User-Assisted Image Shadow Removal

This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piece-wise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods

Shadow Detection and Removal in Single-Image Using Paired Regions

A shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. The shadows are sometimes helpful for providing useful information about objects, and sometimes it degrade the quality of images or it may affect the information provide by them. Thus for the correct image interpretation it is important to detect shadow and restore the information. However, shadow causes problems in computer vision applications, such as segmentation, object detection and object counting. That’s why shadow detection and removal is a pre-processing task in many computer vision applications. So we propose a simple method to detect and remove shadows from a single image. The proposed method begins by selecting shadow image and by pre-processing method we focus only on shadow part. In image classification we distinguish between shadow and non shadow pixels. So that we able to label shadow and non shadow regions of the image. Once shadow is detected that detection results are later refined by image matting, and the shadow- free image is recovered by removing shadow region by non shadow region. Examination of a number of examples indicates that this method yields a significant improvement over previous methods

Shadow Detection and Removal using Artificial Neural Network

Shadow detection and removal is an important task when dealing with colour images. Shadows are generated by a local and relative absence of light or a shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a preprocessing task in computer vision. This thesis work proposes a simple method to detect and remove shadow from a single RGB image using artificial neural network. A shadow detection method is selected based on the phenomena of back propagation algorithm. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. The shadow removal is done by multiplying the shadow region by a constant

Interactive shadow editing from single images

We present a system for interactive shadow editing from single images which includes the manipulations of shape, distribution, sharpness and darkness of shadows according to the features of existing shadows. We first obtain a shadow-free image, shadow boundary and its registered sparse shadow scales using an existing shadow removal method. The modifiable features of the shadow are synthesised from the sparse shadow scales. According to the user-specified shadow-shape and its attributes, our system generates a new shadow matte and composites it into the original image, while also allowing editing of existing shadows. We share our executable for open comparison in community

Identification of Shadowed Areas to Improve Ragweed Leaf Segmentation

As part of a project targeting geometrical structure analysis and identification of ragweed leaves, sample images were created. Even though images were taken under near optimal conditions, the samples still contain noise of cast shadow. The proposed method improves chromaticity based primary shape segmentation efficiency by identification and re-classification of the shadowed areas. The primary classification of each point is done generally based on thresholding the Hue channel of Hue/Saturation/Value color space. In this work, the primary classification is enhanced by thresholding an intra-class normalized weight computed from the class specific Value channel. The corrective step is the removal of areas marked as shadow from the object class. The idea is based on the assumption that the image contains a single, flat leaf in front of a homogeneous background, but there are no color and illumination restrictions. Thus, parameters of the imaging system and the light sources have influence on homogeneity of image parts; however vague shadows differ only in intensity, and hard shadows can only be dropped on the background

Physically-Based Editing of Indoor Scene Lighting from a Single Image

We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that requires modeling complex light transport, and disentangling HDR lighting from material and geometry with only a partial LDR observation of the scene. We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions. We use physically-based indoor light representations that allow for intuitive editing, and infer both visible and invisible light sources. Our neural rendering framework combines physically-based direct illumination and shadow rendering with deep networks to approximate global illumination. It can capture challenging lighting effects, such as soft shadows, directional lighting, specular materials, and interreflections. Previous single image inverse rendering methods usually entangle scene lighting and geometry and only support applications like object insertion. Instead, by combining parametric 3D lighting estimation with neural scene rendering, we demonstrate the first automatic method to achieve full scene relighting, including light source insertion, removal, and replacement, from a single image. All source code and data will be publicly released

IMPLEMENTASI DAN ANALISIS SOFT SHADING REMOVAL DALAM LMS CONE SPACE

ABSTRAKSI: Kondisi illuminasi seringkali membaurkan atau membingungkan berbagai macam algoritma dalam computer vision. Dalam hal ini, shadow pada sebuah image dapat menyebabkan beberapa algoritma yang bertujuan untuk melakukan deteksi tepi seringkali mengalami kegagalan. Selain itu, shadow juga menggagalkan beberapa algoritma dalam computer vision seperti algoritma segmentasi, tracking maupun pengenalan objek. Untuk megatasi hal-hal tersebut, maka diperlukan sebuah algoritma yang dapat menghilangkan shadow dari sebuah image. Salah satu cara yang dapat digunakan untuk menghilangkan shadow adalah dengan memanfaatkan unsur intrinsic image, yaitu reflectance dan shading dengan cara mengadopsikan system pengelihatan primate yaitu dalam L, M, S (Long-wavelenght sensitivity, Medium-wavelenght sensitivity, Short-wavelenght sensitivity) cone space. Dalam Soft shading removal ini, mula-mula image akan diubah ke dalam L M S image dimana tiap pixel-nya mengandung suatu nilai ratio yang menyerupai cone pegelihatan manusia. Dari nilai L,M, S ini akan dilakukan transformasi ke dalam warna Chromatic dan luminance yang akan digunakan untuk mengidentifikasi reflectance suatu image. Hasil akhir dari proses ini adalah sebuah reflectance image yang telah mengalami penghilangan small contour yang berarti pula menghilangkan soft shading dan shadow yang terdapat dalam image. Dari proses ini juga akan diketahui bagaimana pengaruh proses konversi RGB image ke dalam LMS image terhadap hasil akhir reflectance image. Dari hasil pengujian yang dilakukan, proses ini dapat mengurangi shadow yang bersifat self-shadow akan tetapi kurang handal dalam menangani cast shadow.Kata Kunci : reflectance, shading, shadow, LMS cone space.ABSTRACT: Illumination conditions confound many computer visions algorithms. In particular, shadows in an image can cause the edge detection algorithm to fail. It also cause the failure of computer visions algorithms such as segmentation, tracking, or recognition algorithms. One possible sollution to the confouding problems of shadow are to derive mage which are shadow free. One of the way to remove the shadow is using the intrinsic of an image, reflectance and Shading which are adobted primate visual system L, M, S (Long-wavelenght sensitivity, Medium-wavelenght sensitivity, Short-wavelenght sensitivity) cone space. The first step in Soft shading removal is corverting the RGB image into LMS image which are every single pixel on its image contains relative capture ratios of the three human cone types. The LMS will then transformed into chromatic and luminance image which are will be use to identify the reflectance of an image. The final result is a reflectance image with the small contour had already eliminated and it means that there are no soft shading and shadowless image. From this process will be figured out the influence of RGB to LMS convertion mechanism in deriving the final result. Based on the test result, this process successfully reduce the self-shadow, but not robust enough to handle cast shadow. Keyword: reflectance, shading, shadow, LMS cone spac

A Mobile Robot Localization using External Surveillance Cameras at Indoor

AbstractLocalization is a technique that is needed for the service robot to drive at indoors, and it has been studied in various ways. Most localization techniques let the robot measure environmental information to gain location information, but those require high costs as it use many equipment, and also complicate the robot development. But if an external device could calculate the location of the robot and transmit it to the robot, it will reduce the extra cost for the internal equipment needed to recognize the location, and it will also simplify the robot development. Therefore this study suggests an effective way to control the robot by using the location information of the robot included in a map made by visual information from the surveillance cameras installed at indoors. The object in a single image is difficult to tell its size because of the shadow components and occlusion. Therefore, combination of shadow removal technique using HSV image from indoors and images from different perspective using homography to create two- dimensional map with accurate object information is suggested. In the experiment, the effectiveness of the suggested method is shown by analyzing the movement result of the robot which applied the location information from the two-dimensional map that is based on the multi cameras, which its accuracy is measured in advance