• Journal of the Institute of Convergence Signal Processing
• /
• v.14 no.1
• /
• pp.33-38
• /
• 2013

In this paper, we propose an algorithm to track multi-small targets in infrared image sequences in case of dissipation or creation of targets by using the background estimation filter, Kahnan filter and mean shift algorithm. We detect target candidates in a still image by subtracting an original image from an background estimation image, and we track multi-targets by using Kahnan filter and target selection. At last, we adjust specific position of targets by using mean shift algorithm In the experiments, we compare the performance of each background estimation filters, and verified that proposed algorithm exhibits better performance compared to classic methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.434-436
• /
• 2022

For 4th industrial revolution and the development of various communication media, unmanned and automation are rapidly progressing in various fields. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. Accordingly, the importance of preprocessing in a system operating based on an image is increasing, and an algorithm for effectively removing noise from an image is attracting attention. In this paper, we propose a filtering algorithm using noise judgment and a segmentation mask in a complex noise environment. The proposed algorithm calculates the final output by switching the segmentation mask suitable for filtering by performing noise judgment on the pixel values of the input image. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared and evaluated with the existing filter algorithm.

• Journal of Korea Multimedia Society
• /
• v.16 no.10
• /
• pp.1135-1146
• /
• 2013

In this paper, we proposed a new algorithm for individual tooth region segmentation on tooth color images. The proposed algorithm used oral cavity model based on structural feature of tooth and new boundary of watershed algorithm. First, the gray scale image is obtained with emphasized tooth regions from the color images and unnecessary regions are removed on tooth images. Next, the image enhancement of tooth images is implemented using the proposed oral cavity model, and the individual tooth regions are segmented by watershed algorithm on the enhanced images. Boundary and seeds necessary to watershed algorithm are applied boundary of binary image using minimum thresholding and region maximum value. In order to evaluate performance of proposed algorithm, we conduct experiment to compare conventional algorithm with proposed algorithm. As a result of experiment, we confirmed that the proposed algorithm is more improved detection ratio than conventional algorithm at molar regions and the tooth region detection performance is improved by preventing overlap detection on oral cavity.

• Journal of radiological science and technology
• /
• v.47 no.1
• /
• pp.39-48
• /
• 2024

In this study, we optimized the FNLM algorithm through a simulation study and applied it to a phantom scanned by low-dose CT to evaluate whether the FNLM algorithm can be used to obtain improved image quality images. We optimized the FNLM algorithm with MASH phantom and FASH phantom, which the algorithm was applied with MATLAB, increasing the smoothing factor from 0.01 to 0.05 with increments of 0.001 and measuring COV, RMSE, and PSNR values of the phantoms. For both phantom, COV and RMSE decreased, and PSNR increased as the smoothing factor increased. Based on the above results, we optimized a smoothing factor value of 0.043 for the FNLM algorithm. Then we applied the optimized FNLM algorithm to low dose lung CT and lung CT under normal conditions. In both images, the COV decreased by 55.33 times and 5.08 times respectively, and we confirmed that the quality of the image of low dose CT applying the optimized FNLM algorithm was 5.08 times better than the image of lung CT under normal conditions. In conclusion, we found that the smoothing factor of 0.043 among the factors of the FNLM algorithm showed the best results and validated the performance by reducing the noise in the low-quality CT images due to low dose with the optimized FNLM algorithm.

• 한국공간정보시스템학회:학술대회논문집
• /
• 2007.06a
• /
• pp.139-144
• /
• 2007

Recording data in hundreds of narrow contiguous spectral intervals, hyperspectral images have provided the opportunity to detect small differences in material composition. But a limitation of a hyperspectral image is the signal to noise ratio (SNR) lower than that of a multispectral image. This paper presents the efficiency of Spectral Similarity Scale (SSS) in change detection of hyperspectral image and the experiment was performed with Hyperion data. SSS is an algorithm that objectively quantifies differences between reflectance spectra in both magnitude and direction dimensions. The thresholds for detecting the change area were determined through Expectation-Maximization (EM) algorithm. The experimental result shows that the SSS algorithm and EM algorithm are efficient enough to be applied to the unsupervised change detection of hyperspectral images.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.26 no.10
• /
• pp.1445-1450
• /
• 2002

In the present study, a new pattern recognition algorithm was proposed to size spray particles using the boundary curvature information. Conceptually, this algorithm has an advantage over the others because it can identify the particle size and shape simultaneously, and also can separate the overlapped particles more effectively. Curvature of a boundary was obtained from the change of the slopes of two neighboring segments at the corresponding part. The algorithm developed in this study was tested by using an artificially prepared image of a group of spherical particles which were either isolated or overlapped. Particle sizes obtained from the measured curvatures agreed well with the true values. By detecting abrupt changes of the curvature along the image boundary, the element particles could be separated out from their overlapped images successfully.

• Journal of Biomedical Engineering Research
• /
• v.10 no.3
• /
• pp.331-342
• /
• 1989

This paper presents a new algorithm that extracts lung region in X-Rays and enhance.j the region. Comparing to prior algorithms that enhance whole X-Ray image, this algorithm leads more effective results. For this algorithm extracts lung region first, and enhances the lung region excluding parameters of other region. For choosing optimal threshold, we compare OTSU's mothod with the proposed method. We obtain lung boundary using contour following algorithm and Rray level searching method in gray level rescaled image. We Process histogram equalization in lung region and obtain enhanced lung image. By using the proposed algorithm, we obtain lung region effectively in chest X-Ray that need in medical image diagnostic system.

• Proceedings of the KSRS Conference
• /
• 2006.03a
• /
• pp.7-14
• /
• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.7 no.5
• /
• pp.247-253
• /
• 2012

This paper presents sensor network based autonomous vehicle system using a proposed image processing algorithm. The proposed image processing algorithm consists of pre-processing and five-stage image processing: coordinate calculation, driving area decision, line segment calculation, steeling decision, and acceleration decision. We evaluate the performance of the proposed algorithm on both straight road and curved road. Experimental results indicate that the proposed algorithm works well for autonomous vehicles. However, control accuracy of the proposed algorithm decreases as speed is increasing.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1998.10a
• /
• pp.211-221
• /
• 1998

A digital image processing technique which is able to get the velocity vector distribution of a surface of the spilled oil in the ocean without contacting the flow itself. This technique is based upon the PIV(Particle Imaging Velocimetry) technique and its system mainly consists of a high sensitive camera, a CCD camera, an image grabber, and a host computer in which an image processing algorithm is adopted for velocity vector acquisition. For the acquisition of the advective velocity vector of floating matters on the ocean, a new multi-frame tracking algorithm is proposed, and for the acquisition of the diffusion velocity vector distribution of the spilt oil onto the water surface, a high sensitive gray-level cross-correlation algorithm is proposed.