• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.387-392
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• 2014

Recently, various research for intelligent video surveillance system have been proposed, but the existing monitoring systems are inefficient because all of situational awareness is judged by the human. In this paper, shadow removal based moving object tracking method is proposed using the chromaticity and entropy image. The background subtraction model, effective in the context awareness environment, has been applied for moving object detection. After detecting the region of moving object, the shadow candidate region has been estimated and removed by RGB based chromaticity and minimum cross entropy images. For the validity of the proposed method, the highway video is used to experiment. Some experiments are conducted so as to verify the proposed method, and as a result, shadow removal and moving object tracking are well performed.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.420-432
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• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2126-2133
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• 2014

We present a scalable object tracking framework, which is capable of removing shadows and tracking the people. The framework consists of background subtraction, fuzzy based shadow removal and boundary tracking algorithm. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects, and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects and shadows are processed differently in order to supply an object-based selective update. Experimental results demonstrate that the proposed method is able to track the object boundaries under significant shadows with noise and background clutter.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.249-256
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• 2015

Shadow is a common physical phenomenon in natural images and may cause problems in computer vision tasks. Therefore, shadow removal is an essential preprocessing process for effective moving object tracking in video image. In this paper, we proposed the method of shadow removal algorithm using chromaticity, brightness distortion and direction of shadow candidate. The proposed method consists of two steps. First, removal process of primary shadow candidate region by using chromaticity, brightness and distortion. The second stage applies the final shadow candidate region to obtain a direction feature of shadow which is estimated by the thinning algorithm after calculating the lowest pixel position of the moving object. To verify the proposed approach, some experiments are conducted to draw a compare between conventional method and that of proposed. Experimental results showed that proposed methodology is simple, but robust and well adaptive to be need to remove a shadow removal operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3166-3179
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• 2013

A pre-processing system for adaptive noise removal is proposed based on the principle of identifying and filtering object regions and background regions. Human perception of images depends on bright, well-focused object regions; these regions can be treated with the best filters, while simpler filters can be applied to other regions to reduce overall computational complexity. In the proposed method, bright region segmentation is performed, followed by segmentation of object and background regions. Noise in dark, background, and object regions is then removed by the median, fast bilateral, and bilateral filters, respectively. Simulations show that the proposed algorithm is much faster than and performs nearly as well as the bilateral filter (which is considered a powerful noise removal algorithm); it reduces computation time by 19.4 % while reducing PSNR by only 1.57 % relative to bilateral filtering. Thus, the proposed algorithm remarkably reduces computation while maintaining accuracy.

• Korean Journal of Computational Design and Engineering
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• v.11 no.6
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• pp.455-463
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• 2006

Hidden line removal(HLR) algorithms can be devised either in the image space or in the object space. This paper describes a hidden line removal algorithm in the object space specifically for the CAD viewer data. The approach is based on the Appel's 'Quantitative Invisibility' algorithm and fundamental concept of 'back face culling'. Input data considered in this algorithm can be distinguished from those considered for HLR algorithm in general. The original QI algorithm can be applied for the polyhedron models. During preprocessing step of our proposed algorithm, the self intersecting surfaces in the view direction are divided along the silhouette curves so that the QI algorithm can be applied. By this way the algorithm can be used for any triangulated freeform surfaces. A major advantage of this algorithm is the applicability to general CAD models and surface-based visualization data.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.168-176
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• 2014

Detecting moving objects from a video sequence is a fundamental and critical task in video surveillance, traffic monitoring and analysis, and human detection and tracking. It is very difficult to detect moving objects in a video sequence degraded by the environmental factor such as fog. In particular, the color of an object become similar to the neighbor and it reduces the saturation, thus making it very difficult to distinguish the object from the background. For such a reason, it is shown that the performance and reliability of object detection and tracking are poor in the foggy weather. In this paper, we propose a novel method to improve the performance of object detection, combining a haze removal algorithm and a local histogram-based object tracking method. For the quantitative evaluation of the proposed system, information retrieval measurements, recall and precision, are used to quantify how well the performance is improved before and after the haze removal. As a result, the visibility of the image is enhanced and the performance of objects detection is improved.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.941-946
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• 2009

As supplying of automatic surveillance or patrol systems based on image processing, the needs on object extraction technology from images increases. The extraction is more difficult when the lighting condition is changed from time to time. There are many approaches to extract objects from images excluding shadow. They have a common problem something like loss of object region according with shadow removal. In this paper a restoration method using color information of objects to complement the problem is presented. The usefulness of the method is verified using images taken from different lighting conditions and selected from well-known DB.