Study of the photodetector characteristics of a camera for color constancy in natural scenes

Illuminant estimation in multispectral imaging

With the advancement in sensor technology, the use of multispectral imaging is gaining wide popularity for computer vision applications. Multispectral imaging is used to achieve better discrimination between the radiance spectra, as compared to the color images. However, it is still sensitive to illumination changes. This study evaluates the potential evolution of illuminant estimation models from color to multispectral imaging. We first present a state of the art on computational color constancy and then extend a set of algorithms to use them in multispectral imaging. We investigate the influence of camera spectral sensitivities and the number of channels. Experiments are performed on simulations over hyperspectral data. The outcomes indicate that extension of computational color constancy algorithms from color to spectral gives promising results and may have the potential to lead towards efficient and stable representation across illuminants. However, this is highly dependent on spectral sensitivities and noise. We believe that the development of illuminant invariant multispectral imaging systems will be a key enabler for further use of this technology.

Organic Photodiodes: The Future of Full Color Detection and Image Sensing

Major growth in the image sensor market is largely as a result of the expansion of digital imaging into cameras, whether stand-alone or integrated within smart cellular phones or automotive vehicles. Applications in biomedicine, education, environmental monitoring, optical communications, pharmaceutics and machine vision are also driving the development of imaging technologies. Organic photodiodes (OPDs) are now being investigated for existing imaging technologies, as their properties make them interesting candidates for these applications. OPDs offer cheaper processing methods, devices that are light, flexible and compatible with large (or small) areas, and the ability to tune the photophysical and optoelectronic properties - both at a material and device level. Although the concept of OPDs has been around for some time, it is only relatively recently that significant progress has been made, with their performance now reaching the point that they are beginning to rival their inorganic counterparts in a number of performance criteria including the linear dynamic range, detectivity, and color selectivity. This review covers the progress made in the OPD field, describing their development as well as the challenges and opportunities.

Chromaticity space for illuminant invariant recognition

In this paper an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with non-uniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colours under the International Commission on Illumination (CIE) standard illuminants and obtained a good performance. It was also tested for colour based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard colour spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant colour based object recognition and skin detection.

Illuminant spectrum estimation at a pixel

In this paper, an algorithm is proposed to estimate the spectral power distribution of a light source at a pixel. The first step of the algorithm is forming a two-dimensional illuminant invariant chromaticity space. In estimating the illuminant spectrum, generalized inverse estimation and Wiener estimation methods were applied. The chromaticity space was divided into small grids and a weight matrix was used to estimate the illuminant spectrum illuminating the pixels that fall within a grid. The algorithm was tested using a different number of sensor responses to determine the optimum number of sensors for accurate colorimetric and spectral reproduction. To investigate the performance of the algorithm realistically, the responses were multiplied with Gaussian noise and then quantized to 10 bits. The algorithm was tested with standard and measured data. Based on the results presented, the algorithm can be used with six sensors to obtain a colorimetrically good estimate of the illuminant spectrum at a pixel.

Extending "color constancy" outside the visible region

In this paper, the results of an investigation of the possibility of extending "color constancy" to obtain illuminant-invariant reflectance features from data in the near-ultraviolet (UV) and near-infrared (IR) wavelength regions are reported. These features are obtained by extending a blackbody-model-based color constancy algorithm proposed by Ratnasingam and Collins [J. Opt. Soc. Am. A27, 286 (2010)] to these additional wavelengths. Ratnasingam and Collins applied the model-based algorithm in the visible region to extract two illuminant-invariant features related to the wavelength-dependent reflectance of a surface from the responses of four sensors. In this paper, this model-based algorithm is extended to extract two illuminant-invariant reflectance features from the responses of sensors that cover the visible and either the near-UV or near-IR wavelength. In this investigation, test reflectance data sets are generated using the goodness-fitness coefficient (GFC). The appropriateness of the GFC for generating the test data sets is demonstrated by comparing the results obtained with these data with those obtained from data sets generated using the CIELab distance. Results based upon the GFC are then presented that suggest that the model-based algorithm can extract useful features from data from the visible and near-IR wavelengths. Finally, results are presented that show that, although the spectrum of daylight in the near UV is very different from a blackbody spectrum, the algorithm can be modified to extract useful features from visible and near-UV wavelengths.

Optimum sensors for color constancy in scenes illuminated by daylight

The apparent color of an object within a scene depends on the spectrum of the light illuminating the object. However, recording an object's color independent of the illuminant spectrum is important in many machine vision applications. In this paper the performance of a blackbody-model-based color constancy algorithm that requires four sensors with different spectral responses is investigated under daylight illumination. In this investigation sensor noise was modeled as gaussian noise, and the responses were quantized using different numbers of bits. A projection-based algorithm whose output is invariant to illuminant is investigated to improve the results that are obtained. The performance of both of these algorithms is then improved by optimizing the spectral sensitivities of the four sensors using freely available CIE standard daylight spectra and a set of lightness-normalized Munsell reflectance data. With the optimized sensors the performance of both algorithms is shown to be comparable to the human visual system. However, results obtained with measured daylight spectra show that the standard daylights may not be sufficiently representative of measured daylight for optimization with the standard daylight to lead to a reliable set of optimum sensor characteristics.