Computing reflectance ratios from an image

doi:10.1016/0031-3203(93)90158-S

Pattern Recognition

Volume 26, Issue 10, October 1993, Pages 1529-1542

https://doi.org/10.1016/0031-3203(93)90158-S Get rights and content

Abstract

A photometric invariant called reflectance ratio is presented that can be computed from a single brightness image of a scene. The brightness variation in an image of a surface depends on several factors; the three-dimensional shape of the surface, its reflectance properties, and the illumination conditions. Since neighboring points on a smoothly curved surface have similar surface orientations, their brightness values can be used to compute the ratio of their reflectance coefficients. Based on this observation, an algorithm is developed that estimates a reflectance ratio for each region in the image with respect to its background. The algorithm is computationally efficient as it computes ratios for all image regions in just two raster scans. In the first scan, the image is segmented into regions using a sequential labeling algorithm. During labeling, the reflectance ratio between adjacent pixels is used as a measure of connectivity. In the second scan, a reflectance ratio is computed for each image region as an average of the ratios computed for all points that lie on its boundary. The region reflectance ratio is also a photometric invariant; it represents a physical property of the region and is invariant to the illumination conditions. Several experimental results are included to demonstrate the invariance of reflectance ratios to imaging and illumination parameters. A brief discussion on the application of reflectance ratios to the problems of object recognition and visual inspection is also given.

References (14)

B.K.P. Horn
Determining lightness from an image
Comput. Graphics Image Process
(1974)
P.J. Besl et al.
Three-dimensional object recognition
ACM Computing Surveys
(1985)
R.T. Chin et al.
Model-based recognition in robot vision
ACM Computing Surveys
(1986)
E.H. Land
The retinex
Am. Scientist
(1964)
E.H. Land et al.
Lightness and retinex theory
J. Opt. Soc. Am.
(1971)
A. Blake
Boundary conditions for lightness computation in Mondrian world
G.D. Finlayson
Colour object recognition

There are more references available in the full text version of this article.

Cited by (38)

