• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.49-56
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• 2020

Detecting shadows in images and restoring or removing them was a very challenging task in computer vision. Traditional researches used color information, edges, and thresholds to detect shadows, but there were errors such as not considering the penumbra area of shadow or even detecting a black area that is not a shadow. Deep learning has been successful in various fields of computer vision, and research on applying deep learning has started in the field of shadow detection and removal. However, it was very difficult and time-consuming to collect data for network learning, and there were many limited conditions for shooting. In particular, it was more difficult to obtain shadow data from buildings and satellite images, which hindered the progress of the research. In this paper, we propose a method for generating shadow data from buildings and satellites using Unity3D. In the virtual Unity space, 3D objects existing in the real world were placed, and shadows were generated using lights effects to shoot. Through this, it is possible to get all three types of images (shadow-free, shadow image, shadow mask) necessary for shadow detection and removal when training deep learning networks. The method proposed in this paper contributes to helping the progress of the research by providing big data in the field of building or satellite shadow detection and removal research, which is difficult for learning deep learning networks due to the absence of data. And this can be a suboptimal method. We believe that we have contributed in that we can apply virtual data to test deep learning networks before applying real data.

• 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.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.395-402
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• 2005

Object extraction is needed to track objects in color traffic image sequence. To extract objects, we use background differencing method based on MOG(Mixture of Gaussians). In extracted objects, shadows may be included. Due to shadows, we may not find exact location of objects and sometimes we find adjacent objects are glued together. Many methods have been proposed to remove shadows. Conventional methods usually assume that color and texture information are preserved under the shadow. Thus these methods do not work well if these assumptions do not hold. In this paper, we propose a new robust shadow removal method which works well in those situations. First we extract shadow pixel candidates by analysing color information and compute the ratio of shadow pixel candidates over the total number of Pixels. W the ratio is reasonable, we remove shadow candidate Pixels and if not, we use data in history array containing Previous removal records. We applied the method to real color traffic image sequences and obtained good results.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.225-227
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• 1995

An object detection algorithm using a modified IDM(Image Differential Method) is proposed for detecting an object in a level crossing area. The conventional object detection method using LASER light has the deadzone that it cannot detect small objects, while the object detection method using image data in a level crossing area can detect such small objects. But the image data in a level crossing area can be changeable easily because the data is outdoor and sensitive to such surrounding environments as the change of the sun beam, the shadow of cars, and so on. So we resolve these problems by adding the normalization and the process for shadow of the image data in a level crossing area to the basic IDM(Image Differential Method).

• Journal of Ocean Engineering and Technology
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• v.26 no.3
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• pp.61-65
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• 2012

Air-water flow measurements are of importance for the coastal and ocean engineering fields. Although kinematic investigations of the multi-phase flows have been conducted for long time, velocity measurements still are concerned with many researchers and engineers in coastal and ocean areas. In the present study, an imaging technique using shadowgraphy and fiber optic probe for velocity measurements of air bubbles is introduced. The shadow graphy image technique is modified from the typical image velocimetry methods, and optical fibers are used for the well-known intrusive coupled phase-detection probe system. Since the imaging technique is a non-intrusive optical method from the air, it is usually applied for 2D flows. On the other hand, the double fiber optic probes touch flows regardless of flow patterns. The results of the flow measurements by both methods are compared and discussed. The methods are also applied to the measurements of overtopping flows by a breaking wave over the structure fixed on the free surface.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.261-266
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• 1993

This paper describes a 3-dimensional profile measurement method which was intended especially for sheet metal products such as shadow masks. The method is based upon machine vision in which the height variation of the surface is measured by software auto-forus image processing techniques. The method is found suitable for flexible surfaces with interrupt.

• Journal of Astronomy and Space Sciences
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• v.40 no.4
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• pp.173-197
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• 2023

ShadowCam is a high-sensitivity, high-resolution imager provided by NASA for the Danuri (KPLO) lunar mission. ShadowCam calibration shows that it is well suited for its purpose, to image permanently shadowed regions (PSRs) that occur near the lunar poles. It is 205 times as sensitive as the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC). The signal to noise ratio (SNR) is greater than 100 over a large part of the dynamic range, and the top of the dynamic range is high enough to accommodate most brighter PSR pixels. The optical performance is good enough to take full advantage of the 1.7 meter/pixel image scale, and calibrated images have uniform response. We describe some instrument artifacts that are amenable to future corrections, making it possible to improve performance further. Stray light control is very challenging for this mission. In many cases, ShadowCam can image shadowed areas with directly illuminated terrain in or near the field of view (FOV). We include thorough qualitative descriptions of circumstances under which lunar brightness levels far higher than the top of the dynamic range cause detector or stray light artifacts and the size and extent of the artifact signal under those circumstances.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.89-95
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• 2002

A shadow mask inside the Braun tube of a TV is sustained by springs attached to the glass panel, its vibration cause the picture image to discolor, which is called the microphonic phenomenon. It is found that it results from resonance when the natural frequency of the shadow mask coincides with that of built-in speaker sound. This paper describes experimental and analytical investigations by using FEM on the vibration problem of the shadow mask assembly. The simulation scheme may be efficiently used to develop a new design for a large-screen flat TV.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.298-301
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• 2007

Computational speed plays key role in background subtraction and shadow detection, because those are only preprocessing steps of a moving object segmentation, tracking and activity recognition. A color intensity variation based approach fastly detect a moving object and extract shadow in a image sequences. The moving object is subtracted from background using meanmax, meanmin thresholds and shadow is detected by decrease limit and correspondence thresholds. The proposed approach relies on the ability to represent shadow cast impact by offline experiment dataset on sub grouped RGB color space.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.121-132
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• 2016

This study proposes an approach for simulating high spatial resolution satellite images acquired under arbitrary sun-sensor geometry using existing images and 3D (three-dimensional) data. First, satellite images, having significant differences in spectral regions compared with those in the simulated image were transformed to the same spectral regions as those in simulated image by using the UPDM (Universal Pattern Decomposition Method). Simultaneously, shadows cast by buildings or high features under the new sun position were modeled. Then, pixels that changed from shadow into non-shadow areas and vice versa were simulated on the basis of existing images. Finally, buildings that were viewed under the new sensor position were modeled on the basis of open library-based 3D reconstruction program. An experiment was conducted to simulate WV-3 (WorldView-3) images acquired under two different sun-sensor geometries based on a Pleiades 1A image, an additional WV-3 image, a Landsat image, and 3D building models. The results show that the shapes of the buildings were modeled effectively, although some problems were noted in the simulation of pixels changing from shadows cast by buildings into non-shadow. Additionally, the mean reflectance of the simulated image was quite similar to that of actual images in vegetation and water areas. However, significant gaps between the mean reflectance of simulated and actual images in soil and road areas were noted, which could be attributed to differences in the moisture content.