We present technical advances that were made in creating consumer level video mosaicing applications with inputs from hand held inexpensive cameras, and with near-real time response. In developing the VideoBrush/sup TM/ technologies, the goal was to give easily usable but robust video mosaicing capabilities in the hands of lay users. We highlight a number of problems that needed to be solved to enable this: 2D & 1D modes of scene scanning and alignment, progressive complexity of alignment models to provide real-time response to the user and multi-resolution blending for high quality mosaic output. The solution to these key technical challenges has led us to video mosaic technologies that can "scan" scenes as diverse as natural panoramic scenes to white boards and documents.

The two fundamental advantages of video over still imagery are: (i) the ability capture temporal information, and (ii) the ability to acquire a continuously varying set of views of a scene. These advantages are obtained, however, at the cost of vastly increased amount of data. This paper describes an approach to video representation that is based on frame-to-frame alignment, mosaic construction, and 3D parallax recovery. The basic motivation behind our approach is to enable rapid access to the contents, while maintaining the data in a form as close to the source as possible. This representation supports a wide variety of applications that involve transmission, storage, visualization, retrieval, analysis, and manipulation of video sequences.

This paper describes a method for synthesizing images that match the texture appearance of a given digitized sample. This synthesis is completely automatic and requires only the "target" texture as input. It allows generation of as much texture as desired so that any object can be covered. The approach is based on a model of human texture perception, and has potential to be a practically useful tool for image processing and graphics applications.

Article Pyramid-based texture analysis/synthesis Share on Authors: David J. Heeger Stanford University, Department of Psychology, Stanford University, Stanford, CA Stanford University, Department of Psychology, Stanford University, Stanford, CAView Profile , James R. Bergen SRI David Sarnoff Research Center, Princeton, NJ SRI David Sarnoff Research Center, Princeton, NJView Profile Authors Info & Claims SIGGRAPH '95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniquesSeptember 1995 Pages 229–238https://doi.org/10.1145/218380.218446Online:15 September 1995Publication History 520citation4,533DownloadsMetricsTotal Citations520Total Downloads4,533Last 12 Months152Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present a framework for image registration algorithms that finds a lowest-order model of the flow between two images. Low-order models are useful in image registration, because they leave scene structure intact. But in real images complexity varies, and cannot be determined ahead of time. Algorithms in our framework adapt model complexity to image data during a coarse-fine parameter estimation process. Complexity increases keep residual flow small enough that motion can be correctly estimated at each subsequent resolution level. We present one algorithm within this framework which increases complexity by replacing global estimates with estimates over successively smaller patches. We show results of applying this algorithm to the task of mosaicing panoramic aerial images with unknown lens distortion and unknown camera position.>

The efficiency of image search can be greatly improved by using a coarse-to-fine search strategy with a multi-resolution image representa(cid:173) tion. However, if the resolution is so low that the objects have few dis(cid:173) tinguishing features, search becomes difficult. We show that the performance of search at such low resolutions can be improved by using context information, i.e., objects visible at low-resolution which are not the objects of interest but are associated with them. The networks can be given explicit context information as inputs, or they can learn to detect the context objects, in which case the user does not have to be aware of their existence. We also use Integrated Feature Pyramids, which repre(cid:173) sent high-frequency information at low resolutions. The use of multi(cid:173) resolution search techniques allows us to combine information about the appearance of the objects on many scales in an efficient way. A natural fOlm of exemplar selection also arises from these techniques. We illus(cid:173) trate these ideas by training hierarchical systems of neural networks to find clusters of buildings in aerial photographs of farmland.

In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.>

A fundamental assumption made in formulating optical-flow algorithms, that motion at any point in an image can be represented as a single pattern component undergoing a simple translation, fails for a number of situations that commonly occur in real-world images. An alternative formulation of the local motion assumption in which there may be two distinct patterns undergoing coherent (e.g. affine) motion within a given local analysis region is proposed. An algorithm for the analysis of two-component motion in which tracking and nulling mechanisms applied to three consecutive image frames separate and estimate the individual components is given. Precise results are obtained, even for components that differ only slightly in velocity as well as for a faint component in the presence of a dominant, masking component. The algorithm provides precise motion estimates for a set of elementary two-motion configurations and is robust in the presence of noise.>

A fundamental assumption made in formulating optical-flow algorithms is that motion at any point in any image can be represented as a single pattern undergoing a simple translation: even complex motion will appear as a uniform displacement when viewed through a sufficiently small window. This assumption fails in a number of common situations. The authors propose an alternative formulation in which there may be two distinct patterns undergoing coherent motion within a given local analysis region. They then present an algorithm for the analysis of two-component motion. They also demonstrate that the algorithm provides precise motion estimates for a set of elementary two-motion configurations, and show that it is robust in the presence of noise.>

Experiments involving the rapid discrimination of visual patterns are used to infer the spatial information available to an observer within the first few hundred ms of inspection. Eye movements are prevented by a very brief presentation of the stimulus, and the inspection interval is terminated by a presentation of a masking pattern. It is shown that detection of a single vertical target line segment, embedded in an array of differently oriented background segments, improves with the increase of mask delay. The reduction of the area in which the target may lie reduces the inspection time that is required to determine the target's presence or absence. The phenomena are invariant under changes of the spatial scale within the fovea and parafovea. These results are interpreted in the context of a model in which the diameter of the area which can be searched in parallel is proportional to the distance in a feature space between the target and background elements. The geometry of this feature space is similar to the functional architecture of the visual cortex. A theory of texture perception based on a qualitative all-or-none feature space of textons is described. A quantitative model that is shown to be essentially equivalent to the previous theory in the limiting case of very large feature differences is proposed.