A multiscale based approach for automatic shadow detection and removal in natural images (original) (raw)

Effective shadow removal via multi-scale image decomposition

The Visual Computer, 2019

Shadow removal is a fundamental and challenging problem in image processing field. Current approaches can only process shadows with simple scenes. For complex texture and illumination, the performance is less impressive. In this paper, we propose a novel shadow removal algorithm based on multi-scale image decomposition, which can recover the illumination for complex shadows with inconsistent illumination and different surface materials. Independent of shadow detection, our algorithm only requires a rough boundary distinguishing shadow regions from non-shadow regions. It first performs a multi-scale decomposition for the input image based on an illumination-sensitive smoothing process and then removes shadows in the basic layer using a local-to-global optimization strategy, which fuses all local shadow-free results in a global manner. Finally, we recover the texture details for the shadowfree basic layer and obtain the final shadow-free image. We validate the performance of the proposed method under various lighting and texture conditions and show consistent illumination between shadow and surrounding regions in the shadow removal results.

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.

A shadow detection and removal algorithm for 2-D images

International Conference on Acoustics, Speech, and Signal Processing

We present an algorithm that detects and removes background shadows from images where the pattern set occupies the upper most intensity range of the image and the image is background dominant outside the pattern set. The algorithm will remove background shadows and preserve any remaining texture left behind by the shadow function. A mathematical model of the histogram modification function of the shadow removal algorithm has been developed. An analysis of the sequential nature of the algorithm is included, along with simulated results to verify the mathematical model developed and to show the effectiveness of the algorithm in the removal of background pattern shadows.

Shadow Removal via Shadow Image Decomposition

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019

We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects on the images. We train and test our framework on the most challenging shadow removal dataset (ISTD). Compared to the state-ofthe-art method, our model achieves a 40% error reduction in terms of root mean square error (RMSE) for the shadow area, reducing RMSE from 13.3 to 7.9. Moreover, we create an augmented ISTD dataset based on an image decomposition system by modifying the shadow parameters to generate new synthetic shadow images. Training our model on this new augmented ISTD dataset further lowers the RMSE on the shadow area to 7.4.

Shadow detection and removal from images using machine learning and morphological operations

The Journal of Engineering, 2019

In this study, the authors present a system for shadow detection and removal from images using machine learning technique. A machine learning algorithm ESRT (enhanced streaming random tree) model is proposed. The image is converted to HSV and 26 parameters are taken as image measurements. A dataset in Attribute Relation File Format is created for shadow and non-shadow images. The algorithm is trained using the training dataset and tested using the test dataset. Segmentation is performed. The similar threshold homogeneity pixel is grouped. Colour chromaticity is used to remove cast shadow. Morphological processing is performed to remove the shadow from the image. The algorithm shows better detection rate and accuracy compared with Bayesian classifiers available in WEKA. 2 Related work Sun and co-authors [1] propose an object-oriented method to detect change based on random forest. It uses training sample features are combined to represent the features. The random forest classifier is used for providing training change classifiers. The experimental results suggest high accuracy and F1 scores. Zhang et al. [2] propose a method of object oriented shadow detection and removal

Advance Shadow Edge Detection and Removal ( ASEDR )

2017

Shadow causes problem during the identification of objects from images in various computer vision applications, so it is a very crucial task to remove shadow from images that’s why in this paper a new technique for shadow removal based on Advance Shadow Edge Detection and Removal (ASEDR) method is proposed. After Shadow removal some image parameters (Entropy, Standard Deviation, Peak Signal to Noise Ratio) outcome of both previous Patch based Shadow Edge Detection Method and projected ASEDR method are calculated and compared with each other. The consequences demonstrate that the projected ASEDR method is superior to the previous Patch Based Shadow Edge Detection method. Patch Based Shadow edge detection method was performed on the original image but our ASEDR methodology used the grayscale form of the original image for the purpose of shadow recognition and shadow elimination. In purposed technique the value of entropy is smaller and standard deviation is larger than earlier method ...

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

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.

Shadow removal from a single image

… Design and Applications, 2006. ISDA'06. …, 2006

Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, it is always hard to provide a robust system to eliminate shadows in static images. This paper aimed to give a comprehensive method to remove both vague and hard shadows from a single image. First, classification is applied to the derivatives of the input image to separate the vague shadows. Then, color invariant is exploited to distinguish the hard shadow edges from the material edges. Next, we derive the illumination image via solving the standard Poisson equation. Finally, we got the shadow-free reflectance image. Experimental results showed that our method can robustly remove both vague and hard shadows appearing in the real scene images.

Shadow Detection and Removal in Real Images: A Survey

2006

Shadow detection and removal in real scene images is always a challenging but yet intriguing problem. In contrast with the rapidly expanding and continuous interests on this area, the authors are unaware of any comprehensive surveys on this topic. This paper aimed to give a comprehensive and critical survey of current shadow detection and removal methods. Algorithms are categorized into there sets by their different functions and assumptions about the scenes. A discussion of reasonable evaluation is given at the end of this survey.