Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

The word robust has been used in many contexts in signal processing. Our treatment concerns statistical robustness, which deals with deviations from the distributional assumptions. Many problems encountered in engineering practice rely on the Gaussian distribution of the data, which in many situations is well justified. This enables a simple derivation of optimal estimators. Nominal optimality, however, is useless if the estimator was derived under distributional assumptions on the noise and the signal that do not hold in practice. Even slight deviations from the assumed distribution may cause the estimator's performance to drastically degrade or to completely break down. The signal processing practitioner should, therefore, ask whether the performance of the derived estimator is acceptable in situations where the distributional assumptions do not hold. Isn't it robustness that is of a major concern for engineering practice? Many areas of engineering today show that the distribution of the measurements is far from Gaussian as it contains outliers, which cause the distribution to be heavy tailed. Under such scenarios, we address single and multichannel estimation problems as well as linear univariate regression for independently and identically distributed (i.i.d.) data. A rather extensive treatment of the important and challenging case of dependent data for the signal processing practitioner is also included. For these problems, a comparative analysis of the most important robust methods is carried out by evaluating their performance theoretically, using simulations as well as real-world data.

Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts

Fluorescent speckle microscopy (FSM) is a technology used to analyze the dynamics of macromolecular assemblies in vivo and in vitro. Speckle formation by random association of fluorophores with a macromolecular structure was originally discovered for microtubules. Since then FSM has been expanded to study other cytoskeleton and cytoskeleton-binding proteins. Specialized software has been developed to convert the stochastic speckle image signal into spatiotemporal maps of polymer transport and turnover in living cells. These maps serve as a unique quantitative readout of the dynamic steady state of the cytoskeleton and its responses to molecular and genetic interventions, allowing a systematic study of the mechanisms of cytoskeleton regulation and its effect on cell function. Here, we explain the principles of FSM imaging and signal analysis, outline the biological questions and corresponding methodological advances that have led to the current state of FSM, and give a glimpse of new FSM modalities under development.

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Quantitative fluorescent speckle microscopy of cytoskeleton dynamics.

This thesis introduces a new signal transform, called polynomial expansion, and based on this develops novel methods for estimation of orientation and motion. The methods are designed exclusively i ...

http://liu.diva-portal.org/smash/get/diva2:302485/FULLTEXT01

Polynomial Expansion for Orientation and Motion Estimation

This paper presents a new method for estimating piecewise-smooth optical flow. We propose a global optimization formulation with three-frame matching and local variation and develop an efficient technique to minimize the resultant global energy. This technique takes advantage of local gradient, global gradient, and global matching methods and alleviates their limitations. Experiments on various synthetic and real data show that this method achieves highly competitive accuracy.

/pdf/estimating-piecewise-smooth-optical-flow-with-global-3y3fxxtmyb.pdf

Estimating piecewise-smooth optical flow with global matching and graduated optimization

This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms.

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Algorithm performance contest

Methods, systems, and computer-readable media for ascertaining neighborhood information in a dynamically changing environment, such as an electronic ink environment may include: (a) receiving data representing plural electronic ink strokes; (b) defining a first vertex associated with a first ink stroke; and (c) determining neighboring vertices to the first vertex, wherein the neighboring vertices are associated with ink stroke(s) other than the first ink stroke. Additional systems, methods, and computer-readable media may include: (a) receiving data representing plural electronic ink strokes; (b) defining plural vertices associated with the ink strokes; (c) receiving input indicating a selection of an ink component; and (d) determining at least one neighboring component by determining which ink component(s) located outside of the selection include one or more ink strokes having vertices that neighbor vertices included in the selection.

Systems, methods, and computer-readable media for fast neighborhood determinations in dynamic environments

Optical flow estimation can be formulated as two regression stages: derivative estimation and optical flow constraints (OFC) solving. Traditional approaches use least-squares at both stages and are sensitive to assumption violations. To improve estimation accuracy, especially near motion boundaries, we use a least trimmed squares (LTS) estimator to solve the OFC, obtaining a confidence measure for each estimate; and at place with low confidence, we use another LTS estimator to make the derivative estimation robust. This adaptive two-stage robust scheme has significantly higher accuracy than non-robust algorithms and those only using robust methods at the OFC stage. Advantages are illustrated on both synthetic and real data.

/pdf/optical-flow-from-a-least-trimmed-squares-based-adaptive-2r66hsmze6.pdf

Optical flow from a least-trimmed squares based adaptive approach

A system and method for determining hierarchical information is described. Aspects include using the Collins model for parsing non-textual information into hierarchical content. The system and process assign labels to lines that indicate how the lines relate to one another.

Ming Ye

Papers

Estimating piecewise-smooth optical flow with global matching and graduated optimization

Algorithm performance contest

Systems, methods, and computer-readable media for fast neighborhood determinations in dynamic environments

Optical flow from a least-trimmed squares based adaptive approach

Parsing hierarchical lists and outlines