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Dorai Rangaswamy, M. A.
- Analysis of 3D Face Reconstruction
Authors
1 Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Missions University, Rajiv Gandhi Salai,(OMR),Paiyanoor-603104, Kancheepuram District, Tamilnadu, IN
2 Department of Computer Science and Engineering , Aarupadai Veedu Institute of Technology, Vinayaka Missions University, Rajiv Gandhi Salai,(OMR),Paiyanoor-603104, Kancheepuram District, Tamilnadu, IN
Source
Digital Image Processing, Vol 6, No 7 (2014), Pagination: 324-328Abstract
3D shape reconstruction from 2D images is an inverse problem, and is therefore mathematically ill-posed. One solution to 3D shape reconstruction problem is to use a model based approach. This paper presents an analysis by synthesis method for solving 3D face reconstruction problems using anatomical landmarks and intensity from 2D frontal face images.
To improve the quality of 3D shape reconstruction we incorporate a number of steps in analysis by synthesis framework. Firstly, we approach the 3D model construction problem by using rigid and non rigid surface registration. Secondly, we simplify the shape estimation by using multidimensional amoeba optimization to optimize shape parameters while mapping texture directly using 3D-2D alignment. Thirdly, we evaluate the quality of the 3D shape reconstruction in the context of 3D shape error as well as by visual analysis.
Keywords
3D Face Database, Statistical Shape Modeling.- Image Change Detection for Differently Exposed Image Pairs
Authors
1 Sathyabama University, Chennai, IN
2 Department of CSE, Aarupadai Institute of Technology, Chennai, IN
3 Department of IT, Kumaraguru Institute of Technology, Coimbatore, IN
Source
Digital Image Processing, Vol 2, No 12 (2010), Pagination:Abstract
This paper proposes an analog Hopfield neural network (HNN) for automatic image change detection problem between the images taken at two different exposure times. This optimization relaxation approach differs from other techniques in that it provides the strength of the change rather than assigning binary labels (changed/unchanged) to each pixel. By subtracting both images pixel by pixel, a difference image is obtained. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed or unchanged. An energy function is derived, so that the network converges to stable state. The main drawback of existing binary labeling approaches is that pixels are labeled according to the information supplied by its neighbors, where its self information is ignored. The main contribution of the analog Hopfield’s model is that it allows a tradeoff between the influence of a pixel’s neighborhood and its own criterion. This is mapped under the energy function to be minimized. Also a comparison between analog and discrete HNN shows similar Percentage of Correct Classification (PCC) and Yule values. However, the analog counterpart describes the degree of change by embedding both Spatial-Contextual Information and Self-Data Information.
- A Novel Mutual Authentication Scheme Using Non-Expansion Visual Cryptography in Reversible Style
Authors
1 Department of Information Technology, Meenakshi College of Engineering, Chennai - 078, IN
2 Department of Electronics & Communication, College of Engineering, Guindy, Chennai – 025, IN
3 Department of Computer Science, AVIT, Vinayaka Mission University, Chennai, IN