Video Superresolution Reconstruction Using Iterative Back Projection with Critical-Point Filters Based Image Matching

To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction algorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is employed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based image matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR) frames are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration algorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical flow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement are obtained by the proposed algorithm, when applied to video sequence

Using Multi-Descriptors for Real Time Cosmetic Image Retrieval

Cosmetic Image Retrieval (CIR) is a methodology for searching and retrieving images from Cosmetic Image Collection (CIC). There are numerous cosmetic brands whose types are similar to others. In addition, there are not trivial to retrieve cosmetic images because of its complexity and duplicative shape, as well as detail of various cosmetic items. We present a method for CIR using multi-descriptors, combining global and local features for image descriptors. Along with integrating a Scale-Invariant Feature Transform (SIFT) and Critical Point Filters (CPFs) to achieve accuracy and agility in CIR processing, called CPF level 9 & SIFT. SIFT is used for detailed-image, such as cosmetic image, to reduce the time complexity for extracting keypoints. On the other side, CPF will filter only for the critical pixel of the image. From the experiment, our method can reduce computation time by 50.46% and 99.99% by using SIFT and CPF respectively. Moreover, our method is preserved efficiency, measured by precision and recall of CPF level 9 & SIFT, which is as high as the precision and recall of SIFT

The Research of Image Super-resolution Reconstruction Algorithm Based on MAP

随着图像智能化处理在各个领域的日益广泛应用，人们对图像的分辨率和清晰度提出了更高的要求，当前通过传感器捕捉的图像分辨率逐渐满足不了日益增长的需求，同时由于光学器件的物理局限性和昂贵的成本价格，因此基于软件思想的图像超分辨率重建的信号处理技术应运而生。超分辨率重建（Super-ResolutionReconstruction，SRR）技术利用已有的低分辨率成像系统，通过信号处理的方式来提高图像的分辨率，这种技术在国内外学术界和商业上得到了极大的重视和深入的研究，具有重要的理论和应用价值。 本文对于图像超分辨率重建的问题主要围绕着最大后验概率（MaximumAPosteriori，MAP）的方法...With intelligent image processing increasingly wide range of applications in various fields,the higher requirements of the resolution and clarity of the image had been proposed. Currently the resolution of the image captured by the sensor gradually failed to meet the growing demand, at the same time due to the physical limitations of the optics and expensive cost price. Therefore, based on the sof...学位：工学硕士院系专业：信息科学与技术学院_通信与信息系统学号：2332010115314

Example-based image color and tone style enhancement

Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers. © 2011 ACM.postprin

Joint View Triangulation for Two Views

International audienceWe propose the Joint View Triangulation, which coherently models all visible and partially occluded patches within n views of a scene (rigid or not). It is built from an underlying dense matching and can be used for any application requiring discrete and ef cient representation of deformation and displacement between views. First robustness has to deal the unavoidable matching errors. Secondly matched and half occluded areas should be separated in each view to allow different processes on them. Finally, the elements of the structure which represent the matched area of each view pair should be in correspondence. This ensures a global coherence of the data and avoid redundant processes. In fact, we merely expect to an approximate but coherent structure, because of the nite precision of the images and bad matches. This paper deals only with the two view case but also applies the joint view triangulation to morphing between real image pairs with large camera displacement

Wavelet methods in speech recognition

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application

Biometric iris image segmentation and feature extraction for iris recognition

PhD ThesisThe continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State University and Education Task Fund, Nigeri

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Feature based estimation of myocardial motion from tagged MR images