An optimisation approach to the recovery of reflection parameters from a single hyperspectral image
2013, Computer Vision and Image Understanding
Citation Excerpt :
For instance, Nayar and Bolle [35] have used photometric invariants derived from the BRDF to recognise objects with different reflectance properties. This work builds on that reported in [34], where a background to foreground reflectance ratio is introduced. In a related development, Dror et al. [11] have shown how surfaces may be classified from single images through the use of reflectance properties.
In this paper, we present a method to recover the parameters governing the reflection of light from a surface making use of a single hyperspectral image. To do this, we view the image radiance as a combination of specular and diffuse reflection components and present a cost functional which can be used for purposes of iterative least squares optimisation. This optimisation process is quite general in nature and can be applied to a number of reflectance models widely used in the computer vision and graphics communities. We elaborate on the use of these models in our optimisation process and provide a variant of the Beckmann–Kirchhoff model which incorporates the Fresnel reflection term. We show results on synthetic images and illustrate how the recovered photometric parameters can be employed for skin recognition in real world imagery, where our estimated albedo yields a classification rate of 95.09 ± 4.26% as compared to an alternative, whose classification rate is of 90.94 ± 6.12%. We also show quantitative results on the estimation of the index of refraction, where our method delivers an average per-pixel angular error of 0.15°. This is a considerable improvement with respect to an alternative, which yields an error of 9.9°.
A method of illumination compensation for human face image based on quotient image
2008, Information Sciences
Illumination variation is one of the critical factors affecting face recognition rate. A novel approach for human face illumination compensation is presented in this paper. It constructs the nine-dimension face illumination subspace based on quotient image. In addition, with the aim to improve algorithm efficiency, a half-face illumination image is proposed and the low-dimension training set of the face image under different illumination conditions are obtained by means of PCA and wavelet transform. After processing, two different illumination compensation strategies are given: one is adding light, and the other is removing light. Based on the illumination compensation strategy, we implement the typical illumination sample image synthesis and the standard illumination sample image synthesis on a PCA feature subspace and a wavelet transform subspace, respectively, and the illumination compensation of the gray images and the color images are further realized. Experimental results based on the Yale Face Database B, the Extended Yale Face Database B and the CAS-PEAL Face Database indicate that execution time after compensation is approximately half the time and face recognition rate is improved by 20% compared with that of the original images.
Effective vehicle detection technique for traffic surveillance systems
2006, Journal of Visual Communication and Image Representation
Moving object detection is one of the key technologies for intelligent video monitoring systems. For real-time detection of moving object in the surveillance scene, the general and simple method is based on background image difference. However, it requires the accurate current background image and the approach for automatic background updating along with the illumination variance is difficult to design and implement. This limits its applications. To solve the above problem, a new self-adaptive background approximating and updating algorithm based on optical flow theory is presented for the traffic surveillance scene in this paper. To detect the moving regions of interest in the scene, the difference image between the current frame and the updating background is first obtained by using a color image difference model, and then a self-adaptive thresholding segmentation method for moving object detection based on the Gaussian model is developed and implemented. Moreover, an effective shadow-eliminating algorithm based on contour information and color features is developed. Experimental results demonstrate that the proposed background updating method can update the background exactly and rapidly along with the variance of illumination, the self-adaptive thresholding segmentation method based on the Gaussian model can extract the moving object regions accurately and completely, and the shadow can be eliminated accurately. This is the foundation for further objects recognition and understanding.
Estimating the surface radiance function from single images
2005, Graphical Models
This paper describes a simple method for estimating the surface radiance function from single images of smooth surfaces made of materials whose reflectance function is isotropic and monotonic. The method makes implicit use of the Gauss map between the surface and a unit sphere. We assume that the material brightness is monotonic with respect to the angle between the illuminant direction and the surface normal. Under conditions in which the light source and the viewer directions are identical, we show how a tabular representation of the surface radiance function can be estimated using the cumulative distribution of image gradients. Using this tabular representation of the radiance function, surfaces may be rendered under varying light source direction by rotating the corresponding reflectance map on the Gauss sphere about the specular spike direction. We present a sensitivity study on synthetic and real-world imagery. We also present two applications which make use of the estimated radiance function. The first of these illustrates how the radiance function estimates can be used to render objects when the light and viewer directions are no longer coincident. The second application involves applying corrected Lambertian radiance to rough and shiny surfaces.
Motion detection via change-point detection for cumulative histograms of ratio images
2005, Pattern Recognition Letters
Motion detection is widely used as the key module for moving object extraction from image frames. In most of the motion detection methods, backgrounds are subtracted from captured images. This is called background subtraction. As standard intensity can be expressed as the multiplication of illumination and reflectance, illumination changes will produce a poor difference image from background subtraction and affect the accuracy of motion detection. In this paper, we use ratio images as the basis for motion detection. For thresholding the target images, we propose change-point detection for cumulative histograms to prevent the difficulties of searching peaks and valleys in histograms. Experimental results show that change-point detection of cumulative histograms performs very well for thresholding the target images. In addition, the superiority of motion detection based on ratio images to motion detection based on difference images is also depicted in experimentation.
Background subtraction based on logarithmic intensities
2002, Pattern Recognition Letters
Background subtraction is widely used as the basis for moving object extraction from image sequences. For traditional background subtraction, the standard intensities in interested images are compared to those in the reference image. As standard intensity can be expressed as the multiplication of illumination and reflectance, the logarithmic intensity is therefore the addition of logarithmic illumination and logarithmic reflectance, which are easier to be separated and analyzed than the multiplication of illumination and reflectance. In this paper, background subtraction based on logarithmic intensities is proposed. Experimental results show that background subtraction based on logarithmic intensities is superior to traditional background subtraction in producing difference images with better quality. In addition, the threshold selection is less critical with the proposed background subtraction scheme.

View all citing articles on Scopus

View full text

Computing reflectance ratios from an image

Abstract

Comput. Graphics Image Process

Three-dimensional object recognition

ACM Computing Surveys

Model-based recognition in robot vision

ACM Computing Surveys

The retinex

Am. Scientist

Lightness and retinex theory

J. Opt. Soc. Am.

Boundary conditions for lightness computation in Mondrian world

Colour object recognition