Dichromatic Illumination Estimation via Hough Transforms in 3D

A new illumination-estimation method is proposed based on the dichromatic reflection model combined with Hough transform processing. Other researchers have shown that using the dichromatic reflection model under the assumption of neutral interface reflection, the color of the illuminating light can be estimated by intersecting the dichromatic planes created by two or more differently coloured regions. Our proposed method employs two Hough transforms in sequence in RGB space. The first Hough Transform creates a dichromatic plane histogram representing the number of pixels belonging to dichromatic planes created by differently coloured scene regions. The second Hough Transform creates an illumination axis histogram representing the total number of pixels satisfying the dichromatic model for each posited illumination axis. This method overcomes limitations of previous approaches that include requirements such as: that the number of distinct surfaces be known in advance, that the image be presegmented into regions of uniform colour, and that the image contain distinct specularities. Many of these methods rely on the assumption that there are sufficiently large, connected regions of a single, highly specular material in the scene. Comparing the performance of the proposed approach with previous non-training methods on a set of real images, the proposed method yields better results while requiring no prior knowledge of the image content

Analysis of Clipping Effect in Color Images Captured by CCD Cameras

Camera sensors (CCD) have a limited dynamic range that constrains the brightness of the incident light that can be quantified. In other words, if the ray of incident light is too intense, the sensor saturates and the value quantified is inadequately represented. This color clipping effect is a common problem in computer vision and it can become specially difficult when dealing with specular objects against a low-intensity background. In this paper, we present a method for analyzing such clipping effects of CCD cameras appearing in color images. Using an averaging technique to estimate the color of the illuminant, we define two types of axes in the RGB color cube: the Illumination Axis and the Clipping Axis. Our study concludes the followings: 1) the clipped pixels from a dielectric object form one or two lines, depending on the number of color channels on which the clipping effect takes place; and 2) these lines are parallel to the Clipping Axes. These two properties allow for a framework for a color-based segmentation that works even in the presence of saturated (specular) regions in the image. Moreover, the captured images can now be obtained under a wider variation of illumination conditions

Specularity Removal from Imaging Spectroscopy Data via Entropy Minimisation

In this paper, we present a method to remove specularities from imaging spectroscopy data. We do this by making use of the dichromatic model so as to cast the problem in a linear regression setting. We do this so as to employ the average radiance for each pixel as a means to map the spectra onto a two-dimensional space. This permits the use of an entropy minimisation approach so as to recover the slope of a line described by a linear regressor. We show how this slope can be used to recover the specular coefficient in the dichromatic model and provide experiments on real-world imaging spectroscopy data. We also provide comparison with an alternative and effect a quantitative analysis that shows our method is robust to changes the degree of specularity of the image or the location of the light source in the scene

Image Segmentation With Detection of Highlights and Inter-Reflections Using Color

We present an approach to the construction of a computational model for color image segmentation based on the physical properties of sensors, illumination lights and surface reflectances. Using the established model, we perform color image segmentation and detect small interreflections as well as highlights