• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.13 no.3
• /
• pp.225-236
• /
• 2009

Image inpainting is the process of reconstructing lost or deteriorated parts of images using information from surrounding areas. We propose a computationally efficient and fast phase-field method which uses automatic switching parameter, adaptive time step, and automatic stopping of calculation. The algorithm is based on an energy functional. We demonstrate the performance of our new method and compare it with a previous method.

• Proceedings of the Korean Society of Medical Physics Conference
• /
• 2002.09a
• /
• pp.322-323
• /
• 2002

Partial volume averaging effect of PET data influences on the accuracy of quantitative measurements of regional brain metabolism because spatial resolution of PET is limited. The purpose of this study was to evaluate the accuracy of partial volume correction carried out on $^{18}$ F-PET images using Hoffman brain phantom. $^{18}$ F-PET Hoffman phantom images were co-registered to MR slices of the same phantom. All the MR slices of the phantom were then segmented to be binary images. Each of these binary images was convolved in 2 dimensions with the spatial resolution of the PET. The original PET images were then divided by the smoothed binary images in slice-by-slice, voxel-by-voxel basis resulting in larger PET image volume in size. This enlarged partial volume corrected PET image volume was multiplied by original binary image volume to exclude extracortical region. The evaluation of partial volume corrected PET image volume was performed by region of interests (ROI) analysis applying ROIs, which were drawn on cortical regions of the original MR image slices, to corrected and original PET image volume. From the ROI analysis, range of regional mean values increases of partial volume corrected PET images was 4 to 14%, and average increase for all the ROIs was about 10% in this phantom study. Hoffman brain phantom study was useful for the objective evaluation of the partial volume correction method. This MR-based correction method would be applicable to patients in the. quantitative analysis of FDG-PET studies.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.930-932
• /
• 2003

For this study we used a CCD video camera to capture the pavement image information via the computer. During investigation processing, the CCD video camera captured 10${\sim}$30 images per second. If the vehicle velocity is too fast, the collected images will be duplicated and if the velocity is too slow there will be a gapped between images. Therefore, in order to control the efficiency of the image grabber we should add accessory tools such as the Differential Global Positioning System (DGPS) and odometer. Furthermore, Kalman Filtering can also solve these problems. After the CCD video camera captured the pavement images, we used the Least-Squares method to eliminate images of gradation which have non-uniform surfaces due to the illumination at night. The Fuzzy Entropy method calculates images of threshold segments and creates binary images. Finally, the Object Labeling algorithm finds objects that are cracks or noises from the binary image based on volume pixels of the object. We used these algorithms and tested them, also providing some discussion and suggestions.

• Progress in Medical Physics
• /
• v.14 no.3
• /
• pp.203-210
• /
• 2003

PET (positron emission tomography) permits the investigation of physiological and biochemical processes in vivo. The accuracy of quantifying PET data is affected by its finite spatial resolution, which causes partial volume effects. In this study, we developed a method for partial volume correction using Hoffman phantom PET and MR data, and applied various FWHM (full width at half maximum) levels. We also applied this method to PET images of normal controls and tested for the possibility of clinical application. $^{18}$ F-PET Hoffman phantom images were co-registered to MR slices. The gray matter and white matter regions were then segmented into binary images. Each binary image was convolved by 4, 8, 12, 16 mm FWHM levels. These convolved images of gray and white matter were merged corresponding to the same level of FWHM. The original PET images were then divided by the convolved binary images voxel-by-voxel. These corrected PET images were multiplied by binary images. The corrected PET images were evaluated by analyzing regions of interests, which were drawn on the gray and white matter regions of the original MR image slices. We calculated the ratio of white to gray matter. We also applied this method to the PET images of normal controls. On analyzing the corrected PET images of Hoffman phantom, the ratios of the corrected images increased more than that of the uncorrected images. With the normal controls, the ratio of the corrected images increased more than that of the uncorrected images. The ratio increase of the corrected PET images was lower than that of the corrected phantom PET images. In conclusion, the method developed for partial volume correction in PET data may be clinically applied, although further study may be required for optimal correction.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.1
• /
• pp.209-214
• /
• 1997

In this paper, we propose a new preorder tree method for binary images, named the Compact Complementary Quadtree (CCQ). In the proposed method we use type codes for representing nodes in the quadtree instead of using the symbols G, B, and W. From the experimental results, we have confirmed that the CCQ has a higher compression ratio than of the DF-expression. CCQ can be effectively applied to progressive transmission of binary images such as line drawings, geographical maps, and halftones.

• Proceedings of the KIEE Conference
• /
• 1998.11c
• /
• pp.984-986
• /
• 1998

The volume holographic memory system suffers from the crosstalk noise. We study use of error correction coding(ECC) and angular multiplexing for digital holographic memory(DHM) system. The analog image is encoded to binary images by ECC. Binary images are stored using angular multiplexing in DHM. The retrieved binary images are decoded by ECC. The bit error-rate is measured for perspective of the DHM system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3790-3803
• /
• 2018

Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.7
• /
• pp.757-764
• /
• 1990

The purpose of this study is to process binary images of Breadth First Linear Quadtree in hardware. Inthis paper, we designed and verified logic level circuit of ASIC for the encoding part of the binary image that is to convert the binary image into the representation of the Breadth First Linear Quadtree. The logic level circuit is composed of cells in TTL library. The significance of thes study is to implement an algorithm by hardware rather than by software, so that the processing time can be reduced by about 20 times.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.11 no.4
• /
• pp.68-75
• /
• 2008

This paper proposes a new data hiding method for binary images using the weighting value of pixel-value differencing. The binary cover image is partitioned into non-overlapping sub-blocks and find the most suitable position to embed a secret bit for each sub-block. The proposed method calculates the weighted value for a sub-block to pivot a pixel to be changed. This improves the image quality of the stego-image. The experimental results show that the proposed method achieves a good visual quality and high capacity.

• Journal of the Korean Physical Society
• /
• v.73 no.12
• /
• pp.1845-1848
• /
• 2018

We introduce a process for optically reconstructing full-color holographic images recorded by optical scanning holography. A complex RGB-color hologram was recorded and converted into a binary hologram using a direct binary search (DBS) algorithm. The generated binary hologram was then optically reconstructed using a spatial light modulator. The discrepancies between the reconstructed object sizes and colors due to chromatic aberration were corrected by adjusting the reconstruction parameters in the DBS algorithm. To the best of our knowledge, this represents the first optical reconstruction of a full-color hologram recorded by optical scanning holography.