Bicubic interpolation

About: Bicubic interpolation is a research topic. Over the lifetime, 3348 publications have been published within this topic receiving 73126 citations.

B-Spline Surface Approximation to Cross-Sections Using Distance Maps

Abstract: The shape reconstruction of a 3D object from its 2D crosssections is important for reproducing it by NC machining or rapid prototyping In this paper, we present a method of surface approximation to cross-sections with multiple branching problems In this method, we first decompose each multiple branching problem into a set of single branching problems by providing a set of intermediate contours using distance maps For each single branching region, a procedure then performs the skinning of contour curves represented by cubic B-spline curves on a common knot vector, each of which is fitted to its contour points within a given accuracy In order to acquire a more compact representation for the surface, the method includes an algorithm for reducing the number of knots in the common knot vector The approximation surface to the crosssections is represented by a set of bicubic B-spline surfaces This method provides a smooth surface model, yet realises efficient data reduction

Multivariate interpolating (m, l, s)-splines

Abstract: The multivariate interpolating (m, l, s)-splines are a natural generalization of Duchon's thin plate splines (TPS). More precisely, we consider the problem of interpolation with respect to some finite number of linear continuous functionals defined on a semi-Hilbert space and minimizing its semi-norm. The (m, l, s)-splines are explicitly given as a linear combination of translates of radial basis functions. We prove the existence and uniqueness of the interpolating (m, l, s)-splines and investigate some of their properties. Finally, we present some practical examples of (m, l, s)-splines for Lagrange and Hermite interpolation.

Low Resolution Single Neural Network Based Face Recognition

Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on lowresolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses backpropagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image. Keywords—Average filtering, Bicubic Interpolation, Neurons, vectorization.

Four image interpolation techniques for ultrasound breast phantom data acquired using Fischer's full field digital mammography and ultrasound system (FFDMUS): a comparative approach

Abstract: In computer-aided analysis of mammograms, a fast processing analysis for mammograms benefits and facilitate the real time application and is also useful for radiologist to do on-line diagnosis. Down sampling is widely applied to reduce the size of large images and improve the processing speed. This paper presents the performance evaluations among several interpolation techniques (bilinear, bicubic, wavelet and Lanczos) for ultrasound breast phantom data. We also compared lesion segmentation results of down sampled images with the results of original images. Two major metrics: the Hausdorff distance measure (HDM) and polyline distance measure (PDM) were applied to measure the performance of the segmentation. We conclude that without scarifying the quality of ultrasound images, we increase the speed of system processing using our down sampling strategies. Moreover, among the four different technologies, the Lanczos algorithm can achieve the best performance.

Image resolution enhancement by using interpolation followed by iterative back projection

Abstract: In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the interpolated images are being registered in order to generate a sharper high resolution image. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as well as the visual results show the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. For Lena's image, the PSNR is 6.52 dB higher than the bicubic interpolation.