Advance Shadow Edge Detection and Removal ( ASEDR ) (original) (raw)
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Shadow Detection and its Removal from Images Using Strong Edge Detection Method
Shadows cause problems in image processing. In this paper, a new methodology for shadow removal based on Strong Edge Detection (SED) method is proposed. The strong shadow edges are recognized by learning the patch based characteristics of shadow edges and then image features are analyzed to guide a Shadow Edge Classifier. Also, spatial patch smoothing is used to enforce uniformity between adjacent patches. The entropy and standard deviation results of both earlier Patch based Shadow Edge Detection Method and proposed SED method are calculated and compared with each other. The results show that the proposed SED method is better than the previous Patch Based Shadow Edge Detection method.
Comparative Analysis of Shadow Detection and Removal Methods on an Image
The shadow detection and removal is an important step in computer vision applications which has been a key challenge in various real life scenarios which are including under surveillance system, indoor outdoor scenes and tracking. Shadow detection and removal method should be implemented in indoor and outdoor with any objects like human, vehicles, and motorcycles moving objects in different times with different environments including weather, different sources of light and lighting conditions. Shadow detection after its removal is considered as the first step to shadow analysis and image processing in the number of applications. In this framework, recent techniques of shadow detection like Intensity based, Segmentation based, Mask Construction, Color based, Edge based methods are studied. Out of the shadow removal methods like Chromacity, Physical, Geometry, Small region texture based, Large Region Texture Based Method, the Otsu's thresholding along with Chromacity and the Geometry method have been discussed with their comparative analysis. Out of those studies the Otsu's Thresholding method is the best method for removal when compared to the other methods.
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
A survey on shadow detection and removal in images
2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), 2017
Shadow Detection and removal is the process of enhance the computer vision applications including image segmentation, object recognition, object tracking etc. Detection and Removal of shadow from the images and videos can reduce the undesirable outcomes in the computer vision applications and algorithms. The prime objective of this survey paper is to analyze the performance of various currently used shadow detection techniques. In this paper we have discussed the techniques for detecting and removing shadow from the still images and video sequences. The scope of discussed shadow detection and removal techniques is limited to different scenarios: (i) Shadow detection for Indoor and Outdoor scenes, (ii) Shadow detection using fixed or moving camera, (iii) Shadow detection of umbra and penumbra shadows etc.
International Journal of Advanced Computer Science and Applications, 2017
Shadow detection is the most important aspect in the field of image processing. It has become essential to develop such algorithms that are capable of processing the images with the maximum efficiency. Therefore, the research has aimed to propose an algorithm that effectively processes the image on the basis of shadow reduction. An algorithm has been proposed, which was based on RGB (red, green, and blue) and HIS (hue, saturation and intensity) model. Steps for Shadow detection have been defined. Median filter and colour saturation have been widely used to process the outcomes. Algorithm has proved efficient for the detection of shadow from the images. It was found efficient when compared with two previously developed algorithms. 87% efficiency has been observed, implementing the proposed algorithm as compared to the algorithms implemented previously by other researchers. The study proved to make a supportive effort in the development of optimized algorithm. It has been suggested that the market requires such practices that can be used to improve the working conditions of the image processing paradigm.
Review of Shadow Detection and Removal Techniques
International Journal of Advance Research and Innovative Ideas in Education, 2018
Shadows are created when light is obstructed by an opaque source. The shadow can be further classified as selfshadow and cast shadow. There are many techniques by which these shadows can be detected and removed. The techniques work at various levels such as pixel, region, boundary, edge, and colour. This paper highlights some of the techniques followed by researchers to detect and remove shadows
Shadow Detection and Removal based on Automatic Threshold and Boundary Analysis
International Journal of Computer Applications, 2019
The objects extraction from their background could be a difficult assignment. Since one threshold or structure threshold certainly fails to resolve doubt , in this paper, we have proposed a brand new technique that automatically observe the edge to exactly discriminate pixels as foreground or background using automatic threshold mechanism. By first distinguishing boundary, its associated curvatures, and edge response, used as benchmark to gauge the possible location of the boundary.Results show that the projected technique systematically performs well in various illumination conditions, as well as indoor, outdoor, moderate, sunny, and rainy cases. By an examination with an empirical evidence in every case, the error rate and the shadow detector index indicate a correct detection, that shows substantial improvement as compared with alternative existing ways.
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.
Robust Shadow Removal Technique For Improving Image Enhancement Based On Segmentation Method
2016
In this paper, we present an efficient and simple approach for shadow detection based on RGB color space in complex urban color remote sensing images for solving problems caused by shadows, as well as give a brief description of the advantages and disadvantages of this method. In the proposed method shadows are detected using intensity information difference and subsequent thresholding based on Otsu’s 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.
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.