• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.12C
• /
• pp.1231-1238
• /
• 2005

This paper presents a new fast encoding algorithm for image vector quantization that incorporates the partial distances of multiple features with a multidimensional look-up table (LUT). Although the methods which were proposed earlier use the multiple features, they handles the multiple features step by step in terms of searching order and calculating process. On the other hand, the proposed algorithm utilizes these features simultaneously with the LUT. This paper completely describes how to build the LUT with considering the boundary effect for feasible memory cost and how to terminate the current search by utilizing partial distances of the LUT Simulation results confirm the effectiveness of the proposed algorithm. When the codebook size is 256, the computational complexity of the proposed algorithm can be reduced by up to the $70\%$ of the operations required by the recently proposed alternatives such as the ordered Hadamard transform partial distance search (OHTPDS), the modified $L_2-norm$ pyramid ($M-L_2NP$), etc. With feasible preprocessing time and memory cost, the proposed algorithm reduces the computational complexity to below the $2.2\%$ of those required for the exhaustive full search (EFS) algorithm while preserving the same encoding quality as that of the EFS algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.5
• /
• pp.959-970
• /
• 1994

In this thesis, a printed score is read by using handy scanner and the recognition process is excuted in parallel, finally, on Mesh-Connected Computer. What is read is classified into certain patterns and is recognized, based on knowledge. The preprocessing steps are minimized and simple operations are used in the algorithm proposed in this thesis. The score symbols on a printed score can be recognized irrespective of their sizes but their diversity males it difficult to recognize them all, so it is programmed so as to recognize some symbols that is used necessarily and frequently. The recognized result is transformed into the MIDI standard file format. It is required to use a parallel processing system with multiprocessors because the high speed image processing is required. A digitized two-dimensional image is appropriate in processing on the SIMD Mesh-Connected Computer(MCC). Therefore, we explain this architecture and present parallel algorithm using SIMD MCC with n processors that achieves time complexity0(n).

• The KIPS Transactions:PartA
• /
• v.15A no.2
• /
• pp.75-86
• /
• 2008

Black/white comic rendering is one of the researches in the field of non-photorealistic rendering(NPR). Black/white comics have been produced manually as yet. But these previous systems require lots of time and manual work. So we propose the COmics Rendering system on VIdeo Stream (CORVIS) which transforms video streams into black/white comic cuts. Stylized font, one of comic representations, can be used to express onomatopoeic words and mimetic dialogue exaggeratively. But current comic generation systems do not provide enough effects of stylized font. This paper proposes a model for stylized fonts to express various effects. Effects of stylized fonts we proposed include geometric deformations. Thus we could represent stylized fonts on the still cut of movies and the background texture on a cuts of plain black/white comics. The final quality of our system produced is good enough to compare with manual black/white comics.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.235-245
• /
• 2012

This paper introduce a quiz game contents based on gesture interface. We analyzed the off-line quiz games, extracted its presiding components, and digitalized them so that the proposed game contents is able to substitute for the off-line quiz games. We used the Kinect camera to obtain the depth images and performed the preprocessing including vertical human segmentation, head detection and tracking and hand detection, and gesture recognition for hand-up, hand vertical movement, fist shape, pass and fist-and-attraction. Especially, we defined the interface gestures designed as a metaphor for natural gestures in real world so that users are able to feel abstract concept of movement, selection and confirmation tangibly. Compared to our previous work, we added the card compensation process for completeness, improved the vertical hand movement and the fist shape recognition methods for the example selection and presented an organized test to measure the recognition performance. The implemented quiz application program was tested in real time and showed very satisfactory gesture recognition results.

• Journal of the Institute of Convergence Signal Processing
• /
• v.6 no.1
• /
• pp.8-14
• /
• 2005

The implementation of vision system using CCD camera which measures the earing rate of aluminium CAN is represented in this paper. In order to optimize the input image, the object of the input image is separated and the position of the image is calibrated. In the preprocessing, the definition of image is improved by the histogram equalization, and then the edges of the input image are detected by the Robert mask. The heights of the four ears and angles of the aluminium CAN are measured manually with the digital vernier calipers in industry. It takes 30 seconds to measure manually the height of one direction of the aluminium CAN at least three times. However, when the proposed system in this paper is applied, it takes 0.02 seconds only. In conclusion, the efficiency of the proposed system is higher than that of the system used in the industry.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.815-822
• /
• 2003

Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we propose a fingerprint recognition technique by using minutiae linking information. Recognition process have three steps ; preprocessing, minutiae extraction, matching step based on minutiae pairing. After extracting minutiae of a fingerprint from its thinned image for accuracy, we introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection with low cost in comparison stage of two fingerprints. This algorithm is invariable to translation and rotation of fingerprint. The matching algorithm was tested on 500 images from the semiconductor chip style scanner, experimental result revealed the false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.7 no.4
• /
• pp.161-168
• /
• 2006

In this paper, we propose a method for the pattern inspection of LCD module using the color compensation and pattern matching. The pattern matching is generally used for the inspection method of LCD module at the industry. LCD module has many defections such as the brightness difference of the back light, the optic feature of liquid crystal, the difference of the light penetrated by driving LCD and the color difference by the lighting. The conventional method without the color compensation can not solve these defections and decreases the efficiency of inspecting LCD module. The method proposed to inspect defective badness through the pattern matching after it compensated color difference of the LCD occurred by the various causes. At first, it revises with setting by standard tone of color with the LCD pattern of the reference image. And It perform the preprocessing and pattern matching algorithm on the compensated image. In experiment, we confirmed that this algorithm is useful to detect some defections of LCD module. The proposed methods was easy to detect the faulty product.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1061-1070
• /
• 2021

In this manuscript, we tried to improve the performance of the FC-DenseNet by applying an attention gate for the classification of cropping areas. The attention gate module could facilitate the learning of a deep learning model and improve the performance of the model by injecting of spatial/spectral weights to each feature map. Crop classification was performed in the onion and garlic regions using a proposed deep learning model in which an attention gate was added to the skip connection part of FC-DenseNet. Training data was produced using various PlanetScope satellite imagery, and preprocessing was applied to minimize the problem of imbalanced training dataset. As a result of the crop classification, it was verified that the proposed deep learning model can more effectively classify the onion and garlic regions than existing FC-DenseNet algorithm.

• Journal of Broadcast Engineering
• /
• v.26 no.4
• /
• pp.419-428
• /
• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.12
• /
• pp.1596-1603
• /
• 2018

Rapidly detecting and warning of fires is necessary for minimizing human injury and property damage. Generally, when fires occur, both the smoke and the flames are generated, so fire detection systems need to detect both the smoke and the flames. However, most fire detection systems only detect flames or smoke and have the disadvantage of slower processing speed due to additional preprocessing task. In this paper, we implemented a fire detection system which predicts the flames and the smoke at the same time by constructing a CNN model that supports multi-labeled classification. Also, the system can monitor the fire status in real time by using Grad-CAM which visualizes the position of classes based on the characteristics of CNN. Also, we tested our proposed system with 13 fire videos and got an average accuracy of 98.73% and 95.77% respectively for the flames and the smoke.