Illumination problem

About: Illumination problem is a research topic. Over the lifetime, 93 publications have been published within this topic receiving 5859 citations.

Directional gradients integration image for illumination insensitive face representation

Abstract: The illumination problem is one of the main bottlenecks in a practical face recognition system. Illumination preprocessing is an effective way to handle lighting variations for robust face recognition. In this paper, we present a novel illumination insensitive image, namely directional gradients integration image (DGII), for illumination insensitive face recognition. Unlike the existing model-based methods, the DGII is generated directly from the decomposed gradient components of a logarithmic image, without involving any training procedure. Based on the Lambertian reflectance model, we first calculate the horizontal and vertical gradients in the logarithmic domain to eliminate the illumination component. Secondly, to utilize the gradient orientation information, the two gradients are further decomposed into four components along four directions. Then, the four directional gradients are integrated to reconstruct an illumination insensitive image using anisotropic diffusion. Finally, the reconstructed image is fused with the gradient magnitude image through weighted summing. For performance evaluation, we simply use principal component analysis for feature extraction, Euclidean distance as similarity measure and nearest-neighbor classifier for face recognition. Experiments on the Yale B, the extended Yale B and the CMU PIE (The Carnegie Mellon University pose, illumination and expression database) face databases show that the proposed method provides better results than some state-of-the-art methods, showing its effectiveness for illumination normalization.

An efficient all-frequency environment rendering method for mixed reality

Abstract: Consistent illumination is an important research target in the augmented reality system. In this paper, an efficient illumination method is implemented to solve the consistent illumination problem for mixed reality application. We design and implement an all-frequency environment rendering methods based on wavelet transform, and proposed an accelerated rendering method on GPU. The environment rendering method consists of two steps, one is precomputation and the other real-time rendering. An improved shadow algorithm based on shadow map is propose, which can solve least area where light source view frustum should surround according to the relationship of view frustum in eye space and terrain. In the end, we develop a mixed reality system for aircraft design based on the research results of all the work above which realize the illumination and shadow effects on the virtual aircraft. It can be used to assess how the weather impact on the operating.

Unilluminable rooms, billiards with hidden sets, and Bunimovich mushrooms

Abstract: The illumination problem is a popular topic in recreational mathematics: In a mirrored room, is every region illuminable from every point in the region? So-called \enquote{unilluminable rooms} are related to \enquote{trapped sets} in inverse scattering, and to billiards with divided phase space in dynamical systems. In each case, a billiard with a semi-ellipse has always been put forward as the standard counterexample: namely the Penrose room, the Livshits billiard, and the Bunimovich mushroom respectively. In this paper, we construct a large class of planar billiard obstacles, not necessarily featuring ellipses, that have dark regions, hidden sets, or a divided phase space. The main result is that for any convex set $\mathcal{H}$, we can construct a convex, everywhere differentiable billiard table $K$ (at any distance from $\mathcal{H}$) such that trajectories leaving $\mathcal{H}$ always return to $\mathcal{H}$ after one reflection. This billiard generalises the Bunimovich mushroom. As corollaries, we give more general answers to the illumination problem and the trapped set problem. We use recent results from nonsmooth analysis and convex function theory, to ensure that the result applies to all convex sets.

Usinig Optical Flow as an Additional Constraint for Solving the Correspondence Problem in Binocular Stereopsis

Abstract: A stereo matching algorithm for 3D structure reconstruction relies on the correlation of image features in a pair of images. Usually, two calibrated cameras are used to capture scenes, and the left and right images are rectified to reduce the search space from 2D image region to 1D line. Then, the disparity is defined by the horizontal difference between the corresponding points on the search line. To solve the correspondence problem, various image features such as image intensity, edge, color, infrared light, pixel motion, etc. are employed either separately or integrated together. Among these image features, pixel motion or optical flow has been rarely used as one of the matching features (A. Scheuing & H. Niemann, 1986, G. Sudhir et al, 1995, A. M. Waxman & J. H. Duncan, 1986). In this chapter, we focus on the use of optical flow as an additional constraint in solving the correspondence problem. The stereo matching algorithm for 3D structure reconstruction can be divided into two groups based on the matching primitives: intensity based matching (also referred to as area based matching) and feature based matching. The intensity based matching method searches the best matching points using only intensity values of pixels. The method can further be divided into two groups depending on the smoothness constraints in minimization of matching cost function: local (window-based) method and global method. More details on the intensity based method can be found in a survey by Scharstein (D. Scharstein & R. Szeliski. 2002, M. Z. Brown et al, 2003). The intensity based matching method produces a dense disparity map without any additional post-processing, but usually needs an exhaustive search. Since the intensity based method uses the intensity value at each pixel directly, this method may suffer from the varying illumination problem. The feature based matching method searches the best matching points using some special symbolic feature points, such as line, contour, corner, etc. Since this method calculates disparity values only on the pixels corresponding to the feature point, this method does not require an exhaustive search. Also, symbolic features are less sensitive to illumination changes than pixel intensity, so the calculated disparity is more reliable than that of the intensity based matching method in the case of varying illumination. However, this method O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m