. Conventional methods show relatively inadequate performance in images with high frequency components. To overcome this problem, proposed is a filter algorithm that add or subtract difference images in the local mask. And to verify performance of the proposed algorithm, PSNR and enlarged images are used to compare with the existing methods.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.357-368
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• 2003

Evaluation of shape similarity for 3D models is essential in many areas - medicine, mechanical engineering, molecular biology, etc. Moreover, as 3D models are commonly used on the Web, many researches have been made on the classification and retrieval of 3D models. In this paper, we describe methods for 3D shape representation and major concepts of similarity evaluation, and analyze the key features of recent researches for shape comparison after classifying them into four categories including multi-resolution, topology, 2D image, and statistics based methods. In addition, we evaluated the performance of the reviewed methods by the selected criteria such as uniqueness, robustness, invariance, multi-resolution, efficiency, and comparison scope. Multi-resolution based methods have resulted in decreased computation time for comparison and increased preprocessing time. The methods using geometric and topological information were able to compare more various types of models and were robust to partial shape comparison. 2D image based methods incurred overheads in time and space complexity. Statistics based methods allowed for shape comparison without pose-normalization and showed robustness against affine transformations and noise.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.49-57
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• 2010

In this thesis, We studied on hand-written character recognition, that characters entered into a digital input device and remove noise and separating character elements using preprocessing. And processed character images has done thinning and 3-level wavelet transform for making normalized image and reducing image data. The structural method among the numerical Hangul recognition methods are suitable for recognition of printed or hand-written characters because it is usefull method deal with distortion. so that method are applied to separating elements and analysing texture. The results show that recognition by analysing texture is easily distinguished with respect to consonants. But hand-written characters are tend to decreasing successful recognition rate for the difficulty of extraction process of the starting point, of interconnection of each elements, of mis-recognition from vanishing at the thinning process, and complexity of character combinations. Some characters associated with the separation process is more complicated and sometime impossible to separating elements. However, analysis texture of the proposed character recognition with the exception of the complex handwritten is aware of the character.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.651-662
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• 2010

This paper describes a method to identify objects for autonomous navigation of an outdoor mobile robot. To identify objects, the robot recognizes the object from an image taken by moving robot on outdoor environment. As a beginning, this paper presents the candidates for a segment of region to building of artificial object, sky and trees of natural objects. Then we define their characteristics individually. In the process, we segment the regions of the objects included by preprocessing using multiple features. Multiple features are HSI, line segments, context information, hue co-occurrence matrix, principal components and vanishing point. An analysis of building identifies the geometrical properties of building facet such as wall region, windows and entrance. The building as intersection in vertical and horizontal line segment of vanishing point extracts the mesh. The wall region of building detect by merging the mesh of the neighbor parallelograms that have similar colors. The property estimates the number of story and rooms in the same floors by merging skewed parallelograms of the same color. We accomplish the result of image segmentation using multiple features and the geometrical properties analysis of object through experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.359-367
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• 2004

The postprocessing to remove false minutia is important because the extraction of true minutia affects the performance as a key factor in fingerprint identification system. In this paper, we propose an efficient postprocessing algorithm for removing false minutia among the extracted candidates in a thinned image. The proposed algorithm removes false minutia in three steps by using various information in the acquired fingerprint image: the structural information of minutia (end point and bifurcation), the inherent characteristics of fingerprint, and the quality of acquired images. Under Intel Celeron processor environment with 248${\times}$292 images acquired by optic device, the experiments showed that the proposed algorithm efficiently removed false minutia while preserving true minutia. Moreover, the proposed algorithm takes 0.0154 second, which is very small compared to the time for preprocessing (0.343 second).

• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.13-20
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• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.34-42
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• 2007

In this paper, we propose a staff-line detection and removal algorithm from a music score image obtained by a mobile phone camera. As a preprocessing technique to recognize a music score image, staff-line detection and removal should be efficiently applied to the skewed or curved images. The proposed method detects a staff-line by dividing a staff according to the degree of distortion. The number of division is calculated by dividing a staff repletely until an average of differences of y coordinates in every divided position is smaller than a threshold. Therefore, the number of division can be adaptively estimated according to the degree of the distortion. For an experiment, we make various kinds of images by rotating one from $1^{\circ}\;to\;3^{\circ}$ or curving slightly upward. The results show that the proposed method performed well on the experiment images.