R.J. Kolczynski

Bio: R.J. Kolczynski is an academic researcher from Sarnoff Corporation. The author has contributed to research in topics: Motion analysis & Motion field. The author has an hindex of 4, co-authored 4 publications receiving 1179 citations.

Enhanced image capture through fusion

Abstract: The authors present an extension to the pyramid approach to image fusion. The modifications address problems that were encountered with past implementations of pyramid-based fusion. In particular, the modifications provide greater shift invariance and immunity to video noise, and provide at least a partial solution to the problem of combining components that have roughly equal salience but opposite contrasts. The fusion algorithm was found to perform well for a range of tasks without requiring adjustment of the algorithm parameters. Results were remarkably insensitive to changes in these parameters, suggesting that the procedure is both robust and generic. A composite imaging technique is outlined that may provide a powerful tool for image capture. By fusing a set of images obtained under restricted, narrowband, imaging conditions, it is often possible to construct an image that has enhanced information content when compared to a single image obtained directly with a broadband sensor. >

Object tracking with a moving camera

Abstract: The authors describe the implementation of the local and focal levels of a dynamic-motion-analysis framework. Dynamic motion analysis achieves efficiency through sequential decomposition of a complex analysis task into simpler tasks, by 'peeling off complexity', and by directing analysis to portions of a scene that are most critical to the vision task. The authors describe four basic techniques for implementing dynamic analysis: foveation, two-stage motion computation, tracking, and one-component-at-a-time segmentation. Each process entails several iterations of a basic operation but convergence is fast and the computations themselves can be relatively crude. By way of illustration, the dynamic motion analysis technique was applied to a number of image sequences. Particular attention is given to an actual video sequence of a helicopter flying over a terrain. The sequence was obtained from a camera moving relative to the helicopter. It is concluded that the dynamic approach to motion analysis holds the promise of performing real-time processing to obtain precise, robust results, using practical hardware. >

Mechanisms for isolating component patterns in the sequential analysis of multiple motion

Abstract: Pyramid techniques are commonly used to provide computational efficiency in the analysis of image motion. But these techniques can play an even more important role in the analysis of multiple motion, where, for example, a transparent pattern moves in front of a differently moving background pattern. The pyramid framework then separates motion components based on their spatial and temporal frequency characteristics so that each can be estimated independently of the others. This property is key to recently proposed selective stabilization algorithms for the sequential analysis of multiple motion and for the detection of moving objects from a moving platform. The authors determine the conditions for component selection. Results can provide important guidance in practical applications of motion analysis. >

OBJECT TRACKING WITH A MOVING CAMERA An Application of Dynaiiiic Motion Analysis

Abstract: The task of detect.ing and tracking moving 0bject.s is part.icularly challenging if it. niust be perfornied with a camera that is itself moving. Yet, in applications such as automated surveillalice and navigation, this task niust be perforiiied continuously, in real time, and using only niodest computing hardware. Dynaniic analysis techniques provide a key to real-time vision. Through strategies analogous to foveation and eye tracking in humans, these tecliniques direct. analysis to critical regions of a scene, and decompose tlie complex niotion problem into a sequence of relatively simple tasks.

Computer Vision: Algorithms and Applications

Abstract: Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

A survey of medical image registration.

Abstract: The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved.

Human and machine recognition of faces: a survey

Abstract: The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Hierarchical Model-Based Motion Estimation

Abstract: This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examples.

The computation of optical flow

Abstract: Two-dimensional image motion is the projection of the three-dimensional motion of objects, relative to a visual sensor, onto its image plane. Sequences of time-orderedimages allow the estimation of projected two-dimensional image motion as either instantaneous image velocities or discrete image displacements. These are usually called the optical flow field or the image velocity field. Provided that optical flow is a reliable approximation to two-dimensional image motion, it may then be used to recover the three-dimensional motion of the visual sensor (to within a scale factor) and the three-dimensional surface structure (shape or relative depth) through assumptions concerning the structure of the optical flow field, the three-dimensional environment, and the motion of the sensor. Optical flow may also be used to perform motion detection, object segmentation, time-to-collision and focus of expansion calculations, motion compensated encoding, and stereo disparity measurement. We investigate the computation of optical flow in this survey: widely known methods for estimating optical flow are classified and examined by scrutinizing the hypothesis and assumptions they use. The survey concludes with a discussion of current research issues.