• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11C
• /
• pp.1029-1039
• /
• 2009

In this paper, the existing disparity aquisition algorithms were analyzed, on the bases of which a disparity generation technique that is superior in accuracy to the generation time was proposed. Basically it uses a pixel-by-pixel motion estimation technique. It has a merit of possibility of a high-speed operation. But the motion estimation technique has a disadvantage of lower accuracy because it depends on the similarity of the matching window regardless of the distribution characteristics of the texture in an image. Therefore, an enhanced technique to increase the accuracy of the disparity is required. This paper introduced a variable-sized window matching technique for this requirement. By the proposed technique, high accuracies could be obtained at the homogeneous regions and the object edges. A hardware to generate disparity image was designed, which was optimized to the processing speed so that a high throughput is possible. The hardware was designed by Verilog-HDL and synthesized using Hynix $0.35{\mu}m$ CMOS cell library. The designed hardware was operated stably at 120MHz using Cadence NC-VerilogTM and could process 15 frames per second at this clock frequency.

• Journal of Digital Contents Society
• /
• v.15 no.6
• /
• pp.737-743
• /
• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Korea Society of Computer and Information
• /
• v.5 no.2
• /
• pp.60-70
• /
• 2000

As a result of remarkable developments in computer technology, the image retrieval system that can efficiently retrieve image data becomes a core technology of information-oriented society. In this paper, we implemented the Hierarchical Image Retrieval System for content-based image data retrieval. At the first level, to get color information, with improving the indexing method using color distribution characteristic suggested by Striker et al., i.e. the indexing method considering local color distribution characteristics, the system roughly classifies images through the improved method. At the second level, the system finally retrieves the most similar image from the image queried by the user using the shape information about the image groups classified at the first level. To extract the shape information, we use the Improved Moment Invariants(IMI) that manipulates only the pixels on the edges of objects in order to overcome two main problems of the existing Moment Invariant methods large amount of processing and rotation sensitiveness which can frequently be seen in the Directive Histogram Intersection technique suggested by Jain et al. Experiments have been conducted on 300 automobile images And we could obtain the more improved results through the comparative test with other methods.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.1
• /
• pp.89-97
• /
• 2008

In this paper, we present a method for the enhancement of marker detection correctness and marker recognition speed by using artificial neural network. Contours of objects are extracted from the input image. They are approximated to a list of line segments. Quadrangles are found with the geometrical features of the approximated line segments. They are normalized into exact squares by using the warping technique and scale transformation. Feature vectors are extracted from the square image by using principal component analysis. Artincial neural network is used to checks if the square image is a marker image or a non-marker image. After that, the type of marker is recognized by using an artificial neural network. Experimental results show that the proposed method enhances the correctness of the marker detection and recognition.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.3
• /
• pp.179-187
• /
• 2008

In this paper, we apply a set of algorithms to classily normal and cancer nucleus from uterine cervical pap-smear images. First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining $1{\times}400$ microscope magnification. Then, we remove the background from images with the histogram distributions of RGB regions. We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm. Various binarization, cumulative entropy, masking algorithms are used in that process. Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features - directional information, the size of nucleus, and area ratio - with fuzzy membership functions and deciding rules we devised. The experimental result shows our method has low false recognition rate.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.10
• /
• pp.118-125
• /
• 1998

We introduce an adaptive finite state vector quantization using a new side match distortion function. The conventional side match distortion function can make the gray level transition between the block bounddaries as smooth as possible and proper state codebooks in the flat areas where the spatial correlations are high. But it can't make proper codebooks in the edge areas where the spatial correlations are not high. The proposed distortion function adds the variances which represent the image characteristics to the conventional side match distortion function as weighted values. Then it can select better state codebooks than the conventional side match distortion function. Also if it predicts a wrong state, the proposed quantizer can correct the state. As a result, we can obtain the satisfiable image quality.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.1
• /
• pp.81-92
• /
• 1999

In this paper, we propose a new image segmentation algorithm which can be use din object-based image coding applications such as MPGA-4. Since the conventional objet segmentation methods based on mathematical morphology tend to yield oversegmented results, they normally need a postprocess which merges small regions to obtain a larger one. To solve this oversegmentation problem, in this paper, we prosed a block-based segmentation algorithm that can identify large texture regions in the image. Also, by applying the watershed algorithm to the image blocks between the homogeneous regions, we can obtain the exact pixel-based contour. Experimental results show that the proposed algorithm yields larger segments, particularly in the textural area, and reduces the computational complexities.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.5 no.5
• /
• pp.471-476
• /
• 2010

In this paper, we propose an effective method that recognizes the vehicle license plate using RGB color information and back-propagation neural network algorithm. First, the image of the vehicle license plate is adjusted by the Mean of Blue values in the vehicle plate and two candidate areas of Red and Green region are classified by calculating the differences of pixel values and the final Green area is searched by back-propagation algorithm. Second, our method detects the area of the vehicle plate using the frequence of the horizontal and the vertical histogram. Finally, each of codes are detected by an edge detection algorithm and are recognized by error back-propagation algorithm. In order to evaluate the performance of our proposed extraction and recognition method, we have run experiments on a new car plates. Experimental results showed that the proposed license plate extraction is better than that of existing HSI information model and the overall recognition was effective.

• Journal of Korea Game Society
• /
• v.14 no.5
• /
• pp.97-106
• /
• 2014

This study proposes an efficient method to model various and diverse leaves required to express digital plants such as flowers and trees in virtual landscape easily and intuitively. The proposed procedural method divides a leaf mainly into a blade and vein thereby detecting contours from binary images that correspond to blades and generating leaves by modeling leaf veins procedurally based on the detected contours. First of all, a complicated leaf vein structure is divided into main veins, lateral veins, and tertiary vein while all veins grow procedurally directing from start auxin to destination auxin. Here, to calculate destination auxin required for growth automatically, approximated contours from binary images that correspond to blades are found thereby calculating candidate destination auxin. Finally, natural digital leaves are generated by applying a color combination method. Through the proposed method, natural and various leaves can be generated and whether the proposed method is efficient or not is verified through the experiment.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.1
• /
• pp.273-285
• /
• 2000

While page segmentation is an important step in document recognition, there haven's been many researches on it. More improvement is still needed on the segmentation of document elements in complicated or color documents. In this paper, I present a new page segmentation method which can segment pages with multiple columns, dotted lines, graphics, and photographs. I extract all connected components using contour following and combine them depending on the size and positional information of them. Separate text location is done for non-text color regions to extract possible text lines. To see the performance of the proposed method, experiments are done for 180 documents. Four commercial OCR programs are also tested and the proposed method showed the best result.