Barriers to cervical screening participation in high-risk women

Aim Women aged 25–35 years, for whom cervical cancer is most problematic, are least likely to participate in the cervical screening programme. Therefore, identifying barriers to screening participation in this high-risk group is essential. Subject and methods A sample of 430 women completed an electronic survey of their cervical screening history and answered questions on sociodemographic, behavioural, attitudinal and informational barriers to cervical screening uptake. Logistic regression was used to predict cervical screening non attendance. Results Women with more than 10 sexual partners in their lifetime were more likely, but women from ethnic minorities, less likely to participate in the cervical screening programme. Women unaware of the recommended screening interval were also less likely to be screened, as were women who believed that screening is a test for cancer. Screening was also less likely among women who endorsed the belief that screening in the absence of symptoms is unnecessary. Conclusion These data highlight poor knowledge of the recommended screening interval and purpose of cervical cancer screening in this high-risk group. As such, interventions that target these informational barriers might be most effective for increasing cervical screening uptake in this high-risk group

Hidden Markov Models for Spatio-Temporal Pattern Recognition and Image Segmentation

Time and again hidden Markov models have been demonstrated to be highly effective in one-dimensional pattern recognition and classification problems such as speech recognition. A great deal of attention is now focussed on 2-D and possibly 3-D applications arising from problems encountered in computer vision in domains such as gesture, face, and handwriting recognition. Despite their widespread usage and numerous successful applications, there are few analytical results which can explain their remarkably good performance and guide researchers in selecting topologies and parameters to improve classification performance

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%

Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach

Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a higher dimensional space. However, since these manifolds belong to non-Euclidean topological spaces, exploiting their structures is computationally expensive, especially when one considers the clustering analysis of massive amounts of data. To this end, we propose an efficient framework to address the clustering problem on Riemannian manifolds. This framework implements random projections for manifold points via kernel space, which can preserve the geometric structure of the original space, but is computationally efficient. Here, we introduce three methods that follow our framework. We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds. Experimental results demonstrate that our framework maintains the performance of the clustering whilst massively reducing computational complexity by over two orders of magnitude in some cases

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

3D Reconstruction through Segmentation of Multi-View Image Sequences

We propose what we believe is a new approach to 3D reconstruction through the design of a 3D voxel volume, such that all the image information and camera geometry are embedded into one feature space. By customising the volume to be suitable for segmentation, the key idea that we propose is the recovery of a 3D scene through the use of globally optimal geodesic active contours. We also present an extension to this idea by proposing the novel design of a 4D voxel volume to analyse the stereo motion problem in multi-view image sequences

Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences

This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into Hilbert spaces through two types of Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding, but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification and texture categorization

Polar tongue of ionization (TOI) and associated Joule heating intensification investigated during the magnetically disturbed period of 1–2 October 2001

We investigate storm-enhanced density (SED) and polar tongue of ionization (TOI) over North America under southward Interplanetary Magnetic Field conditions. We focus on the 30 September to 1 October 2001 medium magnetic storm's recovery phase (Period 1) and on the last substorm (Period 2) of the following 2 October substorm series. We aim to study the SED-TOI structure in the time frame of solar wind energy input to the magnetosphere-ionosphere system and in terms of Joule heating. We utilize GPS total electron content maps tracking SED plume and polar TOI, and spectrogram images detecting polar rain and precipitation void and thus evidencing dayside merging. The variations of merging electric (E) field (E) and its mapped-down polar equivalent (E), energy input efficiency (EI), and modeled Joule heating rate (Q) are monitored. Results show multiple Joule heating intensification points implying multiple energy deposition points at high latitudes where the magnetic pole was one of the preferred locations. During the higher EI (~1.5%) Period 2, the polar TOI was associated with a well-defined strong Q intensification and with polar rain (or void) on the dayside (or nightside). During the lower EI (~0.5%) Period 1, only weak Q intensification occurred in the absence of both polar TOI and polar rain. We highlight the polar TOI's potential impact on the thermosphere. We conclude that (i) strong (E ≈ 5 mV/m during Period 2) or weak (E ≈ 0.5–2 mV/m during Period 1) E facilitated energy deposition close to the magnetic pole and (ii) E could be used as a diagnostic of the polar TOI's intensity

MMX-Accelerated Real-Time Hand Tracking System

We describe a system for tracking real-time hand gestures captured by a cheap web camera and a standard Intel Pentium based personal computer with no specialized image processing hardware. To attain the necessary processing speed, the system exploits the Multi-Media Instruction set(MMX) extensions of the Intel Pentium chip family through software including. the Microsoft DirectX SDK and the Intel Image Processing and Open Source Computer Vision (OpenCV) libraries. The system is based on the Camshift algorithm (from OpenCV) and the compound constant acceleration Kalman filter algorithms. Tracking is robust and efficient and can track hand motion at 30 fps

Multi-region probabilistic histograms for robust and scalable identity inference

We propose a scalable face matching algorithm capable of dealing with faces subject to several concurrent and uncontrolled factors, such as variations in pose, expression, illumination, resolution, as well as scale and misalignment problems. Each face is described in terms of multi-region probabilistic histograms of visual words, followed by a normalised distance calculation between the histograms of two faces. We also propose a fast histogram approximation method which dramatically reduces the computational burden with minimal impact on discrimination performance. Experiments on the “Labeled Faces in the Wild” dataset (unconstrained environments) as well as FERET (controlled variations) show that the proposed algorithm obtains performance on par with a more complex method and displays a clear advantage over predecessor systems. Furthermore, the use of multiple regions (as opposed to a single overall region) improves accuracy in most cases, especially when dealing with illumination changes and very low resolution images. The experiments also show that normalised distances can noticeably improve robustness by partially counteracting the effects of image variations