Shadow detection using a physical basis (original) (raw)
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Shadow Detection based on Colour Segmentation and Estimated Illumination
Procedings of the British Machine Vision Conference 2011, 2011
In this paper we show how to improve the detection of shadows in natural scenes using a novel combination of colour and illumination features. Detecting shadows is useful because they provide information about both light sources and the shapes of objects thereby illuminated. Recent shadow detection methods use supervised machine learning techniques with input from colour and texture features extracted directly from the original images (e.g. Lalonde et al. ECCV 2010, Zhu et al. CVPR 2010. It seems sensible to augment these with estimates of scene illumination, as can be obtained with an intrinsic image extraction algorithm. Intrinsic image extraction separates the illumination and reflectance components in a scene, and the resulting illumination maps contain robust intensity change features at shadow boundaries. In this paper, we make two main contributions. First we improve upon existing methods for extracting illumination maps. Second we show how to use these illumination maps together with colour segmentation to extend the Lalonde's approach to shadow detection. Illumination maps are extracted using a steerable filter framework based on global and local correlations in low and high frequency bands respectively. The illumination and colour features so extracted are then input to a decision tree trained to detect shadow edges using AdaBoost. We tested variations of our proposed approach on two public databases of natural scenes. This study showed that our approach improves on that of Lalonde both in terms of sensitivity to shadow edges and rejection of false positives. Following Lalonde we show that our detection results are further improved by imposing an edge continuity constraint via a conditional random field (CRF) model.
Moving Shadow Detection using a Combined Geometric and Color Classification Approach
2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1, 2005
Automatic detection of moving objects is a fundamental problem in computer vision. Motion analysis, object recognition, and video surveillance applications often depend on reliable segmentation of moving objects against a fixed background. Although shadows move in the scene with the objects that cast them, it is often important that only objects in motion, and not their shadows, are detected. For example, false positives of moving shadows that are generated by current approaches to motion segmentation can lead to erroneous object models that will ultimately impact recognition rates.
A Review on Survey and Analysis of Shadow Detection Techniques
Many computer vision applications dealing with video require detecting and tracking of objects. When the objects of interest have a well defined shape, template matching or more sophisticated classifiers can be used to directly segment the objects from the image. These techniques work well for well defined objects such as vehicles but are difficult to implement for no rigid objects such as human bodies. Shadows cause serious problems while segmenting and extracting objects, due to the misclassification of shadow points as foreground / object. Shadows can cause object merging, object shape distortion and even object losses (due to the shadow cast over another object(s)). Although the rapid development of computer vision essentially requiring shadow detection and extraction methodologies, still this domain is in infant stage. Diverse information that characterizes shadows is exploited and in many cases such information is combined or used in a different way. This makes very difficult ...
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition, 2012
This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking. The survey covers methods published during the last decade, and places them in a feature-based taxonomy comprised of four categories: chromacity, physical, geometry and textures. A selection of prominent methods across the categories is compared in terms of quantitative performance measures (shadow detection and discrimination rates, colour desaturation) as well as qualitative observations. Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods. The evaluation indicates that all shadow detection approaches make different contributions and all have individual strength and weaknesses. Out of the selected methods, the geometry-based technique has strict assumptions and is not generalisable to various environments, but it is a straightforward choice when the objects of interest are easy to model and their shadows have different orientation. The chromacity based method is the fastest to implement and run, but it is sensitive to noise and less effective in low saturated scenes. The physical method improves upon the accuracy of the chromacity method by adapting to local shadow models, but fails when the spectral properties of the objects are similar to that of the background. The small-region texture based method is especially robust for pixels whose neighbourhood is textured, but may take longer to implement and is the most computationally expensive. The large-region texture based method produces the most accurate results, but has a significant computational load due to its multiple processing steps.
A Review on various widely used shadow detection methods to identify a shadow from images
In this paper, we address the different shadow detection methods and algorithms for both still and moving images as well as give a brief description of the advantages and disadvantages of each method. Shadow detection and removal is an important step in visual surveillance and monitoring systems. Shadow points are often misclassified as object points causing errors on localization, segmentation and classification of objects. Many algorithms and methods have been developed for different environmental conditions to detect shadow from the images. We will review some widely used methods how to detect shadows and extract it avoiding loss of texture information.
A Review on various widely used shadow detection methods to identify a shadow from image
2016
In this paper, we address the different shadow detection methods and algorithms for both still and moving images as well as give a brief description of the advantages and disadvantages of each method. Shadow detection and removal is an important step in visual surveillance and monitoring systems. Shadow points are often misclassified as object points causing errors on localization, segmentation and classification of objects. Many algorithms and methods have been developed for different environmental conditions to detect shadow from the images. We will review some widely used methods how to detect shadows and extract it avoiding loss of texture information.
A Novel Approach for Shadow Detection and Removal from Image
Image processing has been one region of studies that draws the interest of extensive form of researchers. Surveillance structures are in big demand specially, for their packages in public areas, consisting of airports, stations, subways, front to buildings and mass events. Shadow occurs while objects consist of light from light source. Shadows offer wealthy information about the item shapes as well as light orientations. Shadow in picture reduces the reliability of many computer imaginative and prescient algorithms. Shadow regularly degrades the visual exceptional of an image. Shadow removal in an image is pre-processing step for computer imaginative and prescient algorithm and image enhancement. Shadow detection and removal in numerous actual lifestyles situations consisting of surveillance device and laptop vision machine remained a hard project. Shadow in visitors surveillance system might also misclassify the actual item, lowering the gadget overall performance
Shadow detection for moving objects based on texture analysis
Pattern Recognition, 2007
This paper presents a new approach for shadow detection of moving objects in visual surveillance environment, improving localization, segmentation, tracking and classification of detected objects. An automatic segmentation procedure based on adaptive background difference is performed in order to detect potential shadow points so that, for all moving pixels, the approach evaluates the compatibility of photometric properties with shadow characteristics. The shadow detection approach is improved by evaluating the similarity between little textured patches, since shadow regions present same textural characteristics in each frame and in the corresponding adaptive background model. In this work we suggest a new approach to describe textural information in terms of redundant systems of functions. The algorithm is designed to be unaffected by scene type, background type or light conditions. Experimental results validate the algorithm's performance on a benchmark suite of indoor and outdoor video sequences.
Applied Sciences, 2019
The detection and removal of moving object shadows is a challenging issue. In this article, we propose a new approach for accurately removing shadows on modern buildings in the presence of a moving object in the scene. Our approach is capable of achieving good performance when addressing multiple shadow problems, by reducing background surface similarity and ghost artifacts. First, a combined contrast enhancement technique is applied to the input frame sequences to produce high-quality output images for indoor surroundings with an artificial light source. After obtaining suitable enhanced images, segmentation and noise removal filtering are applied to create a foreground mask of the possible candidate moving object shadow regions. Subsequently, geometry and color information are utilized to remove detected shadow pixels that incorrectly include the foreground mask. Here, experiments show that our method correctly detects and removes shadowed pixels in object tracking tasks, such as ...
Detecting and removing shadows
Computer Graphics and Imaging, 2004
This paper describes a method for the detection and removal of shadows in RGB images. The shadows are with hard borders. The proposed method begins with a segmentation of the color image. It is then decided if a segment is a shadow by examination of its neighboring segments. We use the method introduced in Finlayson et. al.