Recognition by directed attention to recursively partitioned images

A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth

Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla

Yhtäaikainen paikannus ja puuston kartoitus 2D- ja 3D- laserskannereilla esittää tavan mitata ja kartoittaa metsän puut. Työssä mittaukset tehtiin paikallisesti metsässä käyttäen kaksi ja kolmiulotteisia laserskannereita liikkeestä mitaten. Työ esittää menetelmän puumaiset kohteiden tunnistamiseen mittalaitteiden tuottamasta pisteparvesta reaaliajassa. Työssä mittaus ja kartoitusalgoritmit sovitetaan erityistesti metsän kartoitusta ja puuston mittausta varten. Diplomityössä yhtäaikaisen paikoituksen ja kartoituksen ongelmaa lähdetään ratkaisemaan mittauslaitteen mahdollisimman tarkan paikan ja asennon estimoinnista metsäolosuhteissa. Mittauslaitteiston paikkaa mitataan laserodometrialla, jossa perättäisiä laserkeilauksia verrataan toisiinsa ja näiden väliltä tunnistetaan liike suhteessa ympäristöön. Työssä kehitetään uusia heuristisia paikannusmetodeja metsäympäristöön. Työ esittelee uuden tavan käyttää ristikorrelaatioita laserodometriassa ja näyttää miten gyro- ja kiihtyvyysantureita voidaan käyttää odometriatiedon parantamiseen. Diplomityö esittää piirrepohjaisen puukartan, jonne 2D- ja 3D-laseretäisyysmittauksista lasketut piirteet lisätään. Karttaa päivitetään jatkuvasti ja uusinta kartan tietoa käytetään mitatun paikkatiedon parantamiseen. Samoin kaikkia kerättyjä mittauksia käytetään tilastollisesti parantamaan aikaisemmin eri korkeuksilta laskettuja puurunkojen läpimittaestimaatteja. Lopputuloksena saatu kartta on melko tarkka. Kartoituksessa puun läpimitan tarkkuus on muutamia senttimetrejä ja puun paikan tarkkuus muuta kymmenen senttimetriä. Mittauslaitteen paikan arvioidaan olevan tarkempi kuin puiden, koska suurta määrää kartoitettuja puita käytetään mittauslaitteen paikan ja asennon sovittamiseen. Suhteellinen kartta on kiinnitetty globaaliin koordinaatistoon GPS mittalaitteen avulla

Multiresolutional Fault-Tolerant Sensor Integration and Object Recognition in Images.

This dissertation applies multiresolution methods to two important problems in signal analysis. The problem of fault-tolerant sensor integration in distributed sensor networks is addressed, and an efficient multiresolutional algorithm for estimating the sensors\u27 effective output is proposed. The problem of object/shape recognition in images is addressed in a multiresolutional setting using pyramidal decomposition of images with respect to an orthonormal wavelet basis. A new approach to efficient template matching to detect objects using computational geometric methods is put forward. An efficient paradigm for object recognition is described

The Three-Dimensional Circumstellar Environment of SN 1987A

We present the detailed construction and analysis of the most complete map to date of the circumstellar environment around SN 1987A, using ground and space-based imaging from the past 16 years. PSF-matched difference-imaging analyses of data from 1988 through 1997 reveal material between 1 and 28 ly from the SN. Careful analyses allows the reconstruction of the probable circumstellar environment, revealing a richly-structured bipolar nebula. An outer, double-lobed ``Peanut,'' which is believed to be the contact discontinuity between red supergiant and main sequence winds, is a prolate shell extending 28 ly along the poles and 11 ly near the equator. Napoleon's Hat, previously believed to be an independent structure, is the waist of this Peanut, which is pinched to a radius of 6 ly. Interior to this is a cylindrical hourglass, 1 ly in radius and 4 ly long, which connects to the Peanut by a thick equatorial disk. The nebulae are inclined 41\degr south and 8\degr east of the line of sight, slightly elliptical in cross section, and marginally offset west of the SN. From the hourglass to the large, bipolar lobes, echo fluxes suggest that the gas density drops from 1--3 cm^{-3} to >0.03 cm^{-3}, while the maximum dust-grain size increases from ~0.2 micron to 2 micron, and the Si:C dust ratio decreases. The nebulae have a total mass of ~1.7 Msun. The geometry of the three rings is studied, suggesting the northern and southern rings are located 1.3 and 1.0 ly from the SN, while the equatorial ring is elliptical (b/a < 0.98), and spatially offset in the same direction as the hourglass.Comment: Accepted for publication in the ApJ Supplements. 38 pages in apjemulate format, with 52 figure

