About: Illumination problem is a research topic. Over the lifetime, 93 publications have been published within this topic receiving 5859 citations.
Papers published on a yearly basis
••29 Apr 2010
TL;DR: Since illumination became a separate field in the authors' universities, use of computers in illumination education has increased rapidly and computer programmers are used in illumination technique to design fixtures, to measure and evaluate interior and exterior lighting.
Abstract: Fundamental topic of Illumination consists of production, distribution, economy and measurement of light Besides, behaviors of people towards light are studied Illumination is a major topic in electrical engineering and architecture in our age Any field to be illuminated should be analyzed as a special problem However, solution to the illumination problem can only be found by electrical engineers having a voice in light production and architects working together Illumination education has only started in recent past in our country In the past, in many applications too many or wrong type lighting fixtures were used mistakenly due to ignorance Since illumination became a separate field in our universities, use of computers in illumination education has increased rapidly Computers are used in preparing illumination projects both to measure and to visualize A designer can make virtual and natural lighting measurements using computers In general, computer programmers are used in illumination technique to design fixtures, to measure and evaluate interior and exterior lighting In addition to this, virtual illumination technique should be used in education in order to be able to visualize the lighting projects in three dimensions
••10 Mar 2021
TL;DR: Zhang et al. as mentioned in this paper applied the Expected Patch Log Likelihood (EPLL) algorithm to extract illumination weight and combined it with the Neighboring Radiance Ratio (NRR) to optimize the initial vector of the Gaussian mixture model, which makes full use of the redundant information in images.
Abstract: Illumination is an important factor that impairs face recognition. Many algorithms have been proposed to solve the illumination problem. Most algorithms focus on one image information and only use local illumination change, to improve the effects of removing facial illumination. In this paper, we apply the Expected Patch Log Likelihood (EPLL) algorithm to extract illumination weight and we combine it with the Neighboring Radiance Ratio algorithm (NRR) to optimize the initial vector of the Gaussian mixture model, which makes full use of the redundant information in images. The experimental results on the extended Yale B and CMU PIE face databases show that the proposed algorithm can effectively eliminate the influence of illumination on face images and has a high robustness.
TL;DR: In this article, the authors determined the illumination number of the unit ball of the variation norm, and hence provided a sharp lower bound for the running time of the Lins and Nussbaum algorithm.
Abstract: In recent work with Lins and Nussbaum the first author gave an algorithm that can detect the existence of a positive eigenvector for order-preserving homogeneous maps on the standard positive cone. The main goal of this paper is to determine the minimum number of iterations this algorithm requires. It is known that this number is equal to the illumination number of the unit ball of the variation norm. In this paper we determine its illumination number, and hence provide a sharp lower bound for the running time of the algorithm.
••01 Dec 2013
TL;DR: A detailed survey of state of the art 2D face recognition algorithms using wavelets for feature extraction using comparative experimental results, merits and demerits of various wavelet methods are presented.
Abstract: The illumination variation problem is one of the well-known problems in face recognition in uncontrolled situation. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using wavelets for feature extraction. Existing problems in face recognition are covered and possible solutions are suggested. This review covers the techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination. This paper also discusses the active techniques that aim to obtain images of face modalities invariant to environmental illumination. A comparative experimental results, merits and demerits of various wavelet methods have been presented.
TL;DR: A nonlinear manifold framework for the face pose and the face illumination normalization processing is proposed and the efficient face tracking and recognition results on indoor and outdoor video are derived.
Abstract: Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.Keywords:Face recognition, Face tracking, Manifold, Retinex
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