Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios. Publisher’s Note: This paper, originally published on 24 December 2013, was replaced with a revised version on 11 June 2014. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance.

In this paper, we propose a new algorithm to estimate a super-resolution image from a given low-resolution image, by adding high-frequency information that is extracted from natural high-resolution images in the training dataset. The selection of the high-frequency information from the training dataset is accomplished in two steps: a nearest-neighbor search algorithm is used to select the closest images from the training dataset, which can be implemented in the GPU, and a sparse-representation algorithm is used to estimate a weight parameter to combine the high-frequency information of selected images. This simple but very powerful super-resolution algorithm can produce state-of-the-art results. Qualitatively and quantitatively, we demonstrate that the proposed algorithm outperforms existing common practices.

As a part of an ongoing project, in this paper we introduce the first version of a system which has a novel methodology for Cine (as in cinema) MRI based control of a cardiac robot for beating heart surgeries. The system uses the preoperative planning approach that we developed earlier, and integrates it to the intraoperative algorithms for controlling a robot and tracking some specific landmarks of a highly dynamical surgical field. In particular, our late studies presented herein aim to demonstrate the feasibility of integrating appropriate computational tools to achieve the volumetric image guidance for minimally invasive surgeries in the beating heart. We conceive of the system as practicable for in vitro experiments upon the completion of the first physical prototype, which may pave the way for expansion of the approach for other complex surgeries as well.