Description: 本书从图形学最基础的光栅扫描、区域填充、画直线和圆弧等算法讲起，详细介绍了线裁剪和面裁剪、凸区域裁剪和凹区域裁剪的异同，景物空间消隐算法和图像空间消隐算法的差别，具体讲述了二叉空间剖分(BSP)、八叉树等图形学中常用的数据结构。新版本增加了图形用户界面、椭圆、图像压缩和线条反走样算法等，还增加了Liang-Barsky裁剪算法和Nicholl-Lee-Nicholl裁剪算法。新版本大大扩充了可见面光线跟踪算法。在绘制这一章中新增了基于物理的光照明模型，透明效果，阴影生成，纹理映射，以及锥光束、平面光束、笔形光束和随机光线跟踪算法；详细讨论了光辐射度基础，统一颜色空间，彩色图象量化和印刷介质上的颜色重现等新内容。本书列举了90个例子，具体描述了各类算法的执行细节。这对初学者体会算法的基本原理，比较各类算法执行时的细微差别大有禅益。具备大学数学基础和高级程序设计语言知识的人均可自学本书。
-Book from the most basic graphics raster scan, area filling, drawing lines and arcs and so I start algorithm, details of the line cutting and surface cutting, cutting convex and concave regions cut regional differences and similarities, features space blanking algorithm and image space removal method of difference, specifically about the binary space partition (BSP), octree and other data structures used in graphics. The new release adds a graphical user interface, ellipse, image compression and line anti-aliasing algorithm, also increased the Liang-Barsky clipping algorithm and the Nicholl-Lee-Nicholl cutting algorithm. Greatly expanded new version of the ray tracing algorithm can be met. In this chapter, drawing new physics-based lighting models, transparent effects, shadow generation, texture mapping, and the cone beam, flat beam, pencil beam and the random ray tracing algorithm detailed discussion of the optical radiometric base for the harmonized color Kongjian color image quanti Platform: |
Size: 20500480 |
Author:李大山 |
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Description: This paper is concerned with the derivation of a progression
of shadow-free image representations. First we show that
adopting certain assumptions about lights and cameras leads to a
1-d, grey-scale image representation which is illuminant invariant
at each image pixel. We show that as a consequence, images
represented in this form are shadow-free. We then extend this 1-d
representation to an equivalent 2-d, chromaticity representation.
We show that in this 2-d representation, it is possible to re-light
all the image pixels in the same way, effectively deriving a 2-d
image representation which is additionally shadow-free. Finally,
we show how to recover a 3-d, full colour shadow-free image
representation by first (with the help of the 2-d representation)
identifying shadow edges. We then remove shadow edges from
the edge-map of the original image by edge in-painting, and we
propose a method to re-integrate this thresholded edge map, thus
deriving the sought-after 3-d shadow-free image. Platform: |
Size: 6412288 |
Author:dermeche |
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Description: For this project, the shadow removal method used by Finlayson et al. in [1] was implemented.
This report contains an overview of the mathematical background and a detailed
discussion on the experiments performed with the implementation.
This method consists of two independent steps. First, an illumination invariant image
is derived from a color image for which an appropriate color calibration is known. Second,
shadow edges are detected by finding edges in the intensity image that are not in the illumination
invariant image. Gradient information is then erased at those shadow edges, and
a new shadowless color image is formed by solving a Poisson’s equation. Platform: |
Size: 3089408 |
Author:dermeche |
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Description: Shadow detection and removal is an important task when dealing with colour outdoor images. Shadows are
generated by a local and relative absence of light. 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. Most shadow detection and segmentation methods are based on image analysis. However, some
factors will affect the detection result due to the complexity of the circumstances, like water and a low intensity
roof because of the special material as they are easy mistaken as shadows. In this paper we present a hypothesis
test to detect shadows from the images and then energy function concept is used to remove the shadow from the
image. Platform: |
Size: 382976 |
Author:dermeche |
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Description: 室内目标跟踪的项目，先是包括光照补偿和阴影去除的图像处理，再就是稍微修改了下码书模板和运动模板，需要的话可以参考下。
本科毕业设计，基于VisualC++ 6.0和OpenCV 1.0。-ndoor target tracking project, first lumen compensation and shadow removal, image processing, then there are slightly modified under the code book templates and sports templates
Need to refer to the following.
Graduate design, based on Visual C 6.0 and OpenCV 1.0. Platform: |
Size: 2743296 |
Author:零崎影识 |
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Description: 室内目标跟踪的项目，先是包括光照补偿和阴影去除的图像处理，再就是稍微修改了下码书模板和运动模板，内含毕设期间提交的代码、文档以及演示。需要的话可以参考下。
本科毕业设计，基于VisualC++ 6.0和OpenCV 1.0。-Indoor target tracking project, first lumen compensation and shadow removal, image processing, then there are slightly modified under the code book templates and sports templates
Contains a complete set submitted during the code, documentation, and presentation. Need to refer to the following.
Graduate design, based on Visual C 6.0 and OpenCV 1.0. Platform: |
Size: 10082304 |
Author:零崎影识 |
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Description: [IOVS]2011 Shadow Removal and Contrast Enhancement in OCT Images of the Human Optic Nerve Headl的实现matlab代码，医学图像增强-[IOVS] 2011 Shadow Removal and Contrast Enhancement in OCT Images of the Human Optic Nerve Headl realization of matlab code medical image enhancement Platform: |
Size: 1024 |
Author:belle |
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Description: work-piecehu.rar基本功能：图像预处理，去除阴影，之后利用hu矩，SVM分类。 -The basic function of the work-piecehu.rar: image preprocessing, shadow removal, followed by Hu moment, SVM classification. Platform: |
Size: 6128640 |
Author: |
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Description: 这是CVPR2011论文“Single-Image Shadow Detection and Removal using Paired Regions”最新的代码，可以直接在Windows环境下直接运行，阴影检测效果很好。-This code is the latest version for the paper, Single-Image Shadow Detection and Removal using Paired Regions ,in CVPR2011.It can directly work in Windows environment and perform well. Platform: |
Size: 16816128 |
Author:Carrie |
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Description: 数字图像处理中的背景差分，里面带有图片，能够直接运行程序，无需调试-Inside the background difference method in digital image processing, with pictures, can directly run the program, do not need to debug Platform: |
Size: 41984 |
Author:1233 |
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Search - shadow removal image