• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.176-178
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• 2005

In the images of a video camera employed for surveillance, detecting targets by extracting foreground image is of great importance. The foreground regions detected, however, include not only moving targets but also their shadows. This paper presents a novel technique to detect shadow pixels in the foreground image of a video camera. The image characteristics of video cameras employed, a web-cam and a CCD, are first analysed in the HSV color space and a pixel-level shadow detection technique is proposed based on the analysis. Compared with existing techniques where unified criteria are used to all pixels, the proposed technique determines shadow pixels utilizing a fact that the effect of shadowing to each pixel is different depending on its brightness in background image. Such an approach can accommodate local features in an image and hold consistent performance even in changing environment. In experiments targeting pedestrians, the proposed technique showed better results compared with an existing technique.

• Journal of Korea Game Society
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• v.20 no.3
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• pp.65-74
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• 2020

In the shadow map method, if the shadow map is magnified, the shadow has a jagged silhouette. Herein, we propose a soft shadow method that filters reshaped silhouettes analytically. First, the shadow silhouette is reshaped through sub-texel edge detection, which is based on linear or quadratic curve models. Second, an integral shadow filtering algorithm is used to accurately obtain the average shadow intensity from a definite integral estimation. The implementation demonstrates that our solution can effectively eliminate jagged aliasing and efficiently generate soft shadows.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.527-532
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• 2008

This paper proposes a novel approach to shadow detection from a single natural image regardless of orientation and type of light sources. This approach is based on the assumption that shadow brightness changes linearly, and the axiom that a region cast shadow on is darker than that not having shadow under the same environment. Firstly, candidates for shadow are extracted by preprocessing. Then, they are quantized to replace the similar values with a representative value because of the more quantization steps of a pixel brightness, the higher linear independency among the neighboring pixels. Finally, shadows are detected according to linear independency of shadow brightness based on the assumption. The experimental results showed the proposed approach can robustly detect umbra as well as self-shadow and penumbra cast on a single-colored background.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.202-208
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• 2013

In the image processing for VS (Video Surveillance), the detection of moving entities in a monitored scene is an important step. A background subtraction technique has been widely employed to find the moving entities. However, the extracted foreground regions often include not only real entities but also their cast shadows, and this can cause errors in following image processing steps, such as tracking, recognition, and analysis. In this paper, a novel technique is proposed to determine the shadow pixels of moving objects in the foreground image of a VS camera. Compared to existing techniques where the same decision criteria are applied to all moving pixels, the proposed technique determines shadow pixels using local features based on two facts: First, the amount of pixel intensity drop due to a shadow depends on the intensity level of background. Second, the distribution pattern of pixel intensities remains even if a shadow is cast. The proposed method has been tested at various situations with different backgrounds and moving humans in different colors.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.400-407
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• 2019

In this paper, the shadow detection and reconstruction method are proposed using intrinsic image, which does not change the essential characteristics under the influence of various illuminance, and multi-scale gamma correction. The shadow detection was estimated by the pixel change information between a grayscale and an intrinsic image of the color image, and the brightness of the image were adjusted by gamma correction in the shadow restoration process. Multi-scale gamma correction is performed for each channel of a color image due to the fact that the saturation can be changed by nonlinear adjustment to individual pixel values. Multi-scale gamma values are estimated based on the information of the crossed edge between shadows and non-shadowed regions in the color image, as a result, the shadows are reconstructed by correcting different region features with multi-scale gamma values. Experimental results show that the proposed method effectively reconstructs shadows in a single natural image.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.889-894
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• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., 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 background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1314-1321
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• 2005

In video surveillance and monitoring (VSAM), extracting foreground by detecting moving regions is the most fundamental step. The foreground extracted, however, includes not only objects in motion but also their shadows, which may cause errors in following video image processing steps. To remove the shadows, this paper presents a new technique to determine shadow pixels in the foreground image of a VSAM camera system. The proposed technique utilizes a fact that the effect of shadowing to each pixel is different defending on its brightness in a background image when determining shadow pixels unlike existing techniques where unified decision criteria are used to all pixels. Such an approach can easily accommodate local features in an image and hold consistent Performance even in changing environment. In real experiments, the proposed technique showed better results compared with an existing technique.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.157-165
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• 2011

It is challenging to detect foreground objects when background includes an illumination variation, shadow or structural variation due to its motion. Basically pixel-based background models including codebook-based modeling suffer from statistical randomness of each pixel. This paper proposes an algorithm that incorporates Markov random field model into pixel-based background modeling to achieve more accurate foreground detection. Under the assumptions the distance between the pixel on the input imaging and the corresponding background model and the difference between the scene estimates of the spatio-temporally neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameters is proposed. The proposed method alternates between estimating the parameters with the intermediate foreground detection and estimating the foreground detection with the estimated parameters, after computing it with random initial parameters. Extensive experiment is conducted with several videos recorded both indoors and outdoors to compare the proposed method with the standard codebook-based algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.17-21
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• 2011

This paper presents a real- time detection of on-road succeeding vehicles based on low level edge features and a boosted cascade of Haar-like features. At first, the candidate vehicle location in an image is found by low level horizontal edge and symmetry characteristic of vehicle. Then a boosted cascade of the Haar-like features is applied to the initial hypothesized vehicle location to extract the refined vehicle location. The initial hypothesis generation using simple edge features speeds up the whole detection process and the application of a trained cascade on the hypothesized location increases the accuracy of the detection process. Experimental results on real world road scenario with processing speed of up to 27 frames per second for $720{\times}480$ pixel images are presented.