A Study of feature extraction algorithms for optical flow tracking

Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbours will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi's “Good features to track", SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned aerial vehicles, and for the purpose of visual odometry estimation.

A New Voltage Stability Index based on the Tangent Vector of the Power Flow Jacobian

This paper presents a new voltage stability index based on the tangent vector of the power flow jacobian. This index is capable of providing the relative vulnerability information of the system buses from the point of view of voltage collapse. In an effort to compare this index with a similar index, the popular voltage stability index L is studied and it is shown through system studies that the L index is not a very consistent indicator of the voltage collapse point of the system but is only a reasonable indicator of the vulnerability of the system buses to voltage collapse. We also show that the new index can be used in the voltage stability analysis of radial systems which is not possible with the L index. This is a significant result of this investigation since there is a lot of contemporary interest in distributed generation and microgrids which are by and large radial in nature. Simulation results considering several test systems are provided to validate the results and the computational needs of the proposed scheme is assessed in comparison with other schemes

Real-time block flow tracking of atrial septal defect motion in 4D cardiac ultrasound

Real-time cardiac ultrasound allows monitoring the heart motion during intracardiac beating heart procedures. Our application assists atrial septal defect (ASD) closure techniques using real-time 3D ultrasound guidance. One major image processing challenge is the processing of information at high frame rate. We present an optimized block flow technique, which combines the probability-based velocity computation for an entire block with template matching. We propose adapted similarity constraints both from frame to frame, to conserve energy, and globally, to minimize errors. We show tracking results on eight in-vivo 4D datasets acquired from porcine beating-heart procedures. Computing velocity at the block level with an optimized scheme, our technique tracks ASD motion at 41 frames/s. We analyze the errors of motion estimation and retrieve the cardiac cycle in ungated images. © 2007 IEEE.

Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm

Image segmentation plays an important role in the analysis of retinal images as the extraction of the optic disk provides important cues for accurate diagnosis of various retinopathic diseases. In recent years, gradient vector flow (GVF) based algorithms have been used successfully to successfully segment a variety of medical imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods can lead to less accurate segmentation results in certain cases. In this paper, we propose the use of a new mean shift-based GVF segmentation algorithm that drives the internal/external energies towards the correct direction. The proposed method incorporates a mean shift operation within the standard GVF cost function to arrive at a more accurate segmentation. Experimental results on a large dataset of retinal images demonstrate that the presented method optimally detects the border of the optic disc.

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.

Vector flow mapping using plane wave ultrasound imaging

Les diagnostics cliniques des maladies cardio-vasculaires sont principalement effectués à l’aide d’échographies Doppler-couleur malgré ses restrictions : mesures de vélocité dépendantes de l’angle ainsi qu’une fréquence d’images plus faible à cause de focalisation traditionnelle. Deux études, utilisant des approches différentes, adressent ces restrictions en utilisant l’imagerie à onde-plane, post-traitée avec des méthodes de délai et sommation et d’autocorrélation. L’objectif de la présente étude est de ré-implémenté ces méthodes pour analyser certains paramètres qui affecte la précision des estimations de la vélocité du flux sanguin en utilisant le Doppler vectoriel 2D. À l’aide d’expériences in vitro sur des flux paraboliques stationnaires effectuées avec un système Verasonics, l’impact de quatre paramètres sur la précision de la cartographie a été évalué : le nombre d’inclinaisons par orientation, la longueur d’ensemble pour les images à orientation unique, le nombre de cycles par pulsation, ainsi que l’angle de l’orientation pour différents flux. Les valeurs optimales sont de 7 inclinaisons par orientation, une orientation de ±15° avec 6 cycles par pulsation. La précision de la reconstruction est comparable à l’échographie Doppler conventionnelle, tout en ayant une fréquence d’image 10 à 20 fois supérieure, permettant une meilleure caractérisation des transitions rapides qui requiert une résolution temporelle élevée.

Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.

Singular vector ensemble forecasting systems and the prediction of flow dependent uncertainty

The ECMWF ensemble weather forecasts are generated by perturbing the initial conditions of the forecast using a subset of the singular vectors of the linearised propagator. Previous results show that when creating probabilistic forecasts from this ensemble better forecasts are obtained if the mean of the spread and the variability of the spread are calibrated separately. We show results from a simple linear model that suggest that this may be a generic property for all singular vector based ensemble forecasting systems based on only a subset of the full set of singular vectors.

Automatic Classification of Three-Phase Flow Patterns of Heavy Oil in a Horizontal Pipe Using Support Vector Machines

The pipe flow of a viscous-oil-gas-water mixture such as that involved in heavy oil production is a rather complex thereto-fluid dynamical problem. Considering the complexity of three-phase flow, it is of fundamental importance the introduction of a flow pattern classification tool to obtain useful information about the flow structure. Flow patterns are important because they indicate the degree of mixing during flow and the spatial distribution of phases. In particular, the pressure drop and temperature evolution along the pipe is highly dependent on the spatial configuration of the phases. In this work we investigate the three-phase water-assisted flow patterns, i.e. those configurations where water is injected in order to reduce friction caused by the viscous oil. Phase flow rates and pressure drop data from previous laboratory experiments in a horizontal pipe are used for flow pattern identification by means of the 'support vector machine' technique (SVM).