• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.2
• /
• pp.42-60
• /
• 1992

An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

• Journal of Broadcast Engineering
• /
• v.19 no.5
• /
• pp.578-589
• /
• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• KSCI Review
• /
• v.2 no.1
• /
• pp.130-139
• /
• 1995

This paper suggests the method to shorten the search area by using edge detection and subimage partition. For the purpose reduce encoding time, The Domain areas are reduced 1/64 by partitioning original image to subimage, and classified them into edge area and shade area so that detect only the area in the same class. for achieving an encoding with good fidelity, tried to differ the search method as the threshold value of edge which is included in subimage, and compared the compression rate and fidelity when set the size of range block as $4{\times}4$ and $8{\times}8$.

• Journal of Electrical Engineering and information Science
• /
• v.1 no.1
• /
• pp.77-81
• /
• 1996

Considering the human visual masking property, a modified relationship between the activity function and the visibility threshold is developed. This leads to a novel objective appraisal method for blocking effects in a lossy subimage coding by virtue of the human visual sensitivity. The appraisal criterion is examined using a series of reconstructed images that are DCT-coded at various bit rates. Experimental results show that the presented blocking effect measure well agrees with the subjective ranking.

• Journal of The Korean Astronomical Society
• /
• v.49 no.4
• /
• pp.157-162
• /
• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.

• Journal of Korea Multimedia Society
• /
• v.6 no.6
• /
• pp.977-984
• /
• 2003

In this paper, we propose an image enhancement algorithm for panoramic infrared image. This method divides a panoramic infrared image into disjoint subimages and performs the contrast stretch for subimages using statistical characteristics of each subimage. But these cause blocking artifact in boundary regions of neighboring subimages which different to the statistical characteristic. To remove blocking artifact, we perform the contrast stretch for subimage's boundary regions using statistical characteristics of horizontally neighboring subimages. Experimental results show that the proposed method effectively improves their appearance to human viewers.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.4
• /
• pp.1066-1071
• /
• 1998

This paper aims to examine the relationship between the directional characteristics and the quality for fingerprint image as preprocessing stage for adative image enchancement. In order to do that, we transformed the original images into directional images and set up the subimage size of 16, 32, 64 and the direction of 1, 2, 3, 4. Then we extracted the accumulated directional value as the measurement of quality for fingerprint images. By using the clustering algirthm, we performed an analytic experiemnt with the result. Finally, we could extract the optimal subimage size and directional characteristics of fingerprint image.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.7
• /
• pp.1515-1522
• /
• 1997

Several image coding algorithms have been developed for the telecommunication and multimedia systems with high image quality and high compression ratio. In order to achieve low entropy and distortion, the system should pay great cost of computation time and memory. In this paper, the uniform cubic lattice is chosen for Lattice Vector Quantization (LVQ) because of its generic simplicity. As a transform coding, the Discrete Wavelet Transform (DWT) is applied to the images because of its multiresolution property. The proposed algorithm is basically composed of the biorthogonal DWT and the uniform cubic LVQ. The multiresolution property of the DWT is actively used to optimize the entropy and the distortion on the basis of the distortion-rate function. The vector codebooks are also designed to be optimal at each subimage which is analyzed by the biorthogonal DWT. For compression efficiency, the vector codebook has different dimension depending on the variance of subimage. The simulation results show that the performance of the proposed coding mdthod is superior to the others in terms of the computation complexity and the PSNR in the range of entropy below 0.25 bpp.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.7
• /
• pp.1193-1205
• /
• 1994

This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1999.06a
• /
• pp.171-176
• /
• 1999

Template matching is one of the most often used techniques for machine vision applications to find a template of size M$\times$M or subimage in a scene image of size N$\times$N. Most template matching methods, however, require pixel operations between the template and the image under analysis resulting in high computational cost of O(M2N2). So in this thesis, we present a two stage template matching method. In the first stage, we use a novel low cost feature whose complexity is approaching O(N2) to select matching candidates. In the second stage, we use conventional template matching method to find out the exact matching point. We compare the result with other methods in terms of complexity, efficiency and performance. Proposed method was proved to have constant time complexity and to be quite invariant to noise.