A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles.

/pdf/a-volumetric-method-for-building-complex-models-from-range-2cky2diol8.pdf

A volumetric method for building complex models from range images

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

http://math.bu.edu/people/horacio/tutorials/hutyar1.pdf

Resonance, oscillation and the intrinsic frequency preferences of neurons.

We advocate the use of point sets to represent shapes. We provide a definition of a smooth manifold surface from a set of points close to the original surface. The definition is based on local maps from differential geometry, which are approximated by the method of moving least squares (MLS). The computation of points on the surface is local, which results in an out-of-core technique that can handle any point set. We show that the approximation error is bounded and present tools to increase or decrease the density of the points, thus allowing an adjustment of the spacing among the points to control the error. To display the point set surface, we introduce a novel point rendering technique. The idea is to evaluate the local maps according to the image resolution. This results in high quality shading effects and smooth silhouettes at interactive frame rates.

/pdf/computing-and-rendering-point-set-surfaces-sb6ln9ovj9.pdf

Computing and rendering point set surfaces

What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin-Huxley equations and Hopfield model, as well as modern developments in the field such as Generalized Linear Models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.

Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition

The authors present a computer vision technique for the acquisition and processing of 3-D images of the profile of wax dental imprints in the automation of diagnosis in orthodontics. The acquisition of the 3-D images is based on the absorption of light by a dispersive medium and uses standard CCD (charge coupled device) cameras. The profiles of both sides of the imprint are acquired simultaneously. The 3-D image of each side of the imprint is segmented by nonlinear filtering of the 3-D data, and the interstices between the teeth are detected. Two operators are presented: one for the detection of the interstices between the teeth for incisors, canines, and premolars, and one for those between molars. A method for deciding the optimal neighborhood of application of each operator is also presented. Experimental results show that the two operators are very effective at detecting the interstices. >

A computer-vision technique for the acquisition and processing of 3-D profiles of dental imprints: an application in orthodontics

Registering range views of multipart objects

Membrane Current Noise in Lobster Axon under Voltage Clamp

An apparatus for the measurement in realtime of the transfer function in both magnitude and phase of an unknown system including apparatus for generating a pseudo-random binary sequence as a test signal, means to match the timing of this sequence and of the sampling process so that the discrete Fourier transform (DFT) of the test signal and the response signal obtained from the unknown system can be computed, means such that this operation can be repeated successively in precisely the same manner without significant time (phase) jitter, means such that the magnitude function obtained from any single transformation can be computed and displayed, and means such that the difference between the phase functions obtained from two distinct DFT's can be computed and displayed.

Transfer function measurement

Spectral analysis (1–1000 Hz) of spontaneous fluctuations of potential and current in small areas of squid (Loligo pealei) axon shows two forms of noise:f−1 noise occurs in both excitable and inexcitable axons with an intensity which depends upon the driving force for potassium ions. The other noise has a spectral form corresponding to a relaxation process, i.e., its asymptotic behavior at low frequencies is constant, and at high frequencies it declines with a slope of −2. This latter noise occurs only in excitable axons and was identified in spectra by (1) its disappearance after reduction of K+ current by internal perfusion with solutions containing tetraethylammonium (TEA+), Cs+ or reduced [Ki+] and (2) its insensitivity to block of Na+ conduction and active transport. The transition frequency of relaxation spectra are also voltage and temperature dependent and relate to the kinetics of K+ conduction in the Hodgkin-Huxley formulation. These data strongly suggest that the relaxation noise component arises from the kinetic properties of K+ channels. Thef−1 noise is attributed to restricted diffusion in conducting K+ channels and/or leakage pathways. In addition, an induced K+-conduction noise associated with the binding of TEA+ and triethyldecylammonium ion to membrane sites is described. Measurement of the induced noise may provide an alternative means of characterizing the kinetics of interaction of these molecules with the membrane and also suggests that these and other pharmacological agents may not be useful in identifying noise components related to the sodium conduction mechanism which, in these experiments, appears to be much lower in intensity than either the normal K conduction or induced noise components.

Denis Poussart

Papers

A computer-vision technique for the acquisition and processing of 3-D profiles of dental imprints: an application in orthodontics

Registering range views of multipart objects

Membrane Current Noise in Lobster Axon under Voltage Clamp

Transfer function measurement

Potassium-ion conduction noise in squid axon membrane.