Hallucinated 3d face model from a single 2d low-resolution face using machine learning

In this paper, the 3D face hallucination system is proposed on both 2D training face images as well as respective 3D training face models with grey-level. The proposed method hallucinates the 3D high-resolution model patch using same position of each image patches of interpolated 2D training image and 3D high-resolution training face model for low-resolution input image. Firstly, the optimal weights of the 2D input low-resolution image position-patches are estimated with the corresponding 2D low-resolution training image patches. The canonical correlation analysis (CCA) is used to learn the mapping between the 2D interpolated face training image and the 3D face model with respect to their weights. Secondly, the corresponding 3D face model patch with weight by matching high score among the 2D interpolated training image patches and 3D training face model is selected. Finally, the 3D high-resolution facial model is formed by integrating the hallucinated 3D patches which are obtained through mapping patches with respective weights. In order to evaluate the performances of the above approaches, we used example based learning methods to obtain the high-resolution output for a low-resolution input. In this approach, we used the available frontal data sets such as FERET, CAS-PERL and CMU to analysis the performance and some parameters are also considered, which may affect the results from the above proposed method

General Regularization for Image Motion Problem

An adaptive control of general regularization is presented in this paper which is based on a posteriori error estimation for a variational optic flow model, which is designed using complementary approach. In this paper the adaptive control and general regularization technique is improved and extended as appeared in previous work. It is shown that with the improvement in the data term, the successful and fast "a posteriori" error control is obtained with a significant improvement in regularization process and determination of dense optic flow field. This method is based on the adaptive finite element method using unstructured grid as the discrete computational domain which allows the locally adaptive choice of optimal general regularization parameters. The given Meshes on unstructured grid and the dramatic improvement in flow field at various adaptive iterations is the core of this presentation and could be an attraction for the image community

Tikhonov regularization using finite element method for a variationaldDenoising problem

A local adaptive control of regularization for a denoising problem is proposed in this article. Our goal is to solve a linear variational denoising problem which is based on the idea of Tikhonov regularization using finite element method with un-structured grid as a domain of computation and also the control of adaptation for local smoothness parameters in the certain regions of image where the solution is less regular and the absolute average error is large. The regularization process is performed by an adaptive algorithm which is intelligent in the sense that the optimal selection of local regularization parameters is automatic. The algorithm decreases the absolute average error and increases the quality of diffusion at each adaptive ste

Algorithm for object detection using raspberry pi

This paper presents about “Detecting the movement of object and capture the still of the object”. It describes how the system is built and implemented in the embedded device using “Raspberry Pi”. These type of system is troubled people for years because it is expensive, large size, hard to install and immovable after installed. To solve this problem, “An Object Detection Monitoring System can be designed using Raspberry Pi, PIR Sensor and Pi camera as hardware modules with help of python language and Linux shell script. The program can be developed using these languages to detect the motion of object and takes picture of it and send to mail under Internet network, same time it will record the video of motion. And It is capable of finding the ratio of detected object with in the camera frame