The Dichromatic Reflection Model - Future Research Directions and Applications (original) (raw)

Bi-illuminant dichromatic reflection model for image manipulation

2006

This paper presents a new model for understanding the appearance of objects that exhibit both body and surface reflection under realistic illumination. Specifically, the model represents the appearance of surfaces that interact with a dominant illuminant and a non-negligible ambient illuminant that may have different spectral power distributions. Real illumination environments usually have an ambient illuminant, and the current dynamic range of consumer cameras is sufficient to capture significant information in shadows. The bi-illuminant dichromatic reflection model explains numerous empirical findings in the literature and has implications for commonly used chromaticity spaces that claim to be illumination invariant but are not in many natural situations. One outcome of the model is the first 2-D chromaticity space for an RGB image that is robust to illumination change given dominant and ambient illuminants with different spectral power distributions.

Describing Reflectances for Color Segmentation Robust to Shadows, Highlights, and Textures

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modelled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram-space. The method is based on a multilocal creaseness analysis of the histogram, which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior-knowledge from image statistics, and incorporate spatial coherence by using multi-scale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.

An illumination and highlights invariant colour model for image matching

Imaging Science Journal, The, 2009

Colour can provide critical information for a variety of computer vision tasks such as image matching, object recognition and image retrieval. However, for it to be useful in practice, the colour model used to represent the intrinsic properties of the imaged objects must be insensitive of imaging conditions such as lighting geometry, illumination colour and highlights. In this paper, we present a colour model for image matching and object recognition that is invariant for illumination and highlights. The colour model is defined as the ratios of the colour differences between neighbouring pixels for each colour component. Based on the dichromatic reflection colour model, it is shown that the proposed colour model is invariant to lighting geometry, illumination colour, highlights and diffuse lighting. Experimental results show robust image matching using the proposed colour model on objects that are illuminated under different illumination colours and lighting geometry. The proposed colour model can be used as a prepossessing step for applications where limited or no constraints on the imaging process can be imposed.

A physical approach to color image understanding

International Journal of Computer Vision, 1990

In this paper, we present an approach to color image understanding that can be used to segment and analyze surfaces with color variations due to highlights and shading. The work is based on a theory-the Dichromatic Reflection Model-which describes the color of the reflected light as a mixture of light from surface reflection (highlights) and body reflection (object color). In the past, we have shown how the dichromatic theory can be used to separate a color image into two intrinsic reflection images: an image of just the highlights, and the original image with the highlights removed. At that time, the algorithm could only be applied to hand-segmented images. This paper shows how the same reflection model can be used to include color image segmentation into the image analysis. The result is a color image understanding system, capable of generating physical descriptions of the reflection processes occurring in the scene. Such descriptions include the intrinsic reflection images, an image segmentation, and symbolic information about the object and highlight colors. This line of research can lead to physicsbased image understanding methods that are both more reliable and more useful than traditional methods.

Separation of diffuse and specular reflection in color images

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001

The presence of specular reflections in images can lead many traditional computer vision algorithms to produce erroneous results. To address this problem, we propose a method based on the neutral interface reflection model for separating the diffuse and specular reflection components in color images. From two photometric images without calibrated lighting, the illuminant chromaticity is estimated, and the RGB intensities of the two reflection components are computed for each pixel using a linear model of surface reflectance. Unlike most previous methods, the presented technique does not assume any dependencies among pixels, such as regionally-uniform surface reflectance.

Reflectance Measurement System for Skin Color Modeling in Chromaticity Color Space

2009 16th International Conference on Systems, Signals and Image Processing, 2009

Experimental skin reflectance measurement setup is presented to acquire skin color signals under different illuminant conditions directly by an image sensor. To obtain stimulus of varying illuminant conditions an adjustable RGB light source is used. Skin color modeling is an important step for efficient skin color image segmentation, which plays an important role when defining regions of interest in image sequence for a successful hand tracking and gesture recognition within spatially defined vision based interaction. To improve detection accuracy of pixel based skin color segmentation a parametric skin color model can be used, which is able to adapt different illuminant conditions. Therefore skin reflectance under different illuminants plays an important role to obtain such a model. Experimental results of hand skin reflectance measurements within chromaticity space are shown.

Color Invariant Representation and Applications

2017

Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the invariant representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the representation for effective edge detection is...

Learning photometric invariance from diversified color model ensembles

2009

Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles.

Physics-based modelling of human skin colour under mixed illuminants

Robotics and Autonomous Systems, 2001

Skin colour is an often used feature in human face and motion tracking. It has the advantages of being orientation and size invariant and it is fast to process. The major disadvantage is that it becomes unreliable if the illumination changes. In this paper, skin colour is modelled based on a reflectance model of the skin, the parameters of the camera and light sources. In particular, the location of the skin colour area in the chromaticity plane is modelled for different and mixed light sources. The model is empirically validated. It has applications in adaptive segmentation of skin colour and in the estimation of the current illumination in camera images containing skin colour.

Body color sets: A compact and reliable representation of images

Journal of Visual Communication and Image Representation, 2011

This paper proposes a novel definition of color sets, called body color sets and lines. The proposed technique refers to the dichromatic reflectance model, which states that the colors of a uniform lambertian object are roughly located around straight vectors going from the black to the body color, in relation to physical properties of the object. Ends of body vectors are robustly detected, from the clearest to the darkest through a multi-level histogram analysis, a key step of the algorithm. Finally, instead of defining the topographic map along the one and only luminance direction, body lines are designed along each body vector. The resulting topographic map is more compact and requires less executing times and resources than in previous works [gouiffes08]. Experimental results show that this approach provides a better trade-off between compactness and quality of a topographic map. Furthermore, it provides increased stability under temperature changes of the illuminant.► A novel definition of a color topographic map. ► Use of the dichromatic reflectance model to define a new color representation for lambertian model. ► Detection of the body vectors directly in the RGB space. ► The color images are designed along each body vector. ► Results prove a good trade-off between compactness and quality of the topographic map.