Color constancy for landmark detection in outdoor environments

European Workshop on Advanced Mobile Robots (EUROBOT), 2001, Lund (Suecia)This work presents an evaluation of three color constancy techniques applied to a landmark detection system designed for a walking robot, which has to operate in unknown and unstructured outdoor environments. The first technique is the well-known image conversion to a chromaticity space, and the second technique is based on successive lighting intensity and illuminant color normalizations. Based on a differential model of color constancy, we propose the third technique, based on color ratios, which unifies the processes of color constancy and landmark detection. The approach used to detect potential landmarks, which is common to all evaluated systems, is based on visual saliency concepts using multiscale color opponent features to identify salient regions in the images. These regions are selected as landmark candidates, and they are further characterized by their features for identification and recognition.This work was supported by the project 'Navegación autónoma de robots guiados por objetivos visuales' (070-720).Peer Reviewe

Probabilistic Color Constancy

In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions using a graph-based representation of the image. To estimate the weight of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar colors contribute similarly and darker (super-)pixels contribute less. The resulting system has one global optimum solution. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.Comment: 5 pages, 1 figur

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201

Retinex-Based Low Contrast Image Enhancement Using Adaptive Tone-Mapping

Department of Electrical EngineeringIn this paper, we enhance low contrast images using the human visual system based Retinex theory and adaptive tone-mapping. We try to reduce halo artifact and color inconsistency, but also preserve naturalness of images. In the proposed algorithm, we process only the Y channel of the Yuv color space rather than RGB color space to maintain color-constancy. We first apply an adaptive bilateral filtering on the Y channel image to alleviate halo artifact during enhancement. Then we partition the intensity range of probability distribution of filtered Y channel image into low, middle, and high contrast regions according to a cost function. We improve the contrast of filtered Y channel image by using A-law based tone mapping by stretching the low contrast regions and compressing the high contrast regions adaptively. Experimental results show that the proposed algorithm enhances the visibility of input low contrast images efficiently.ope