Optical Flow Estimation using Fourier Mellin Transform

In this paper, we propose a novel method of computing the optical flow using the Fourier Mellin Transform (FMT). Each image in a sequence is divided into a regular grid of patches and the optical flow is estimated by calculating the phase correlation of each pair of co-sited patches using the FMT. By applying the FMT in calculating the phase correlation, we are able to estimate not only the pure translation, as limited in the case of the basic phase correlation techniques, but also the scale and rotation motion of image patches, i.e. full similarity transforms. Moreover, the motion parameters of each patch can be estimated to subpixel accuracy based on a recently proposed algorithm that uses a 2D esinc function in fitting the data from the phase correlation output. We also improve the estimation of the optical flow by presenting a method of smoothing the field by using a vector weighted average filter. Finally, experimental results, using publicly available data sets are presented, demonstrating the accuracy and improvements of our method over previous optical flow methods

Face Recognition and Facial Attribute Analysis from Unconstrained Visual Data

Analyzing human faces from visual data has been one of the most active research areas in the computer vision community. However, it is a very challenging problem in unconstrained environments due to variations in pose, illumination, expression, occlusion and blur between training and testing images. The task becomes even more difficult when only a limited number of images per subject is available for modeling these variations. In this dissertation, different techniques for performing classification of human faces as well as other facial attributes such as expression, age, gender, and head pose in uncontrolled settings are investigated. In the first part of the dissertation, a method for reconstructing the virtual frontal view from a given non-frontal face image using Markov Random Fields (MRFs) and an efficient variant of the Belief Propagation (BP) algorithm is introduced. In the proposed approach, the input face image is divided into a grid of overlapping patches and a globally optimal set of local warps is estimated to synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it with images from a training database of frontal faces. The alignments are performed efficiently in the Fourier domain using an extension of the Lucas-Kanade (LK) algorithm that can handle illumination variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem using an MRF. The reconstructed frontal face image can then be used with any face recognition technique. The two main advantages of our method are that it does not require manually selected facial landmarks as well as no head pose estimation is needed. In the second part, the task of face recognition in unconstrained settings is formulated as a domain adaptation problem. The domain shift is accounted for by deriving a latent subspace or domain, which jointly characterizes the multifactor variations using appropriate image formation models for each factor. The latent domain is defined as a product of Grassmann manifolds based on the underlying geometry of the tensor space, and recognition is performed across domain shift using statistics consistent with the tensor geometry. More specifically, given a face image from the source or target domain, multiple images of that subject are first synthesized under different illuminations, blur conditions, and 2D perturbations to form a tensor representation of the face. The orthogonal matrices obtained from the decomposition of this tensor, where each matrix corresponds to a factor variation, are used to characterize the subject as a point on a product of Grassmann manifolds. For cases with only one image per subject in the source domain, the identity of target domain faces is estimated using the geodesic distance on product manifolds. When multiple images per subject are available, an extension of kernel discriminant analysis is developed using a novel kernel based on the projection metric on product spaces. Furthermore, a probabilistic approach to the problem of classifying image sets on product manifolds is introduced. Understanding attributes such as expression, age class, and gender from face images has many applications in multimedia processing including content personalization, human-computer interaction, and facial identification. To achieve good performance in these tasks, it is important to be able to extract pertinent visual structures from the input data. In the third part of the dissertation, a fully automatic approach for performing classification of facial attributes based on hierarchical feature learning using sparse coding is presented. The proposed approach is generative in the sense that it does not use label information in the process of feature learning. As a result, the same feature representation can be applied for different tasks such as expression, age, and gender classification. Final classification is performed by linear SVM trained with the corresponding labels for each task. The last part of the dissertation presents an automatic algorithm for determining the head pose from a given face image. The face image is divided into a regular grid and represented by dense SIFT descriptors extracted from the grid points. Random Projection (RP) is then applied to reduce the dimension of the concatenated SIFT descriptor vector. Classification and regression using Support Vector Machine (SVM) are combined in order to obtain an accurate estimate of the head pose. The advantage of the proposed approach is that it does not require facial landmarks such as the eye and mouth corners, the nose tip to be extracted from the input face image as in many other methods

