ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing

Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset. The resulting images, named ORGB, have almost the same appearance as the original RGB images while are more illumination-robust for color space conversion. Besides, image processing using ORGB instead of RGB is free from the interference of shadows. Finally, the proposed offset correction method is applied to road detection task, improving the performance both in quantitative and qualitative evaluations.Comment: Project website: https://baidut.github.io/ORGB

Colour Constancy: Cues, Priors and Development

Colour is crucial for detecting, recognising, and interacting with objects. However, the reflected wavelength of light ("colour") varies vastly depending on the illumination. Whilst adults can judge colours as relatively invariant under changing illuminations (colour constancy), much remains unknown, which this thesis aims to resolve. Firstly, previous studies have shown adults can use certain cues to estimate surface colour. However, one proposed cue - specular highlights - has been little researched so this is explored here. Secondly, the existing data on a daylight prior for colour constancy remain inconclusive so we aimed to further investigate this. Finally, no studies have investigated the development of colour constancy during childhood so the third aim is to determine at what age colour constancy becomes adult-like. In the introduction, existing research is discussed, including cues to the illuminant, daylight priors, and the development of perceptual constancies. The second chapter contains three experiments conducted to determine whether adults can use a specular highlight cue and/ or daylight prior to aid colour constancy. Results showed adults can use specular highlights when other cues are weakened. Evidence for a daylight prior was weak. In the third chapter the development of colour constancy during childhood was investigated by developing a novel child-friendly task. Children had higher constancy than adults, and evidence for a daylight prior was mixed. The final experimental chapter used the task developed in Chapter 3 to ask whether children can use specular highlights as a cue for colour constancy. Testing was halted early due to the coronavirus pandemic, yet the data obtained suggest that children are negatively impacted by specular highlights. Finally, in the general discussion, the results of the six experiments are brought together to draw conclusions regarding the use of cues and priors, and the development of colour constancy. Implications and future directions for research are discussed

Real-time Global Illumination Decomposition of Videos

We propose the first approach for the decomposition of a monocular color video into direct and indirect illumination components in real time. We retrieve, in separate layers, the contribution made to the scene appearance by the scene reflectance, the light sources and the reflections from various coherent scene regions to one another. Existing techniques that invert global light transport require image capture under multiplexed controlled lighting, or only enable the decomposition of a single image at slow off-line frame rates. In contrast, our approach works for regular videos and produces temporally coherent decomposition layers at real-time frame rates. At the core of our approach are several sparsity priors that enable the estimation of the per-pixel direct and indirect illumination layers based on a small set of jointly estimated base reflectance colors. The resulting variational decomposition problem uses a new formulation based on sparse and dense sets of non-linear equations that we solve efficiently using a novel alternating data-parallel optimization strategy. We evaluate our approach qualitatively and quantitatively, and show improvements over the state of the art in this field, in both quality and runtime. In addition, we demonstrate various real-time appearance editing applications for videos with consistent illumination