Showing papers by "Majid Mirmehdi published in 2014"

Online quality assessment of human movement from skeleton data

Abstract: This work addresses the challenge of analysing the quality of human movements from visual information which has use in a broad range of applications, from diagnosis and rehabilitation to movement optimisation in sports science. Traditionally, such assessment is performed as a binary classification between normal and abnormal by comparison against normal and abnormal movement models, e.g. [5]. Since a single model of abnormal movement cannot encompass the variety of abnormalities, another class of methods only compares against one model of normal movement, e.g. [4]. We adopt this latter strategy and propose a continuous assessment of movement quality, rather than a binary classification, by quantifying the deviation from a normal model. In addition, while most methods can only analyse a movement after its completion e.g. [6], this assessment is performed on a frame-by-frame basis in order to allow fast system response in case of an emergency, such as a fall. Methods such as [4, 6] are specific to one type of movement, mostly due to the features used. In this work, we aim to represent a large variety of movements by exploiting full body information. We use a depth camera and a skeleton tracker [3] to obtain the position of the main joints of the body, as seen in Fig. 1. We normalise this skeleton for global position and orientation of the camera, and for the varying height of the subjects, e.g. using Procrustes analysis. The normalised skeletons have high dimensionality and tend to contain outliers. Thus, the dimensionality is reduced using Diffusion Maps [1] which is modified by including the extension that Gerber et al. [2] presented to deal with outliers in Laplacian Eigenmaps. The resulting high level feature vector Y, obtained from the normalised skeleton at one frame, represents an individual pose and is used to build a statistical model of normal movement. Our statistical model is made up of two components that describe the normal poses and the normal dynamics of the movement. The pose model is in the form of the probability density function (pdf) fY (y) of a random variable Y that takes as value y = Y our pose feature vector Y. The pdf is learnt from all the frames of training sequences that contain normal instances of the movement, using a Parzen window estimator. The quality of a new pose yt at frame t is then assessed as the log-likelihood of being described by the pose model, i.e.

Integrated Segmentation and Interpolation of Sparse Data

Abstract: We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

Segmentation of the right ventricle using diffusion maps and Markov random fields

Abstract: Accurate automated segmentation of the right ventricle is difficult due in part to the large shape variation found between patients. We explore the ability of manifold learning based shape models to represent the complexity of shape variation found within an RV dataset as compared to a typical PCA based model. This is empirically evaluated with the manifold model displaying a greater ability to represent complex shapes. Furthermore, we present a combined manifold shape model and Markov Random Field Segmentation framework. The novelty of this method is the iterative generation of targeted shape priors from the manifold using image information and a current estimate of the segmentation; a process that can be seen as a traversal across the manifold. We apply our method to the independently evaluated MICCAI 2012 RV Segmentation Challenge data set. Our method performs similarly or better than the state-of-the-art methods.

Real-time text tracking in natural scenes

Abstract: The authors present a system that automatically detects, recognises and tracks text in natural scenes in real-time. The focus of the author's method is on large text found in outdoor environments, such as shop signs, street names, billboards and so on. Built on top of their previously developed techniques for scene text detection and orientation estimation, the main contribution of this work is to present a complete end-to-end scene text reading system based on text tracking. They propose to use a set of unscented Kalman filters to maintain each text region's identity and to continuously track the homography transformation of the text into a fronto-parallel view, thereby being resilient to erratic camera motion and wide baseline changes in orientation. The system is designed for continuous, unsupervised operation in a handheld or wearable system over long periods of time. It is completely automatic and features quick failure recovery and interactive text reading. It is also highly parallelised to maximise usage of available processing power and achieve real-time operation. They demonstrate the performance of the system on sequences recorded in outdoor scenarios.

Text Line Aggregation

Abstract: We present a new approach to text line aggregation that can work as both a line formation stage for a myriad of text segmentation methods (over all orientations) and as an extra level of filtering to remove false text candidates. The proposed method is centred on the processing of candidate text components based on local and global measures. We use orientation histograms to build an understanding of paragraphs, and filter noise and construct lines based on the discovery of prominent orientations. Paragraphs are then reduced to seed components and lines are reconstructed around these components. We demonstrate results for text aggregation on the ICDAR 2003 Robust Reading Competition data, and also present results on our own more complex data set.

Weakly supervised learning of semantic colour terms

Abstract: Recognition of visual attributes in images allows an image's information content to be expressed textually. This has benefits for web search and image archiving, especially since visual attributes transcend language barriers. Classifiers are traditionally trained using manually segmented images, which are expensive and time consuming to produce. The authors propose a method which uses raw, noisy and unsegmented results of web image searches, to learn semantic colour terms. They use probabilistic graphical models on continuous domain, both for weakly supervised learning, and for segmentation of novel images. Experiments show that the authors methods give better results than the current state of the art in colour naming using noisy, weakly labelled training data. *Note: Colour figures are available in the online version of this paper.