PHÁT HIỆN VI KHUẨN PASTEURELLA MULTOCIDA GÂY BỆNH TỤ HUYẾT TRÙNG Ở CỪU PHAN RANG BẰNG KỸ THUẬT PCR

Pasteurellosis is an infectious disease causing severe damage to livestock and poultry since it leads to animals’ death, including sheep of all ages. In this study, we developed a sensitive, specific and accurate PCR method for detecting P. multocida in Phan Rang sheep using FKMT1/RKMT1 primers targeted to the KMT1 gene of P. multocida. The PCR-based method was used to detect the KMT1 gene from 104 copies of the KMT1-bearing plasmid. The method’s specificity was demonstrated by the successful amplification of KMT1 from the mixture containing other DNA of Gram-negative bacteria, such as E. coli or M. haemolytica. In particular, with the present method, we successfully detected P. multocida with crude DNA obtained from P. multocida or nasal swabs of pasteurellosis-suspected sheep samples treated in a TE buffer containing 0.1% TritonX-100. This is the first report on using a PCR-based method for detecting P. multocida from Phan Rang sheep, and it can serve as the basis for an effective procedure to diagnose pasteurellosis in sheep.Tụ huyết trùng là một bệnh truyền nhiễm có thể gây thiệt hại nghiêm trọng cho chăn nuôi gia súc, gia cầm nếu không được chẩn đoán và điều trị kịp thời. Vì vậy, trong nghiên cứu này chúng tôi đã phát triển phương pháp PCR có độ nhạy, độ đặc hiệu và độ chính xác cao để phát hiện sự có mặt của vi khuẩn P. multocida, một trong những tác nhân gây bệnh tụ huyết trùng trên cừu Phan Rang. Gen KMT1 của vi khuẩn P. multocida được nhân lên bằng cặp mồi đặc hiệu FKMT1/RKMT1 ở nồng độ khoảng 104 bản sao của plasmid mang gen đích. Phản ứng PCR không bị ảnh hưởng khi có mặt DNA của một số vi khuẩn Gram âm khác như E. coli hay M. haemolytica. Đặc biệt, với phương pháp PCR, chúng tôi đã phát hiện sự có mặt của vi khuẩn P. multocida từ mẫu DNA thô thu được bằng xử lý mẫu vi khuẩn/mẫu dịch ngoáy mũi của cừu nghi nhiễm bệnh trong đệm TE chứa 0,1% TritonX-100 mà không cần tinh sạch DNA tổng số. Đây là nghiên cứu đầu tiên công bố một phương pháp xác định sự có mặt của P. multocida trên cừu Phan Rang và là cơ sở để xây dựng quy trình chẩn đoán hiệu quả nhằm góp phần kiểm soát bệnh tụ huyết trùng trên đối tượng này

A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials

Background Dengue is a neglected tropical disease, for which no therapeutic agents have shown clinical efficacy to date. Clinical trials have used strikingly variable clinical endpoints, which hampers reproducibility and comparability of findings. We investigated a delta modified Sequential Organ Failure Assessment (delta mSOFA) score as a uniform composite clinical endpoint for use in clinical trials investigating therapeutics for moderate and severe dengue. Methods We developed a modified SOFA score for dengue, measured and evaluated its performance at baseline and 48 h after enrolment in a prospective observational cohort of 124 adults admitted to a tertiary referral hospital in Vietnam with dengue shock. The modified SOFA score included pulse pressure in the cardiovascular component. Binary logistic regression, cox proportional hazard and linear regression models were used to estimate association between mSOFA, delta mSOFA and clinical outcomes. Results The analysis included 124 adults with dengue shock. 29 (23.4%) patients required ICU admission for organ support or due to persistent haemodynamic instability: 9/124 (7.3%) required mechanical ventilation, 8/124 (6.5%) required vasopressors, 6/124 (4.8%) required haemofiltration and 5/124 (4.0%) patients died. In univariate analyses, higher baseline and delta (48 h) mSOFA score for dengue were associated with admission to ICU, requirement for organ support and mortality, duration of ICU and hospital admission and IV fluid use. Conclusions The baseline and delta mSOFA scores for dengue performed well to discriminate patients with dengue shock by clinical outcomes, including duration of ICU and hospital admission, requirement for organ support and death. We plan to use delta mSOFA as the primary endpoint in an upcoming host-directed therapeutic trial and investigate the performance of this score in other phenotypes of severe dengue in adults and children

3D surface matching from range images using multiscale local features.

