We develop an effective algorithm, based on the filtered backprojection (FBP) approach, for the imaging of vegetation. Under the FBP scheme, the reconstruction amounts at a non-trivial Fourier inversion, since the data are Fourier samples arranged on a non-Cartesian grid. The computational issue is efficiently tackled by Non-Uniform Fast Fourier Transforms (NUFFTs), whose complexity grows asymptotically as that of a standard FFT. Furthermore, significant speed-ups, as compared to fast CPU implementations, are obtained by a parallel versions of the NUFFT algorithm, purposely designed to be run on Graphic Processing Units (GPUs) by using the CUDA language. The performance of the parallel algorithm has been assessed in comparison to a CPU-multicore accelerated, Matlab implementation of the same routine, to other CPU-multicore accelerated implementations based on standard FFT and employing linear, cubic, spline and sinc interpolations and to a different, parallel algorithm exploiting a parallel linear interpolation stage. The proposed approach has resulted the most computationally convenient. Furthermore, an indoor, polarimetric experimental setup is developed, capable to isolate and introduce, one at a time, different non-idealities of a real acquisition, as the sources (wind, rain) of temporal decorrelation. Experimental far-field polarimetric measurements on a thuja plicata (western redcedar) tree point out the performance of the set up algorithm, its robustness against data truncation and temporal decorrelation as well as the possibility of discriminating scatterers with different features within the investigated scene.

In this paper, we propose a new method to measure the 3D object shape without special purpose lighting based upon the Backprojection of Pixel Data.This method need not extract feature points such as edges from images at all and can measure not only the feature points but the whole object surface. It is simply done by project all pixel data back into the object space from each image. Actually, we first assign all pixel data of images into voxels in the object space, and evaluate the variance of assigned data for all voxels. This process is based on the idea that a point on the object surface gives the similar color information or gray level when it is observed from different view points. Then, two kinds of voting are executed as an enhancement process to eliminate the voxels containing the false points. We present experimental results under the circular constraint of camera movement and show the possibility of the proposed method.