Representation Learning in Sensory Cortex: a theory

We review and apply a computational theory of the feedforward path of the ventral stream in visual cortex based on the hypothesis that its main function is the encoding of invariant representations of images. A key justification of the theory is provided by a theorem linking invariant representations to small sample complexity for recognition – that is, invariant representations allows learning from very few labeled examples. The theory characterizes how an algorithm that can be implemented by a set of ”simple” and ”complex” cells – a ”HW module” – provides invariant and selective representations. The invariance can be learned in an unsupervised way from observed transformations. Theorems show that invariance implies several properties of the ventral stream organization, including the eccentricity dependent lattice of units in the retina and in V1, and the tuning of its neurons. The theory requires two stages of processing: the first, consisting of retinotopic visual areas such as V1, V2 and V4 with generic neuronal tuning, leads to representations that are invariant to translation and scaling; the second, consisting of modules in IT, with class- and object-specific tuning, provides a representation for recognition with approximate invariance to class specific transformations, such as pose (of a body, of a face) and expression. In the theory the ventral stream main function is the unsupervised learning of ”good” representations that reduce the sample complexity of the final supervised learning stage.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Wavelet-Based Registration of Medical Images.

Registration is the process of spatially aligning two objects and is normally a preprocessing step in most object recognition algorithms. Registration of images and recognition of signatures of objects in images is important for clinical and diagnostic purposes in medicine. Recognizing structure, potential targets for defense purposes and changes in the terrain, from aerial surveillance images and SAR images is the focus of extensive research and development today. Automatic Target Recognition is becoming increasingly important as the defense systems and armament technology move to use smarter munitions. Registration of images is a preprocessing step in any kind of machine vision for robots, object recognition in general, etc. Registration is also important for tuning instruments dealing with images. Most of the available methods of registration today are operator assisted. The state of registration today is more art than science and there are no standards for measuring or validating registration procedures. This dissertation provides a viable method to automatically register images of rigid bodies. It provides a method to register CT and MRI images of the brain. It uses wavelets to determine sharp edges. Wavelets are oscillatory functions with compact support. The Wavelet Modulus Maxima singularides. It also provides a mechanism to characterize the singularities in the images using Lipschitz exponents. This research provides a procedure to register images which is computationally efficient. The algorithms and techniques are general enough to be applicable to other application domains. The discussion in this dissertation includes an introduction to wavelets and time frequency analysis, results on MRI data, a discussion on the limitations, and certain requirements for the procedure to work. This dissertation also tracks the movement of edges across scales when a wavelet algorithm is used and provides a formula for this edge movement. As part of this research a registration classification schematic was developed

