• Journal of Korean Ophthalmic Optics Society
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• v.15 no.1
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• pp.61-66
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• 2010

Purpose: This study was performed to determine vertical fixation disparity by Fixation Disparity Card. The purpose of this study was to investigate distribution of the curve types obtained with Wesson card. Methods: Fixation disparity curves were measured on 52 subjects with the Wesson fixation disparity card. A fixation disparity curve was an x, y coordinate plot of the angular amount of fixation disparity as a function of the power of prisms through which the patient views. The fixation disparity curve variables that were used to aid in the diagnosis and management of binocular vision disorders included the x-intercept, y-intercept, curve slope and curve type. Results: Vertical fixation disparity curves by Wesson Fixation Disparity Card were very various. Conclusions: Vertiical fixation disparity curves could be used to aid in the diagnosis and prescription.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.1
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• pp.53-59
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• 2007

A commonly used device to determine fixation disparity curves is the Wesson Fixation Disparity Card. A fixation disparity curve is an x, y coordinate plot of the angular amount of fixation disparity as a function of the power of prisms through which the patient views. The fixation disparity curve variables that are used to aid in the diagnosis and management of binocular vision disorders include the x-intercept, y-intercept, curve slope and curve type. Fixation disparity curves were measured on 102 subjects with the Wesson fixation disparity card. The purpose of this study is to investigate distribution of the curve types obtained with Wesson card. Fixation Disparity by Wesson Fixation Disparity Card were that in case of type I 63.0%, in case of type II 0.0%, in case of type III 25.0% and in case of type IV 12.0%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.99-102
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• 2009

In this paper a hierarchical stereo matching algorithm based on feature extraction is proposed. The boundary (edge) as feature point in an image is first obtained by segmenting an image into red, green, blue and white regions. With the obtained boundary information, disparities are extracted by matching window on the image boundary, and the initial disparity map is generated when assigned the same disparity to neighbor pixels. The final disparity map is created with the initial disparity. The regions with the same initial disparity are classified into the regions with the same color and we search the disparity again in each region with the same color by changing block size and search range. The experiment results are evaluated on the Middlebury data set and it show that the proposed algorithm performed better than a phase based algorithm in the sense that only about 14% of the disparities for the entire image are inaccurate in the final disparity map. Furthermore, it was verified that the boundary of each region with the same disparity was clearly distinguished.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.279-287
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• 2009

In this paper, a hierarchical stereo matching with color information is proposed. To generate an initial disparity map, feature based stereo matching is carried out and to generate a final disparity map, hierarchical stereo matching is carried out. The boundary (edge) region is obtained by segmenting a given image into R, G, B and White components. From the obtained boundary, disparity is extracted. The initial disparity map is generated when the extracted disparity is spread to the surrounding regions by evaluating autocorrelation from each color region. The initial disparity map is used as an initial value for generating the final disparity map. The final disparity map is generated from each color region by changing the size of a block and the search range. 4 test images that are provided by Middlebury stereo vision are used to evaluate the performance of the proposed algorithm objectively. The experiment results show better performance compared to the Graph-cuts and Dynamic Programming methods. In the final disparity map, about 11% of the disparities for the entire image were inaccurate. It was verified that the boundary for the non-contiguous point was clear in the disparity map.

• Journal of Korean Ophthalmic Optics Society
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• v.19 no.4
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• pp.539-550
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• 2014

Purpose: This research was designed to figure out if there are changes based on types of vergence fixation disparity curve after optical correction and vision training to for people with steep slope of forced vergence fixation disparity curve and symptoms. Methods: For 30 subjects out of 91 subjects, who have steep slopes of fixation disparity curve examined with Wesson Fixation Disparity Card and symptoms about steep slope of fixation disparity curve, proper vision training was offered for 5 weeks. Results: After Vision training for 30 people with the symptoms of type I, II, III, IV for 5 weeks, slope of fixation disparity curve was significantly changed (p=0.013), and the results of binocular vision test and subjective symptoms were also significantly improved. Conclusions: Vision training can not only affect the change of fixation disparity curve, but also relieve the symptoms. Fixation disparity curve has lots of visual function information of the subject and can be suggested as a guideline for prescribing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.93-96
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• 2015

The aim of the estimation of disparity map is to find the corresponding pixels from similar two or more images. However, it is a difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric properties such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose an approaching of disparity map estimation that is reliable in controlling various radiometric variations close to the real environment. This method is motivated by following constancy. Even though each other has different radiometric property in stereo images, intensity of pixels of object have general constancy in specific block. Experimental results show that the proposed method has better performances compared to the comparison group under different radiometric conditions between stereo images. Consequentially, the proposed method is able to estimate the disparity map in stable under various radiometric variations.

• 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.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.304-309
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• 1999

In this paper, we presents a new disparity map refinement algorithm using statistical characteristics of disparity map and edge information. The proposed algorithm generate a refined disparity map using disparity maps which are obtained from area and feature-based Stereo Matching by selecting a disparity value of edge point based on the statistics of both disparity maps. Experimental results on aerial stereo image show the better results than conventional fusion algorithms in the disparity error. This algorithm can be applied to the reconstruction of building image from the high resolution remote sensing data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.7
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• pp.37-42
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• 2008

In this paper, a real-time multiview video coding system using fast disparity estimation is proposed. In the multiview encoder, adaptive disparity-motion estimation (DME) for an effective 3-dimensional (3D) processing are proposed. That is, by adaptively predicting the mutual correlation between stereo images in the key-frame using the proposed algorithm, the bandwidth of stereo input images can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and adaptive disparity vectors. Also, in multiview decoder, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (DSA) for real-time multiview video processing is proposed. The proposed IVR can reduce a processing time of disparity estimation by selecting adaptively disparity search range. Accordingly, the proposed multiview video coding system is able to increase the efficiency of the coding rate and improve the resolution.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.131-143
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• 1998

This paper presents a high performance disparity extraction algorithm that generate a dense and accurate disparity map using low-resolution disparity histogram. Disparity distribution of background and object areas can besegmented from low-resolution disparity histogram. These information can be used to reduce the search area and search range of the high-resolution image resulting reliable disparity information in high speed. The computationally efficient matching pixel count(MPC) similarity measure technique is useed extensively toremove the redundancies inherent in the area-based matching method, and also results robust matching at the boundary region. Resulting maches are further improved using iterative support algorithm and post processing. We have obtained good results on randomdot stereogram and real images obtained in our carmera system.