In the past few years we witnessed an increase in mortality due to cancer relative to mortality due to cardiovascular diseases. In 2008, the Netherlands Statistics Agency reports that 33.900 people died of cancer against 33.100 deaths due to cardiovascular diseases, making cancer the number one cause of death in the Netherlands [33]. Even if the rate of people affected by heart diseases is continually rising, they "simply don’t die of it", according to the research director Prof. Mat Daemen of research institute CARIM of the University of Maastricht [50]. The reason for this is the early diagnosis, and the treatment of people with identified risk factors for diseases like ischemic heart disease, hypertrophic cardiomyopathy, thoracic aortic disease, pericardial (sac around the heart) disease, cardiac tumors, pulmonary artery disease, valvular disease, and congenital heart disease before and after surgical repair. Cardiac imaging plays a crucial role in the early diagnosis, since it allows the accurate investigation of a large amount of imaging data in a small amount of time. Moreover, cardiac imaging reduces costs of inpatient care, as has been shown in recent studies [77]. With this in mind, in this work we have provided several tools with the aim to help the investigation of the cardiac motion. In chapters 2 and 3 we have explored a novel variational optic flow methodology based on multi-scale feature points to extract cardiac motion from tagged MR images. Compared to constant brightness methods, this new approach exhibits several advantages. Although the intensity of critical points is also influenced by fading, critical points do retain their characteristic even in the presence of intensity changes, such as in MR imaging. In an experiment in section 5.4 we have applied this optic flow approach directly on tagged MR images. A visual inspection confirmed that the extracted motion fields realistically depicted the cardiac wall motion. The method exploits also the advantages from the multiscale framework. Because sparse velocity formulas 2.9, 3.7, 6.21, and 7.5 provide a number of equations equal to the number of unknowns, the method does not suffer from the aperture problem in retrieving velocities associated to the critical points. In chapters 2 and 3 we have moreover introduced a smoothness component of the optic flow equation described by means of covariant derivatives. This is a novelty in the optic flow literature. Many variational optic flow methods present a smoothness component that penalizes for changes from global assumptions such as isotropic or anisotropic smoothness. In the smoothness term proposed deviations from a predefined motion model are penalized. Moreover, the proposed optic flow equation has been decomposed in rotation-free and divergence-free components. This decomposition allows independent tuning of the two components during the vector field reconstruction. The experiments and the Table of errors provided in 3.8 showed that the combination of the smoothness term, influenced by a predefined motion model, and the Helmholtz decomposition in the optic flow equation reduces the average angular error substantially (20%-25%) with respect to a similar technique that employs only standard derivatives in the smoothness term. In section 5.3 we extracted the motion field of a phantom of which we know the ground truth of and compared the performance of this optic flow method with the performance of other optic flow methods well known in the literature, such as the Horn and Schunck [76] approach, the Lucas and Kanade [111] technique and the tuple image multi-scale optic flow constraint equation of Van Assen et al. [163]. Tests showed that the proposed optic flow methodology provides the smallest average angular error (AAE = 3.84 degrees) and L2 norm = 0.1. In this work we employed the Helmholtz decomposition also to study the cardiac behavior, since the vector field decomposition allows to investigate cardiac contraction and cardiac rotation independently. In chapter 4 we carried out an analysis of cardiac motion of ten volunteers and one patient where we estimated the kinetic energy for the different components. This decomposition is useful since it allows to visualize and quantify the contributions of each single vector field component to the heart beat. Local measurements of the kinetic energy have also been used to detect areas of the cardiac walls with little movement. Experiments on a patient and a comparison between a late enhancement cardiac image and an illustration of the cardiac kinetic energy on a bull’s eye plot illustrated that a correspondence between an infarcted area and an area with very small kinetic energy exists. With the aim to extend in the future the proposed optic flow equation to a 3D approach, in chapter 6 we investigated the 3D winding number approach as a tool to locate critical points in volume images. We simplified the mathematics involved with respect to a previous work [150] and we provided several examples and applications such as cardiac motion estimation from 3-dimensional tagged images, follicle and neuronal cell counting. Finally in chapter 7 we continued our investigation on volume tagged MR images, by retrieving the cardiac motion field using a 3-dimensional and simple version of the proposed optic flow equation based on standard derivatives. We showed that the retrieved motion fields display the contracting and rotating behavior of the cardiac muscle. We moreover extracted the through-plane component, which provides a realistic illustration of the vector field and is missed by 2-dimensional approaches

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research