Beating in Membrane Potential Mediated by Cortical γ-Oscillations for Reading Out Synchronous Neuronal Activities Over Distant Cortical Areas

I investigate whether cortical -oscillations are relevant to reading out synchronous neuronal firings that are distributed over the brain. A cortical neural network model is proposed and simulated. The model consists of two sensory networks (SI and SII) and one coincidence-sensitive network (CS). -oscillatory inputs and external stimuli are applied to interneurons and projection neurons of the sensory networks, respectively. The SI and SII networks project to the CS network in a divergent/convergent manner. Here I show that beating in membrane potential of CS neurons mediates the detection of synchronous neuronal firings between the SI and SII networks. A slight increase (a few Hz) in difference between frequencies of the -oscillations in the SI and SII networks generates the beating in the CS network. The synchronous neuronal firings are detected at each peak of the beating, regardless of difference in signal transmission time between SI-to-CS and SII-to-CS pathways.     

Robust face recognition using 2D and 3D data: Pose and illumination compensation

The paper addresses the problem of face recognition under varying pose and illumination. Robustness to appearance variations is achieved not only by using a combination of a 2D color and a 3D image of the face, but mainly by using face geometry information to cope with pose and illumination variations that inhibit the performance of 2D face recognition. A face normalization approach is proposed, which unlike state-of-the-art techniques is computationally efficient and does not require an extended training set. Experimental results on a large data set show that template-based face recognition performance is significantly benefited from the application of the proposed normalization algorithms prior to classification.     

Estimation of a fluorescent lamp spectral distribution for color image in machine vision

We present a technique to quickly estimate the Illumination Spectral Distribution (ISD) in an image illuminated by a fluorescent lamp. It is assumed that the object colors are a set of colors for which spectral reflectances are available (in our experiments we use spectral measurements of 12 colors checker chart), the sensitivities of the camera sensors are known and the camera response is linear. Thus, the ISD can be approximated by a finite linear combinations of a small number of basis functions.     

Classification of Illumination Methods for Mixed Reality

A mixed reality (MR) represents an environment composed both by real and virtual objects. MR applications are used more and more, for instance in surgery, architecture, cultural heritage, entertainment, etc. For some of these applications it is important to merge the real and virtual elements using consistent illumination. This paper proposes a classification of illumination methods for MR applications that aim at generating a merged environment in which illumination and shadows are consistent. Three different illumination methods can be identified: common illumination, relighting and methods based on inverse illumination. In this paper a classification of the illumination methods for MR is given based on their input requirements: the amount of geometry and radiance known of the real environment. This led us to define four categories of methods that vary depending on the type of geometric model used for representing the real scene, and the sdifferent radiance information available for each point of the real scene. Various methods are described within their category. The classification points out that in general the quality of the illumination interactions increases with the amount of input information available. On the other hand, the accessibility of the method decreases since its pre‐processing time increases to gather the extra information. Recent developed techniques managed to compensate unknown data with clever techniques using an iterative algorithm, hardware illumination or recent progress in stereovision. Finally, a review of illumination techniques for MR is given with a discussion on important properties such as the possibility of interactivity or the amount of complexity in the simulated illumination.     

一种基于单目多幅图像的三维曲面重构方法

根据在不同照明情况下获得的多幅图像,提出一种三维曲面重构方法。先运用解方程组、复化积分计算表面高度值,再利用变分和有限差分思想对所得拟合表面进行进一步的迭代和修正,从而获得最佳再建表面。通过对选取曲面的算例分析,表明该算法具有一定的准确性和适用性。     

Face recognition under arbitrary illumination using illuminated exemplars

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.     

Boosted manifold principal angles for image set-based recognition

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.     

A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques.     

A note on optimal floodlight illumination of stages

We consider the problem of illuminating a straight line segment with floodlights. The possible locations of the floodlights are given in advance and we want the total size of the floodlights to be minimum. We show that this problem admits a polynomial time algorithm even if we require that no two floodlights are placed at the same location.     

织物组织结构与其光照模型的计算机模拟

为了从组织结构上模拟织物的真实外观效果,建立了一种提高CAD系统模拟织物立体感的织物组织模型,建立起以凸透镜形为截面的织物几何结构的数学模型和相应的光照模型,并以斜纹组织织物为实例,得出了其模拟后的立体感强的效果图,证明该方法能够较好地模拟织物组织结构.