Real-Time Markerless Tracking the Human Hands for 3D Interaction

This thesis presents methods for enabling suitable human computer interaction using only movements of the bare human hands in free space. This kind of interaction is natural and intuitive, particularly because actions familiar to our everyday life can be reflected. Furthermore, the input is contact-free which is of great advantage e.g. in medical applications due to hygiene factors. For enabling the translation of hand movements to control signals an automatic method for tracking the pose and/or posture of the hand is needed. In this context the simultaneous recognition of both hands is desirable to allow for more natural input. The first contribution of this thesis is a novel video-based method for real-time detection of the positions and orientations of both bare human hands in four different predefined postures, respectively. Based on such a system novel interaction interfaces can be developed. However, the design of such interfaces is a non-trivial task. Additionally, the development of novel interaction techniques is often mandatory in order to enable the design of efficient and easily operable interfaces. To this end, several novel interaction techniques are presented and investigated in this thesis, which solve existing problems and substantially improve the applicability of such a new device. These techniques are not restricted to this input instrument and can also be employed to improve the handling of other interaction devices. Finally, several new interaction interfaces are described and analyzed to demonstrate possible applications in specific interaction scenarios.Markerlose Verfolgung der menschlichen Hände in Echtzeit für 3D Interaktion In der vorliegenden Arbeit werden Verfahren dargestellt, die sinnvolle Mensch- Maschine-Interaktionen nur durch Bewegungen der bloßen Hände in freiem Raum ermöglichen. Solche "natürlichen" Interaktionen haben den besonderen Vorteil, dass alltägliche und vertraute Handlungen in die virtuelle Umgebung übertragen werden können. Außerdem werden auf diese Art berührungslose Eingaben ermöglicht, nützlich z.B. wegen hygienischer Aspekte im medizinischen Bereich. Um Handbewegungen in Steuersignale umsetzen zu können, ist zunächst ein automatisches Verfahren zur Erkennung der Lage und/oder der Art der mit der Hand gebildeten Geste notwendig. Dabei ist die gleichzeitige Erfassung beider Hände wünschenswert, um die Eingaben möglichst natürlich gestalten zu können. Der erste Beitrag dieser Arbeit besteht aus einer neuen videobasierten Methode zur unmittelbaren Erkennung der Positionen und Orientierungen beider Hände in jeweils vier verschiedenen, vordefinierten Gesten. Basierend auf einem solchen Verfahren können neuartige Interaktionsschnittstellen entwickelt werden. Allerdings ist die Ausgestaltung solcher Schnittstellen keinesfalls trivial. Im Gegenteil ist bei einer neuen Art der Interaktion meist sogar die Entwicklung neuer Interaktionstechniken erforderlich, damit überhaupt effiziente und gut bedienbare Schnittstellen konzipiert werden können. Aus diesem Grund wurden in dieser Arbeit einige neue Interaktionstechniken entwickelt und untersucht, die vorhandene Probleme beheben und die Anwendbarkeit eines solchen Eingabeinstruments für bestimmte Arten der Interaktion verbessern oder überhaupt erst ermöglichen. Diese Techniken sind nicht auf dieses Eingabeinstrument beschränkt und können durchaus auch die Handhabung anderer Eingabegeräte verbessern. Des Weiteren werden mehrere neue Interaktionsschnittstellen präsentiert, die den möglichen Einsatz bloßhändiger Interaktion in verschiedenen, typischen Anwendungsgebieten veranschaulichen

A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

We describe a quantitative theory to account for the computations performed by the feedforward path of the ventral stream of visual cortex and the local circuits implementing them. We show that a model instantiating the theory is capable of performing recognition on datasets of complex images at the level of human observers in rapid categorization tasks. We also show that the theory is consistent with (and in some case has predicted) several properties of neurons in V1, V4, IT and PFC. The theory seems sufficiently comprehensive, detailed and satisfactory to represent an interesting challenge for physiologists and modelers: either disprove its basic features or propose alternative theories of equivalent scope. The theory suggests a number of open questions for visual physiology and psychophysics

Depth Sensing Planar Structures: Detection of Office Furniture Configurations

Handheld devices with depth sensors have the potential to aid low-vision users in performing tasks that are difficult with traditional modes of assistance. Heuristic studies have revealed that tables have a key functional role in indoor scene descriptions. The research question addressed in this thesis is: how can we robustly and efficiently detect tables in indoor office environments? This thesis presents a solution that utilizes a functional approach to robustly detect rectangular tables in depth images generated from a Kinect sensor. Perhaps the most significant function of a table is to provide its users with a supporting plane. This demands that the table’s surface is orthogonal to the scene’s gravity vector. In order to fully take advantage of this functional property in the detection process, the scene must be properly oriented. A planar model fitting procedure is used to detect the scene’s floor, which is utilized to properly orient the scene. The scene is then sliced at average table height, using a small buffer. The height component is removed from the 3-dimensional slice by projecting it into a two-dimensional plane. Next, an iterative labeling procedure is used to separate the image into independent blobs, allowing for 2-dimensional shape detection. Sufficiently large blobs are then subjected to a cleaning process in order to remove any extraneous features. Several features of the cleaned blobs are calculated and used in a supervised classification process. The coordinates of blobs that are classified as tables are translated back to 3-dimensions, allowing for the segmentation of all detected tables in the scene

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics