• The Transactions of the Korea Information Processing Society
• /
• v.3 no.5
• /
• pp.1329-1336
• /
• 1996

In many image compression applications, the discrete cosine transform (DCY) is well known for is highly efficient coding performance. However, it produces undesirable block artifacts in low-bit rate coding. In addition, in many practical applications, faster computation and easier VLST implementation of DCT coefficients are also important issues. The removal of the block artifacts and faster DCT computation are therefor of practical interest. In this paper, a modified DCTcomputation scheme was investigated, which provides a simple efficient solution to the reduction of the block artifacts while achieving faster computation. We have applied the new ap-proach to the low-bit rate coding and decoding of images. Simulation results on real images have verified the improved performance of the proposed method over the standar d method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.25 no.6_1
• /
• pp.507-516
• /
• 2007

Segmentation and organization of the LiDAR (Light Detection and Ranging) data of the Earth's surface are difficult tasks because the captured LiDAR data are composed of irregularly distributed point clouds with lack of semantic information. The reason for this difficulty in processing LiDAR data is that the data provide huge amount of the spatial coordinates without topological and/or relational information among the points. This study introduces LiDAR data segmentation technique by utilizing histograms of the LiDAR height image data and analyzing roof shape for 3D reconstruction and visualization of the buildings. One of the advantages in utilizing LiDAR height image data is no registration required because the LiDAR data are geo-referenced and ortho-projected data. In consequence, measurements on the image provide absolute reference coordinates. The LiDAR image allows measurement of the initial building boundaries to estimate locations of the side walls and to form the planar surfaces which represent approximate building footprints. LiDAR points close to each side wall were grouped together then the least-square planar surface fitting with the segmented point clouds was performed to determine precise location of each wall of an building. Finally, roof shape analysis was performed by accumulated slopes along the profiles of the roof top. However, simulated LiDAR data were used for analyzing roof shape because buildings with various shapes of the roof do not exist in the test area. The proposed approach has been tested on the heavily built-up urban residential area. 3D digital vector map produced by digitizing complied aerial photographs was used to evaluate accuracy of the results. Experimental results show efficiency of the proposed methodology for 3D building reconstruction and large scale digital mapping especially for the urban area.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.3
• /
• pp.288-296
• /
• 1992

This paper describes an improved 2D QPS(quadrature polar separable) filter design and its applications to texture processing. The filter kernel pair consists of the product of a radial weighting function based on the finite PSS (prolate spheroidal sequences) and an exponential at tenuation function for the orientational angle. It is quadrature and polar separable in the frequency domain. It is near optimal in the energy loss because we let the orientational angle function approximate the radial weighting function. The filter frequency characteristics is easy to control as it depends only upon the design specifications such as the bandwidth, the directional angle, the attenuation constant, and the shift constant of the central frequency. Some applications of the filter in texture processing, such as the generation of the texture image, the estimation of orientation angles, and the segmentations for the synthetic texture image, are considered. The result shows that the filter with the wide bandwidth can be used for the generation of discrimination of the strong orientational textures and the segmentation results are good.

• Journal of Korea Multimedia Society
• /
• v.11 no.9
• /
• pp.1296-1301
• /
• 2008

Ultrasonography constructs pictures of areas inside the body needs in diagnosis by bouncing high-enorgy sound waves(ultrasound) off internal tissues or organs. In constructing an ultrasonographic image, the weakness of bounding signals induces noises and detailed differences of brightness, so that having a difficulty in detecting and diagnosing with the naked eyes in the analysis of ultrasonogram. Especially, the difficulty is extended when diagnosing muscle areas by using ultrasonographic images in the musculoskeletal test. In this paper, we propose a novel image processing method that computationally extracts a muscle area from an ultrasonographic image to assist in diagnosis. An ultrasonographic image consists of areas corresponding to various tissues and internal organs. The proposed method, based on features of intensity distribution, morphology and size of each area, extracts areas of the fascia, the subcutaneous fat and other internal organs, and then extracts a muscle area enclosed by areas of the fascia. In the extraction of areas of the fascia, a series of image processing methods such as histogram stretching, multiple operation, binarization and area connection by labeling is applied. A muscle area is extracted by using features on relative position and morphology of areas for the fascia and muscle areas. The performance evaluation using real ultrasonographic images and specialists' analysis show that the proposed method is able to extract target areas being approximate to real muscle areas.

• Journal of Biosystems Engineering
• /
• v.26 no.2
• /
• pp.147-154
• /
• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• The Transactions of the Korea Information Processing Society
• /
• v.2 no.2
• /
• pp.199-208
• /
• 1995

In this paper, a neural network architecture which can extract map symbols by being based on the results of physiological and neuropsychological studies on pattern recognition is proposed. This network is composed of multi-layers and synaptic activities of combining layers are implemented by spatial filters which approximate receptive fields of optic nerve cells. In pattern recognition which is followed by color classification for extracting of map symbols from input image, this network is searching for candidatepoints in lower layers (layer 2, 3) by using local features such as lines and end-points and then processing symbols recognition on those points in upper layer(layer 4) by using global features.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.20 no.5
• /
• pp.1524-1533
• /
• 1996

Risidual stress of cylindrical glass rods are measured by photoelasticity to study the variation of stresses with respect to heat treatment temperatures. In order to measure the stresses accurately, fringe sharpening and multiplication techniques are applied to the determination of photoelastic fringe orders. Filon's separationmethod is used to resolve circumferential and redial stress ocmponents from isochromatic fringes which are the same as in-plane maximum shearing stresses. According to the photoelastic measurements, residual stress is increased as the heat treatment temperature of the rods is raised from $560^{\circ}C$ to $650^{\circ}C$ All the circumferential stress components are changed from tensile stresses to compressive ones at approximate $R_m$/$R_o$ = 0.6, where $R_o$/ is outer radius and $R_m$any measured radius. This analysis shows that residual stresses of the glass rods approach zero if the rods are heat-treated near the strain point.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.607-614
• /
• 1996

A computer vision based automatic intelligent sorting system for dried oak mushrooms has been developed. The developed system was composed of automatic devices for mushroom feeding and handling, two sets of computer vision system for grading , and computer with digital I/O board for PLC interface, and pneumatic actuators for the system control. Considering the efficiency of grading process and the real time on-line system implementation, grading was done sequentially at two consecutive independent stages using the captured image of either side. At the first stage, four grades of high quality categories were determined from the cap surface images and at the second stage 8 grades of medium and low quality categories were determined from the gill side images. The previously developed neuro-net based mushroom grading algorithm which allowed real time on-line processing was implemented and tested. Developed system revealed successful performance of sorting capability of approximate y 5, 000 mushrooms/hr per each line i.e. average 0.75 sec/mushroom with the grading accuracy of more than 88%.

• The KIPS Transactions:PartB
• /
• v.9B no.1
• /
• pp.99-104
• /
• 2002

Existing methods of object tracking use template matching, re-detection of object boundaries or motion information. The template matching method requires very long computation time. The re-detection of object boundaries may produce false edges. The method using motion information shows poor tracking performance in moving camera. In this paper, a robust object tracking algorithm is proposed, using projected motion and histogram intersection. The initial object image is constructed by selecting the regions of interest after image segmentation. From the selected object, the approximate displacement of the object is computed by using 1-dimensional intensity projection in horizontal and vortical direction. Based on the estimated displacement, various template masks are constructed for possible orientations and scales of the object. The best template is selected by using the modified histogram intersection method. The robustness of the proposed tracking algorithm has been verified by experimental results.

• Journal of the Korea Computer Graphics Society
• /
• v.25 no.5
• /
• pp.11-20
• /
• 2019

With the development of deep learning technology, research is being actively carried out on user-friendly interfaces that are suitable for use in virtual reality or augmented reality applications. To support the interface using the user's hands, this paper proposes a deep learning-based fingertip detection method to enable the tracking of fingertip coordinates to select virtual objects, or to write or draw in the air. After cutting the approximate part of the corresponding fingertip object from the input image with the Grad-CAM, and perform the convolution neural network with Atrous Convolution for the cut image to detect fingertip location. This method is simpler and easier to implement than existing object detection algorithms without requiring a pre-processing for annotating objects. To verify this method we implemented an air writing application and showed that the recognition rate of 81% and the speed of 76 ms were able to write smoothly without delay in the air, making it possible to utilize the application in real time.