• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.353-361
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• 2001

A new stereoscopic codec. structure using MPEG-2 multiview profile is presented in this paper. In the suggested codec., the left image is coded with motion estimation in the base layer and the right image is coded with disparity estimation in the enhancement layer. Since it is possible to calculate rough motion of the right image sequence with disparity and motion of the left image sequence, motion compensation of the enhancement layer is performed without motion estimation. To apply this mathod to MVP codec., the prediction mode of base layer and enhancement layer is restricted, and B picture mode in the base layer is removed. Since the proposed codec. does not perform motion estimation in the enhancement layer encoding and prediction mode of base layer is restricted, it's structure is simple and reduces the encoding time. We compared the SNR of encoded image with three different structured codec., and the experimental results show suggested codec. have comparable result.

• Proceedings of the KSRS Conference
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• v.2
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• pp.695-697
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• 2006

Elshayal $Smart{\copyright}$ software is an almost First Arabian GIS $software{\copyright}$ which completely developed by Arabian developers team and independent of any commercial software package. The software current Features are View and Edit shape files, build new layers, add existing layers, remove layers, swap layers, save layers, set layer data sources, layer properties, zoom in & zoom out, pan, identify, selecting features, invert selection, show data table, data query builder, location query builder, build network, find shortest path, print map, save map image, copy map image to clipboard, save project map, edit move vertex, edit move features, snap vertexes, set vertex XY, move settings, converting coordinate system, applying VB script, copy selected features to another layer, move selected features to another layer, delete selected features, edit data table, modify table structure, edit map features, drawing new features, GPS tracking, 3D view, etc... The software expected Features are: Viewing raster image and image geo-referencing, read other map formats such as DXF Format and Tiger Line Format.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.29-32
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• 2000

This paper proposes a motion segmentation algorithm for layer decomposition of image sequences. The proposed algorithm segments an image into initial regions by using its color and texture and computes a motion model of each initial region. Each pixel assigns one of the motion represented by the models or a motion except them, which segments the image into the motion regions. The proposed algorithm is app]ied image sequences and the segmented motion is shown.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1269-1279
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• 2019

In medical image analysis, segmentation is considered as a vital process since it partitions an image into coherent parts and extracts interesting objects from the image. In this paper, we consider automatic segmentations of OCT retinal images to find six layer boundaries using convolutional neural networks. Segmenting retinal images by layer boundaries is very important in diagnosing and predicting progress of eye diseases including diabetic retinopathy, glaucoma, and AMD (age-related macular degeneration). We applied well-known CNN architecture for general image segmentation, called Segnet, U-net, and CNN-S into this problem. We also proposed a shortest path-based algorithm for finding the layer boundaries from the outputs of Segnet and U-net. We analysed their performance on public OCT image data set. The experimental results show that the Segnet combined with the proposed shortest path-based boundary finding algorithm outperforms other two networks.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.920-928
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• 2016

Recently high dynamic range imaging technique is hot issue in computer graphic area. We present a progressive tone mapping algorithm, which is based on weighted least squares optimization framework. Our approach combines weighted least squares filtering with iCAM06 model. To show more perceptual high dynamic range images in conventional display, we decompose high dynamic range image into base layers and detail layers. The base layers are obtained by using weighted least squares filter. Then, we adopt chromatic adaption function and non-linear compression function to deal with base layers. Only the base layers reduce contrast, and preserving detail. The image quality assessment shows that our tone mapped image is more similar to original high dynamic range image. Moreover, the subjective result shows our algorithm produces more reliable and pleasing image.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.143-146
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• 1998

A new stereoscopic codec. structure using MPEG-2 multiview profile is presented in this paper. In the suggested codec., the left image is coded with motion estimation in the base layerand the right image is coded with disparity estimation in the enhancement layer. Since it is possible to calculate rough motion of the right image sequence with disparity and motion of the left image sequence, motion compensation of the enhancement layer is performed without motion estimation. Since the proposed codec. does not perform motion estimation in the enhancement layer encoding, it is simple and reduces the encoding time. We compared the PSNR of encoded image with three different structured codec., and the experimental results show that suggested codec. has comparable with other codecs.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.255-261
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• 2009

A new polarimetric SAR image classification technique based on the degree of polarization (DoP) and the co-polarized phase-difference (CPD) is presented in this paper. Since the DoP and the CPD of a scattered wave provide information on the randomness of the scattering and the type of scattering mechanisms, at first, the statistics of the DoP and CPD are examined with measured polarimetric SAR image data. Then, a DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification technique is verified using the JPL AirSAR and ALOS PALSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.6
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• pp.185-192
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• 2022

Multimodal medical image fusion (MMIF) fuses two images containing different structural details generated in two different modes into a comprehensive image with saturated information, which can help doctors improve the accuracy of observation and treatment of patients' diseases. Therefore, a method based on double-layer decomposer and fine structure preservation model is proposed. Firstly, a double-layer decomposer is applied to decompose the source images into the energy layers and structure layers, which can preserve details well. Secondly, The structure layer is processed by combining the structure tensor operator (STO) and max-abs. As for the energy layers, a fine structure preservation model is proposed to guide the fusion, further improving the image quality. Finally, the fused image can be achieved by performing an addition operation between the two sub-fused images formed through the fusion rules. Experiments manifest that our method has excellent performance compared with several typical fusion methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6017-6037
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• 2017

In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.