• Smart Media Journal
• /
• v.1 no.3
• /
• pp.48-55
• /
• 2012

In general, background subtraction based methods are used to detect the moving objects in visual tracking applications. In this paper we employed background subtraction based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection and we compare those in terms of detection performance and computational complexity. In the first approach we used single background and in the second approach we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped object. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short time fully occlusion and illumination changes, as well as it can operate in real time.

• Journal of Cerebrovascular and Endovascular Neurosurgery
• /
• v.25 no.3
• /
• pp.306-310
• /
• 2023

Traumatic internal carotid artery injuries can produce direct carotid-cavernous fistulas as well as giant internal carotid artery pseudoaneurysms. Clinical sequelae can include headaches, cranial nerves palsies, proptosis, chemosis and optic neuropathy with visual loss as the most dangerous complication. Herein, we present a case of one of the largest reported internal carotid artery pseudoaneurysms associated with a direct carotid cavernous fistula. We describe the techniques and pitfalls of treatment with parent vessel occlusion.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.6
• /
• pp.323-330
• /
• 2021

Visual object tracking is a challenging area of study in the field of computer vision due to many difficult problems, including a fast variation of target shape, occlusion, and arbitrary ground truth object designation. In this paper, we focus on the reinforced feature of the dynamic search area to get better performance than conventional discriminative model prediction trackers on the condition when the accuracy deteriorates since low feature discrimination. We propose a reinforced input feature method shown like the spotlight effect on the dynamic search area of the target tracking. This method can be used to improve performances for deep learning based discriminative model prediction tracker, also various types of trackers which are used to infer the center of the target based on the visual object tracking. The proposed method shows the improved tracking performance than the baseline trackers, achieving a relative gain of 38% quantitative improvement from 0.433 to 0.601 F-score at the visual object tracking evaluation.

• Journal of the Korea Computer Graphics Society
• /
• v.21 no.4
• /
• pp.11-17
• /
• 2015

As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.434-436
• /
• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.23 no.3
• /
• pp.219-225
• /
• 2007

The ability of a dentist to select and communicate an acceptable shade match to a dental laboratory may be the most important factor in esthetic restorative dentistry. However, shade matching is a very complex situation. In this study, an attempt was made to compare and evaluate the conventional visual assessment and colorimetric analysis in clinical shade matching. 20 patients were selected and their maxillary central incisors shade were measured by Vita classic shade guide, using $Shadepilot^{TM}$ and ShadeEye $NCC^{(R)}$. The results indicate that there is much variation in the shade selection by visual and instrumental methods.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.23 no.2
• /
• pp.131-137
• /
• 2007

The ability of a dentist to select and communicate an acceptable shade match to a dental laboratory may be the most important factor in esthetic restorative dentistry. However, shade matching is a very complex situation. In this study, an attempt was made to compare and evaluate the conventional visual assessment and colorimetric analysis in clinical shade matching. 20 patients were selected and their maxillary central incisors shade were measured by Vita classic shade guide, using $Shadepilot^{TM}$ and ShadeEye $NCC^{(R)}$. The results indicate that there is much variation in the shade selection by visual and instrumental methods.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.6-8
• /
• 2007

In this paper, We propose reasoning system for object recognition and space classification using not only visual features but also contextual information. It is necessary to perceive object and classify space in real environments for mobile robot. especially vision based. Several visual features such as texture, SIFT. color are used for object recognition. Because of sensor uncertainty and object occlusion. there are many difficulties in vision-based perception. To show the validities of our reasoning system. experimental results will be illustrated. where object and space are inferred by bi -directional rules even with partial and uncertain information. And the system is combined with top-down and bottom-up approach.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.1
• /
• pp.141-150
• /
• 2013

This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.761-770
• /
• 2010

In this paper, we propose a new Frame Rate Up-Conversion (FRUC) scheme to increase the frame rate from a lower number into a higher one and enhance the decoded video quality at the decoder. The proposed algorithm utilizes the preliminary frames of forward and backward direction using bilateral prediction. In the process of the preliminary frames, an additional interpolation is performed for the occlusion area because if the calculated value of the block with reference frame if larger than the predetermine thresholdn the block is selected as the occlusion area. In order to interpolate the occlusion area, we perform re-search to obtain the osiomal block considerhe osiomnumber of available ne block consblock. The experimental results show that performance of the proposed algorithm has better PSNR and visual quality than the conventional methods.