Fast and accurate boundary variation method for multilayered diffraction optics

A boundary variation method for the forward modeling of multilayered diffraction optics is presented. The approach permits fast and high-order accurate modeling of periodic transmission optics consisting of an arbitrary number of materials and interfaces of general shape subject to plane-wave illumination or, by solving a sequence of problems, illumination by beams. The key elements of the algorithm are discussed, as are details of an efficient implementation. Numerous comparisons with exact solutions and highly accurate direct solutions confirm the accuracy, the versatility, and the efficiency of the proposed method. (C) 2004 Optical Society of America

An Integral geometry based method for fast form-factor computation

Monte Carlo techniques have been widely used in rendering algorithms for local integration. For example, to compute the contribution of a patch to the luminance of another. In the present paper we propose an algorithm based on Integral geometry where Monte Carlo is applied globally. We give some results of the implementation to validate the proposition and we study the error of the technique, as well as its complexity.Postprint (published version

High performance, LED powered, waveguide based total internal reflection microscopy.

Total internal reflection fluorescence (TIRF) microscopy is a rapidly expanding optical technique with excellent surface sensitivity and limited background fluorescence. Commercially available TIRF systems are either objective based that employ expensive special high numerical aperture (NA) objectives or prism based that restrict integrating other modalities of investigation for structure-function analysis. Both techniques result in uneven illumination of the field of view and require training and experience in optics. Here we describe a novel, inexpensive, LED powered, waveguide based TIRF system that could be used as an add-on module to any standard fluorescence microscope even with low NA objectives. This system requires no alignment, illuminates the entire field evenly, and allows switching between epifluorescence/TIRF/bright field modes without adjustments or objective replacements. The simple design allows integration with other imaging systems, including atomic force microscopy (AFM), for probing complex biological systems at their native nanoscale regimes

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments

One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a Deep Neural Network (DNN) with the purpose of obtaining enhanced representations of the sequences for VO. Then, we demonstrate how the insertion of Long Short Term Memory (LSTM) allows us to obtain temporally consistent sequences, as the estimation depends on previous states. However, the use of very deep networks does not allow the insertion into a real-time VO framework; therefore, we also propose a Convolutional Neural Network (CNN) of reduced size capable of performing faster. Finally, we validate the enhanced representations by evaluating the sequences produced by the two architectures in several state-of-art VO algorithms, such as ORB-SLAM and DSO