Object recognition is one of the most important problems in computer vision. Traditional object recognition techniques are usually performed on optical images that are 2D projections of the 3D world. Information about the depth of objects in the scene is not provided explicitly in these images and thus, it makes 2D object recognition techniques sensitive to changes in illumination and shadowing. As surface acquisition methods such as LADAR or range scanners are becoming more popular, there is an increasing interest in the use of three-dimensional geometric data in object recognition to overcome these limitations. However, the matching of 3D free-form surfaces is also a difficult problem due to the shape and topological complexity of 3D surfaces. In addition, the problem is further complicated by other issues such as variations in surface sampling resolution, occlusion, clutter and sensor noise. The huge amount of information required to describe a 3D surface is also another challenge that 3D surface matching techniques have to deal with. This thesis investigates the problems of 3D surface matching that include 3D surface registration and object recognition from range images. It focuses on developing a novel and efficient framework for aligning 3D surfaces in different coordinate systems and from this, recognizing 3D models from scenes with high levels of occlusion and clutter using multi-scale local features. The first part of the thesis presents two different schemes for extracting salient geometric features from 3D surfaces using surface curvature measures known as the curvedness and shape index. By deriving the scale-space representation of the input surface, surface positions with high local curvature or high local shape variations are selected as features at various degrees of scale. One advantage of the proposed approaches is their applicability to both 3D meshes with connectivity information and unstructured point clouds. In the second part of the thesis, an application of the multi-scale feature extraction framework to 3D surface registration and object recognition is proposed. A Delaunay tetrahedrization is performed on the features extracted from each input range image to obtain a set of triangles. Possible correspondences are found by matching all possible pairs of triangles between the scene and model surfaces. From these correspondences, possible transformations between the two surfaces can be hypothesized and tested. In order to increase the accuracy and efficiency of the algorithm, various surface geometric and rigidity constraints are applied to prune unlikely correspondences. By finding the match that aligns the largest number of features between the two surfaces, the best transformation can be estimated. In the case of surface registration, this transformation can be used to coarse-align two different views of the same object. In the case of 3D object recognition, it provides information about the possible pose (location and orientation) of the model in the scene surface. Experimental results on a variety of 3D models and real scenes are shown to verify the effectiveness and robustness of the approach.Thesis (M.App.Sc.) -- University of Adelaide, School of Electrical and Electronic Engineering, 200

Quaternion Potential Functions for a Colour Image Completion Method Using Markov Random Fields

An exemplar-based algorithm has been proposed recently to solve the Image completion problem by using a discrete global optimisation strategy based on Markov Random Fields. We can apply this algorithm to the task of completing colour images by processin

Optical flow estimation using Fourier Mellin Transform

In this paper, we propose a novel method of computing the optical flow using the Fourier Mellin Transform (FMT). Each image in a sequence is divided into a regular grid of patches and the optical flow is estimated by calculating the phase correlation of each pair of co-sited patches using the FMT. By applying the FMT in calculating the phase correlation, we are able to estimate not only the pure translation, as limited in the case of the basic phase correlation techniques, but also the scale and rotation motion of image patches, i.e. full similarity transforms. Moreover, the motion parameters of each patch can be estimated to sub-pixel accuracy based on a recently proposed algorithm that uses a 2D esinc function in fitting the data from the phase correlation output. We also improve the estimation of the optical flow by presenting a method of smoothing the field by using a vector weighted average filter. Finally, experimental results, using publicly available data sets are presented, demonstrating the accuracy and improvements of our method over previous optical flow methods

Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields

Recently, a new exemplar-based method for image completion, texture synthesis and image inpainting was proposed which uses a discrete global optimization strategy based on Markov Random. Fields. Its main advantage lies in the use of priority belief propagation and dynamic label, pruning to reduce the computational cost of standard belief propagation while producing high quality results. However, one of the drawbacks of the method is its use of a heuristically chosen parameter set. In this paper, a method for automatically determining the parameters for the belief propagation and dynamic label pruning steps is presented. The method is based on an information theoretic approach making use of the entropy of the image patches and the distribution of pairwise node potentials. A number of image completion results are shown demonstrating the effectiveness of our method