Over a wide range of viewing conditions, our experience of an object’s color varies little. This “color constancy” is a striking achievement of human vision, and raises a much-debated question: What is the proper theoretical framework for understanding color constancy? Several approaches have been considered. The most common has been to estimate the spectral composition of the illuminant, and t...

Recent advances in computer vision on the one hand, and imaging technologies on the other hand, have opened up a number of interesting possibilities for robust 3D scene labeling. This paper presents contributions in several directions to improve the state-of-the-art in RGB-D scene labeling. First, we present a novel combination of depth and color features to recognize different object categorie...

The problem of holistic scene understanding involves many vision tasks such as depth estimation, scene categorization, event categorization, etc. Each of these tasks explores some aspects of the scene but, these tasks are related in that, they represent attributes of the same scene. An intuition is that one task can provide meaningful attributes to aid the learning process of another task. In t...

The great diffusion of digital cameras and the widespread use of the internet have produced a mass of digital images depicting a huge variety of subjects, generally acquired by unknown imaging systems under unknown lighting conditions. This makes color balancing, recovery of the color characteristics of the original scene, increasingly difficult. In this paper we describe a method for detecting...

The color of an object, when part of a complex scene, is determined not only by its spectral reflectance but also by the colors of all other objects in the scene (von Helmholtz, 1886; Ives, 1912; Land, 1959). By taking global color information into account, the visual system is able to maintain constancy of the color appearance of the object, despite large variations in the light incident on th...

Spectral based color image editing systems include methods for generating spectral representations of surfaces and illuminants from input signals of image capture devices (such as digital cameras and scanners), editing spectral representations of surfaces and illuminants in a scene, and transforming spectral representations of surfaces and illuminants to output signals of image devices (such as...

The creation process of interactive non-linear videos affords the definition of scenes which are connected in a scene graph. These might be available in form of raw material (shots) or need to be extracted from existing films. In the latter case, the scenes have to be defined by the author in a time-consuming process. A semi-automated scene extraction helps the user perform this task. Detected ...

In the field of outdoor scene analysis [1], well known procedure to analyze an image is as follows: (1) The image is divided into regions mainly based on color feature. (2) Each region is hypothesized as an object based on statistical knowledge between its characteristics and object labels. (3) The hypotheses are checked by knowledge of three dimensional relation among the objects. In theses st...

We present a method to give color advice for Home Décor using images of room finishes, such as paint, flooring, or textiles, taken with uncalibrated cameras. Due to variations in color rendering across devices, an object imaged with different cameras will have different color values. Color information can still be accurately retrieved from uncalibrated images taken under uncontrolled lighting c...

Having perceptual differences between scene colors is key in many computer vision applications such as image segmentation or visual salient region detection. Nevertheless, most of the times, we only have access to the rendered image colors, without any means to go back to the true scene colors. The main existing approaches propose either to compute a perceptual distance between the rendered ima...