We present a retrieval method for 3D CAD assemblies consisted of multiple components. The proposed method distinguishes not only shapes of 3D CAD assemblies but also layouts of their components. Similarity between two assemblies is computed from feature quantities of the components constituting the assemblies. In order to make the similarity robust to translation and rotation of an assembly in 3D space, we use the 3D Radon transform and the spherical harmonic transform. We show that this method has better retrieval precision and efficiency than targets for comparison by experimental evaluation.

For Doppler parameter estimation of forward-looking SAR, the third-order Doppler parameter can not be neglected. In this paper, the azimuth signal of the transmitter fixed bistatic forward-looking SAR is modeled as a cubic polynomial phase signal (CPPS) and multiple time-overlapped CPPSs, and the modified cubic phase function is presented to estimate the third-order Doppler parameter. By combining the cubic phase function (CPF) with Radon transform, the method can give an accurate estimation of the third-order Doppler parameter. Simulations validate the effectiveness of the algorithm.

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 approach to rotation invariant texture analysis. This method uses the Radon transform with some considerations in direction estimation of textural images. Furthermore, it utilizes the information obtained from the number of peaks in the variance array of the Radon transform as a realty feature. The textural features are then generated after rotation of texture along principle direction. Also, to eliminating the introduced error due to rotation of texture, a simple technique is presented. Experimental results on a set of images from the Brodatz album show a good performance achieved by the proposed method in comparison with some recent texture analysis methods.

A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.

In this paper, we address the problem of the rotation-invariant texture analysis. For this purpose, we first present a modified version of the discrete Radon transform whose performance, including accuracy and processing time, is significantly better than the conventional transform in direction estimation and categorization of textural images. We then utilize this transform with a rotated version of Gabor filters to propose a new scheme for texture classification. Experimental results on a set of images from the Brodatz album indicate that the proposed scheme outperforms previous works.

Based on Radon transform, a novel method for registering a periodic (self-referencing) watermark is presented. Although the periodic watermark is widely used as a countermeasure for affine transformation, there is no known efficient method to register it. Experimental results show that the proposed method is effective for registering the watermark from an image that had undergone both affine transformations and severe lossy compression.

This paper describes an efficient face recognition method using a combination of the Radon transform and the KL expansion. In this paper, each facial image is transformed into many sets of line integrals resulting from the Radon transform in 2D space. Based on this transformation, a new face-recognition method is proposed by using many subspaces generated from the vector spaces of the Radon transform. The efficiencies of the proposed method are proved by the classification rate of 100% in the experimental results, and the reduction to O(n4) instead of O(n6) of the operation complexity in KL(Karhunen-Loeve) expansion, where n is the size of sample images.