• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5963-5980
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• 2017

Image scrambling is an important technology in information hiding, where the Arnold transformation is widely used. Several researchers have proposed the application of $H{\acute{e}}non$ map in square image scrambling, and certain improved technologies require scrambling many times to achieve a good effect without resisting chosen-plaintext attack although it can be directly applied to non-square images. This paper presents a non-square image scrambling algorithm, which can resist chosen-plaintext attack based on a chaotic two-dimensional Sine Logistic modulation map and $H{\acute{e}}non$ map (2D-SLHM). Theoretical analysis and experimental results show that the proposed algorithm has advantages in terms of key space, efficiency, scrambling degree, ability of anti-attack and robustness to noise interference.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.35-39
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• 2007

We applied digital image processing about BitMap file structure in this research and studied about digital communication system. digital record saving devices can express 'High' and 'Low' as all data. Only, digital technique that two expression methods is used into several expenditures. People and communication that are far away by development of digital technology, became smooth. We wished to make communication system taking advantage of digi-tech. This system was made for communication with disabled and normal person specially. To communication method that we are studied such BitMap file handling image communication system. The goal of this contribution is to present the overview of basic algorithms for image processing using BitMap.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• Smart Media Journal
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• v.1 no.3
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• pp.29-35
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• 2012

A depth image-based rendering (DIBR) technique is one of the rendering processes of virtual views with a color image and the corresponding depth map. The most important issue of DIBR is that the virtual view has no information at newly exposed areas, so called dis-occlusion. In this paper, we propose an intermediate view generation algorithm using the Kinect depth camera that utilizes the infrared structured light. After we capture a color image and its corresponding depth map, we pre-process the depth map. The pre-processed depth map is warped to the virtual viewpoint and filtered by median filtering to reduce the truncation error. Then, the color image is back-projected to the virtual viewpoint using the warped depth map. In order to fill out the remaining holes caused by dis-occlusion, we perform a background-based image in-painting operation. Finally, we obtain the synthesized image without any dis-occlusion. From experimental results, we have shown that the proposed algorithm generated very natural images in real-time.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.626-632
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• 2016

In this paper, we propose the defect emphasis in TFT-LCD panel image. The defect emphasis image consist of S(Shape) map and B(Brightness) map. S map based on DoG(difference of gaussian) is made with the mura defect shape characteristic. And B map use defect intensity property that defect intensity is higher than background. The experiments were conducted to evaluate the performance of the proposed defect emphasis method. The results of experiments show the validity of the defect emphasis using the proposed method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.295-301
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• 2004

Satellite captures images periodically and economically over the area wider than aerial photographs, and reconnaissance to unapproachable area. For these advantages, mapping using high resolution satellite image has high potentials of marketability and development. Therefore, utilization of satellite image in mapping and GIS is expected to be growing and research on describable feature, positional accuracy and, possible mapping scale is urgently needed. This research presented that Quick Bird orthoimage could be used to update digital map on a scale of 1:5,000. Quick Bird image was corrected geometrically based on ground control points. DEM was generated using height data of digital topographic map. The orthoimge was produced by digital differential rectification based on DEM which was generated using height data of digital topographic map(scale 1;5,000 and 1;1,000). When the digital topographic map was overlaid with the orthoimage, it was very easy to find changed region or new features builded after the map compiled.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.790-792
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• 2003

The remote sensing image server provides advanced image serving capabilities for geospatial image. Wide seamless image mosaics of Landsat 5 over GMS countries, which exceed a 15 GB or more in size per image, can integrate with other GIS map servers. The approach of two improvement algorithms leads to speed up the response time while preserving the data quality. This system does not only provide images on the web, but also provide GIS layers to WMS client map servers. The advantage of this approach is its efficiency lower cost in terms of cost, time and updating to obtain and utilize remote sensing image.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1266-1272
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• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.3-10
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• 2018

Depth map has been utilized in a varity of fields. Recently research on generating depth map by artificial neural network (ANN) has gained much interest. This paper validates the feasibility of implementing the ready-made depth map generation by convolutional neural network (CNN). First, for a given image, a depth map is generated by the weighted average of a saliency map as well as a motion history image. Then CNN network is trained by test images and depth maps. The objective and subjective experiments are performed on the CNN and showed that the CNN can replace the ready-made depth generation method.