Ruwan B. Tennakoon

Robust Model Fitting Using Higher Than Minimal Subset Sampling

Identifying the underlying model in a set of data contaminated by noise and outliers is a fundamental task in computer vision. The cost function associated with such tasks is often highly complex, hence in most cases only an approximate solution is obtained by evaluating the cost function on discrete locations in the parameter (hypothesis) space. To be successful at least one hypothesis has to be in the vicinity of the solution. Due to noise hypotheses generated by minimal subsets can be far from the underlying model, even when the samples are from the said structure. In this paper we investigate the feasibility of using higher than minimal subset sampling for hypothesis generation. Our empirical studies showed that increasing the sample size beyond minimal size (p), in particular up to p + 2, will significantly increase the probability of generating a hypothesis closer to the true model when subsets are selected from inliers. On the other hand, the probability of selecting an all inlier sample rapidly decreases with the sample size, making direct extension of existing methods unfeasible. Hence, we propose a new computationally tractable method for robust model fitting that uses higher than minimal subsets. Here, one starts from an arbitrary hypothesis (which does not need to be in the vicinity of the solution) and moves until either a structure in data is found or the process is re-initialized. The method also has the ability to identify when the algorithm has reached a hypothesis with adequate accuracy and stops appropriately, thereby saving computational time. The experimental analysis carried out using synthetic and real data shows that the proposed method is both accurate and efficient compared to the state-of-the-art robust model fitting techniques.

Nonrigid Registration of Volumetric Images Using Ranked Order Statistics

Nonrigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation burden and increase the registration accuracy has become an intensive area of research. In this paper, we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of end-inhale to end-exhale lung CT scan pairs, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art sampling based techniques, particularly for registration of images with large deformations.

Quantification of Smoothing Requirement for 3D Optic Flow Calculation of Volumetric Images

Complexities of dynamic volumetric imaging challenge the available computer vision techniques on a number of different fronts. This paper examines the relationship between the estimation accuracy and required amount of smoothness for a general solution from a robust statistics perspective. We show that a (surprisingly) small amount of local smoothing is required to satisfy both the necessary and sufficient conditions for accurate optic flow estimation. This notion is called “just enough” smoothing, and its proper implementation has a profound effect on the preservation of local information in processing 3D dynamic scans. To demonstrate the effect of “just enough” smoothing, a robust 3D optic flow method with quantized local smoothing is presented, and the effect of local smoothing on the accuracy of motion estimation in dynamic lung CT images is examined using both synthetic and real image sequences with ground truth.

Robust Data Modelling Using Thin Plate Splines

Using splines to model spatio-temporal data is one of the most common methods of data fitting used in a variety of computer vision applications. Despite its ubiquitous applications, particularly for volumetric image registration and interpolation, the existing estimation methods are still sensitive to the existence of noise and outliers. A method of robust data modelling using thin plate splines, based upon the well-known least K-th order statistical model fitting, is proposed and compared with the best available robust spline fitting techniques. Our experiments show that existing methods are not suitable for typical computer vision applications where outliers are structured (pseudo-outliers) while the proposed method performs well even when there are numerous pseudo-outliers.

Labeled multi-Bernoulli tracking for industrial mobile platform safety

This paper presents a track-before-detect labeled multi-Bernoulli filter tailored for industrial mobile platform safety applications. We derive two application specific separable likelihood functions that capture the geometric shape and colour information of the human targets who are wearing a high visibility vest. These likelihoods are then used in a labeled multi-Bernoulli filter with a novel two step Bayesian update. Preliminary simulation results evaluated using several video sequences show that the proposed solution can successfully track human workers wearing a luminous yellow colour vest in an industrial environment.

Efficient nonrigid registration using ranked order statistics

Non-rigid image registration techniques are widely used in medical imaging applications. Due to high computational complexities of these techniques, finding appropriate registration method to both reduce the computation burden and increase the registration accuracy has become an intense area of research. In this paper we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of real lung CT images, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art techniques, particularly for registration of images with large deformations.

Visual Inspection of Storm-Water Pipe Systems using Deep Convolutional Neural Networks.

Condition monitoring of storm-water pipe systems are carried-out regularly using semi-automated processors. Semi-automated inspection is time consuming, expensive and produces varying and relatively unreliable results due to operators fatigue and novicity. This paper propose an innovative method to automate the storm-water pipe inspection and condition assessment process which employs a computer vision algorithm based on deep-neural network architecture to classify the defect types automatically. With the proposed method, the operator only needs to guide the robot through each pipe and no longer needs to be an expert. The results obtained on a CCTV video dataset of storm-water pipes shows that the deep neural network architectures trained with data augmentation and transfer learning is capable of achieving high accuracies in identifying the defect types.

Robust visual data segmentation: Sampling from distribution of model parameters

Abstract This paper approaches the problem of geometric multi-model fitting as a data segmentation problem. The proposed solution is based on a sequence of sampling hyperedges from a hypergraph, model selection and hypergraph clustering steps. We developed a sampling method that significantly facilitates solving the segmentation problem using a new form of the Markov-Chain-Monte-Carlo (MCMC) method to effectively sample from hyperedge distribution. To sample from this distribution effectively, our proposed Markov Chain includes new ways of long and short jumps to perform exploration and exploitation of all structures. To enhance the quality of samples, a greedy algorithm is used to exploit nearby structure based on the minimization of the Least kth Order Statistics cost function. Unlike common sampling methods, ours does not require any specific prior knowledge about the distribution of models. The output set of samples leads to a clustering solution by which the final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.

MCMC based sampling technique for robust multi-model fitting and visual data segmentation

This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution in the parameter space that is obtained by modifying the Least kth Order Statistics cost function. To sample from this distribution effectively, our proposed Markov Chain includes novel long and short jumps to ensure exploration and exploitation of all structures. It also includes fast local optimization steps to target all, even fairly small, putative structures. This leads to a clustering solution through which final model parameters for each segment are obtained. The method competes favorably with the state-of-the-art both in terms of computation power and segmentation accuracy.

Nonlinear Approaches in Three Dimensional Medical Image Registration

Image registration is the task of finding a spatial transformation T that aligns the objects in two or more images capturing the same or related scene. It is one of the most crucial problems of computer vision and has been studied for over three decades. The underlying task is very general and has a wide range of applications in the fields of medical imaging, machine vision, remote sensing, cartography, etc. In this chapter we focus on the application of image registration in the fields of medical imaging.