Reconstructing vectorised photographic images

We address the problem of representing captured images in the continuous mathematical space more usually associated with certain forms of drawn ('vector') images. Such an image is resolution-independent so can be used as a master for varying resolution-specific formats. We briefly describe the main features of a vectorising codec for photographic images, whose significance is that drawing programs can access images and image components as first-class vector objects. This paper focuses on the problem of rendering from the isochromic contour form of a vectorised image and demonstrates a new fill algorithm which could also be used in drawing generally. The fill method is described in terms of level set diffusion equations for clarity. Finally we show that image warping is both simplified and enhanced in this form and that we can demonstrate real histogram equalisation with genuinely rectangular histograms

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

Nonlocal Edge-Directed Interpolation

In this paper, we proposed a new edge-directed image interpolation algorithm which can preserve the edge features and natural appearance of images efficiently. In the proposed scheme, we first get a close-form solution of the optimal interpolation coefficients under the sense of minimal mean square error by exploiting autoregressive model (AR) and the geometric duality between the low-resolution and high-resolution images .Then the coefficients of the Nonlocal Edge-directed interpolation (NLEDI) are derived with structure similarity in images, which are solutions of weighted least square equations. The new image interpolation approach preserves spatial coherence of the interpolated images better than the existing methods and it outperforms the other methods in terms of objective and subjective image quality. ? 2009 Springer-Verlag Berlin Heidelberg.EI

An Investigation into the Effects of Image Resolution on a Facial-Image-Based Personal Authentication System

The issues associated with image resolution in automated authentication or identification systems has become one of the important challenges for researchers' in biometrics. The aim of this thesis is to investigate the effect of variable resolutions on the performance of a Facial-Image-Based Person Authentication System. Image resolution may vary significantly especially in uncontrolled acquisition environments or when sensing from a distance and so on. The detail available in the data thus reduces which may deteriorate the performance of such system. In this project we investigated the impact on system accuracy when image resolution is gradually reduced by a given factor. As a remedy, we investigated different methods for increasing image resolution prior to using those images for authentication and compared the relative gains in accuracy. The main procedure of the face image authentication system based on comparing landmarks of the face remains the same. In this study, we found that several issues related to image resolutions might have an impact on the recognition rate performance such as facial expressions, image background, and others. The influence of image resolution on the recognition rate increases roughly with the increasing resolution at a specific degree, high-image resolution would not be good for recognition rate always; reducing high image resolution makes it easier to achieve high face recognition rates

Tanner Graph Based Image Interpolation

This paper interprets image interpolation as a channel decoding problem and proposes a tanner graph based interpolation framework, which regards each pixel in an image as a variable node and the local image structure around each pixel as a check node. The pixels available from low-resolution image are 'received' whereas other missing pixels of high-resolution image are 'erased', through an imaginary channel. Local image structures exhibited by the low-resolution image provide information on the joint distribution of pixels in a small neighborhood, and thus play the same role as parity symbols in the classic channel coding scenarios. We develop an efficient solution for the sum-product algorithm of belief propagation in this framework, based on a gaussian auto-regressive image model. Initial experiments show up to 3dB gain over other methods with the same image model. The proposed framework is flexible in message processing at each node and provides much room for incorporating more sophisticated image modelling techniques. ? 2010 IEEE.EI

Video Deinterlacing using Control Grid Interpolation Frameworks

abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201

Robust Face Representation and Recognition Under Low Resolution and Difficult Lighting Conditions

This dissertation focuses on different aspects of face image analysis for accurate face recognition under low resolution and poor lighting conditions. A novel resolution enhancement technique is proposed for enhancing a low resolution face image into a high resolution image for better visualization and improved feature extraction, especially in a video surveillance environment. This method performs kernel regression and component feature learning in local neighborhood of the face images. It uses directional Fourier phase feature component to adaptively lean the regression kernel based on local covariance to estimate the high resolution image. For each patch in the neighborhood, four directional variances are estimated to adapt the interpolated pixels. A Modified Local Binary Pattern (MLBP) methodology for feature extraction is proposed to obtain robust face recognition under varying lighting conditions. Original LBP operator compares pixels in a local neighborhood with the center pixel and converts the resultant binary string to 8-bit integer value. So, it is less effective under difficult lighting conditions where variation between pixels is negligible. The proposed MLBP uses a two stage encoding procedure which is more robust in detecting this variation in a local patch. A novel dimensionality reduction technique called Marginality Preserving Embedding (MPE) is also proposed for enhancing the face recognition accuracy. Unlike Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which project data in a global sense, MPE seeks for a local structure in the manifold. This is similar to other subspace learning techniques but the difference with other manifold learning is that MPE preserves marginality in local reconstruction. Hence it provides better representation in low dimensional space and achieves lower error rates in face recognition. Two new concepts for robust face recognition are also presented in this dissertation. In the first approach, a neural network is used for training the system where input vectors are created by measuring distance from each input to its class mean. In the second approach, half-face symmetry is used, realizing the fact that the face images may contain various expressions such as open/close eye, open/close mouth etc., and classify the top half and bottom half separately and finally fuse the two results. By performing experiments on several standard face datasets, improved results were observed in all the new proposed methodologies. Research is progressing in developing a unified approach for the extraction of features suitable for accurate face recognition in a long range video sequence in complex environments

Locally Adaptive Wavelet-Based Image Interpolation

We describe a spatially adaptive algorithm for image interpolation. The algorithm uses a wavelet transform to extract information about sharp variations in the low-resolution image and then implicitly applies interpolation which adapts to the image local smoothness/singularity characteristics. The proposed algorithm yields images that are sharper compared to several other methods that we have considered in this paper. Better performance comes at the expense of higher complexity

Interpolation algorithms with image structure preservation

Predmet istraživanja ove doktorske disertacije je problem interpolacije slike. Glavni fokus disertacije je interpolacija slike uz očuvanje prirodnosti teksture i očuvanje ivica (oštrine) interpolirane slike. Dodatni izazov je da algoritam za interpolaciju slike bude pogodan za primenu u uređajima sa ograničenim resursima. Kvalitet rešenja se ocenjuje poređenjem sa algoritmima poznatim u dostupnoj literaturi korišćenjem odgovarajućih metrika.This PhD dissertation addresses the problem of image interpolation. The main focus of the dissertation is image interpolation algorithm which preserves edges and keeps a natural texture of interpolated images. Additional challenge for image interpolation algorithm is to be suitable for application on resourcelimited platforms. The quality of the proposed solution is benchmarked against known image interpolation algorithms using appropriate metrics

A Simple Edge-Sensitive Image Interpolation Filter

A novel scheme for edge-preserving image interpolation is introduced, which is based on the use of a simple nonlinear filter which accurately reconstructs sharp edges. Simulation results show the superior performances of the proposed approach with respect to other interpolation technique