Tat-Jun Chin

Accelerated Hypothesis Generation for Multistructure Data via Preference Analysis

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.

Robust fitting of multiple structures: The statistical learning approach

We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. The Mercer kernel permits the application of well-grounded statistical learning methods, among which nonlinear dimensionality reduction, principal component analysis and spectral clustering are applied for robust fitting. Our method can remove gross outliers and in parallel discover the multiple structures present. It functions well under severe outliers (more than 90% of the data) and considerable inlier noise without requiring elaborate manual tuning or unrealistic prior information. Experiments on synthetic and real problems illustrate the superiority of the proposed idea over previous methods.

Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers

We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiple-structure data and is of interest by itself since it can be used in other robust estimators. In addition to IKOSE, our framework includes several original elements based on the weighting, clustering, and fusing of hypotheses. AKSWH can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously. We demonstrate good performance in practical applications such as line fitting, circle fitting, range image segmentation, homography estimation, and two--view-based motion segmentation, using both synthetic data and real images.

Dynamic and hierarchical multi-structure geometric model fitting

The ability to generate good model hypotheses is instrumental to accurate and robust geometric model fitting. We present a novel dynamic hypothesis generation algorithm for robust fitting of multiple structures. Underpinning our method is a fast guided sampling scheme enabled by analysing correlation of preferences induced by data and hypothesis residuals. Our method progressively accumulates evidence in the search space, and uses the information to dynamically (1) identify outliers, (2) filter unpromising hypotheses, and (3) bias the sampling for active discovery of multiple structures in the data—All achieved without sacrificing the speed associated with sampling-based methods. Our algorithm yields a disproportionately higher number of good hypotheses among the sampling outcomes, i.e., most hypotheses correspond to the genuine structures in the data. This directly supports a novel hierarchical model fitting algorithm that elicits the underlying stratified manner in which the structures are organized, allowing more meaningful results than traditional “flat” multi-structure fitting.

The Random Cluster Model for Robust Geometric Fitting

Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal or elemental subsets of the data, and hypothesize the geometric model from the selected subsets. While taking minimal subsets increases the chance of simultaneously “hitting” inliers in a sample, it amplifies the noise of the underlying model, and hypotheses fitted on minimal subsets may be severely biased even if they contain purely inliers. In this paper we propose to use Random Cluster Models, a technique used to simulate coupled spin systems, to conduct hypothesis generation using subsets larger than minimal. We show how large clusters of data from genuine instances of the geometric model can be efficiently harvested to produce more accurate hypotheses. To take advantage of our hypothesis generator, we construct a simple annealing method based on graph cuts to fit multiple instances of the geometric model in the data. Experimental results show clear improvements in efficiency over other methods based on minimal subset samplers.

Incremental kernel SVD for face recognition with image sets

Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such kernel methods include kernel PCA, kernel DA, kernel SVD and kernel QR. Since incremental computation algorithms for these methods do not exist yet, the practicality of these methods on large datasets or online video processing is minimal. We propose an approximate incremental kernel SVD algorithm for computer vision applications that require estimation of non-linear subspaces, specifically face recognition by matching image sets obtained through long-term observations or video recordings. We extend a well-known linear subspace updating algorithm to the nonlinear case by utilizing the kernel trick, and apply a reduced set construction method to produce sparse expressions for the derived subspace basis so as to maintain constant processing speed and memory usage. Experimental results demonstrate the effectiveness of the proposed method

Accelerated hypothesis generation for multi-structure robust fitting

Random hypothesis generation underpins many geometric model fitting techniques. Unfortunately it is also computationally expensive. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points to have arisen from the same model and is obtained without recourse to domain knowledge (e.g. keypoint matching scores) typically used in previous sampling enhancement methods. More crucially our approach is naturally capable of handling data with multiple model instances and excels in applications (e.g. multi-homography fitting) which easily frustrate other techniques. Experiments show that our method provides superior efficiency on various geometric model estimation tasks. Implementation of our algorithm is available on the authors, homepage.

A global optimization approach to robust multi-model fitting

We present a novel Quadratic Program (QP) formulation for robust multi-model fitting of geometric structures in vision data. Our objective function enforces both the fidelity of a model to the data and the similarity between its associated inliers. Departing from most previous optimization-based approaches, the outcome of our method is a ranking of a given set of putative models, instead of a pre-specified number of “good” candidates (or an attempt to decide the right number of models). This is particularly useful when the number of structures in the data is a priori unascertainable due to unknown intent and purposes. Another key advantage of our approach is that it operates in a unified optimization framework, and the standard QP form of our problem formulation permits globally convergent optimization techniques. We tested our method on several geometric multi-model fitting problems on both synthetic and real data. Experiments show that our method consistently achieves state-of-the-art results.

In defence of RANSAC for outlier rejection in deformable registration

This paper concerns the robust estimation of non-rigid deformations from feature correspondences. We advance the surprising view that for many realistic physical deformations, the error of the mismatches (outliers) usually dwarfs the effects of the curvature of the manifold on which the correct matches (inliers) lie, to the extent that one can tightly enclose the manifold within the error bounds of a low-dimensional hyperplane for accurate outlier rejection. This justifies a simple RANSAC-driven deformable registration technique that is at least as accurate as other methods based on the optimisation of fully deformable models. We support our ideas with comprehensive experiments on synthetic and real data typical of the deformations examined in the literature.

A Multiple Hypothesis Tracker for Multitarget Tracking With Multiple Simultaneous Measurements

Typical multitarget tracking systems assume that in every scan there is at most one measurement for each target. In certain other systems such as over-the-horizon radar tracking, the sensor can generate resolvable multiple detections, corresponding to different measurement modes, from the same target. In this paper, we propose a new algorithm called multiple detection multiple hypothesis tracker (MD-MHT) to effectively track multiple targets in such multiple-detection systems. The challenge for this tracker, which follows the multiple hypothesis framework, is to jointly resolve the measurement origin and measurement mode uncertainties. The proposed tracker solves this data association problem via an extension to the multiframe assignment algorithm. Its performance is demonstrated on a simulated over-the-horizon-radar multitarget tracking scenario, which confirms the effectiveness of this algorithm.