Anisotropic diffusion processes in early vision

Summary form only given. Images often contain information at a number of different scales of resolution, so that the definition and generation of a good scale space is a key step in early vision. A scale space in which object boundaries are respected and smoothing only takes place within these boundaries has been defined that avoids the inaccuracies introduced by the usual method of low-pass-filtering the image with Gaussian kernels. The new scale space is generated by solving a nonlinear diffusion differential equation forward in time (the scale parameter). The original image is used as the initial condition, and the conduction coefficient c(x, y, t) varies in space and scale as a function of the gradient of the variable of interest (e.g. the image brightness). The algorithms are based on comparing the local values of different diffusion processes running in parallel on the same image

Deformable kernels for early vision

Early vision algorithms often have a first stage of linear-filtering that `extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce computation and storage costs. A technique is presented that allows: 1) computing the best approximation of a given family using linear combinations of a small number of `basis' functions; and 2) describing all finite-dimensional families, i.e., the families of filters for which a finite dimensional representation is possible with no error. The technique is based on singular value decomposition and may be applied to generating filters in arbitrary dimensions and subject to arbitrary deformations. The relevant functional analysis results are reviewed and precise conditions for the decomposition to be feasible are stated. Experimental results are presented that demonstrate the applicability of the technique to generating multiorientation multi-scale 2D edge-detection kernels. The implementation issues are also discussed

Vision of a Visipedia

The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures first-class citizens of the web, I explore the idea of Visipedia, a visual interface for Wikipedia that is able to answer visual queries and enables experts to contribute and organize visual knowledge. Five distinct groups of humans would interact through Visipedia: users, experts, editors, visual workers, and machine vision scientists. The latter would gradually build automata able to interpret images. I explore some of the technical challenges involved in making Visipedia happen. I argue that Visipedia will likely grow organically, combining state-of-the-art machine vision with human labor

Detecting and localizing edges composed of steps, peaks and roofs

It is well known that the projection of depth or orientation discontinuities in a physical scene results in image intensity edges which are not ideal step edges but are more typically a combination of steps, peak and roof profiles. However most edge detection schemes ignore the composite nature of these edges, resulting in systematic errors in detection and localization. We address the problem of detecting and localizing these edges, while at the same time also solving the problem of false responses in smoothly shaded regions with constant gradient of the image brightness. We show that a class of nonlinear filters, known as quadratic filters, are appropriate for this task, while linear filters are not. A series of performance criteria are derived for characterizing the SNR, localization and multiple responses of these filters in a manner analogous to Canny's criteria for linear filters. A two-dimensional version of the approach is developed which has the property of being able to represent multiple edges at the same location and determine the orientation of each to any desired precision. This permits junctions to be localized without rounding. Experimental results are presented

Birth and Early History of Nonlinear Dynamics in Economics

Desde comienzos de los ’80, la elaboración de modelos no lineales se está volviendo una metodología cada vez más popular en economía. Sin embargo, no es tan novedosa como muchos investigadores parecen creer. Antes de que el enfoque lineal dominara a la teoría económica alrededor de los ’50, muchos economistas se encontraban comprometidos con el desarrollo de modelos no lineales, especialmente durante el período 1930-1950. El principal objetivo de este ensayo es proporcionar una revisión sistemática de los desarrollos pioneros en dinámica no lineal en economía, desde el modelo original de impulso y propagación de Frisch en 1933, hasta la formalización de Goodwin del ciclo límite en 1951. Since the 1980s, nonlinear dynamic modelling is becoming a popular methodology in economics. However, it is not as new as many researchers seem to believe. Before the linear approach dominated economic theory around the 1950s, many economists were actively involved in the development of nonlinear models, this tendency being particularly strong during the period 1930-1950. The main objective of this essay is to offer a systematic and comprehensive survey of the early developments in nonlinear dynamics in economics, ranging form Frisch’s original impulse and propagation model in 1933, to Goodwin’s formalisation of the limit cycle in 1951.modelos no lineales, ciclos económicos, macrodinámica, fluctuaciones endógenas/exógenas, historia del pensamiento 1930-1950, nonlinear modelling, economic cycles, macrodynamics, endogeneous/exogenous fluctuations, history of thought 1930-1950

Measuring and Predicting Importance of Objects in Our Visual World

Associating keywords with images automatically is an approachable and useful goal for visual recognition researchers. Keywords are distinctive and informative objects. We argue that keywords need to be sorted by 'importance', which we define as the probability of being mentioned first by an observer. We propose a method for measuring the `importance' of words using the object labels that multiple human observers give an everyday scene photograph. We model object naming as drawing balls from an urn, and fit this model to estimate `importance'; this combines order and frequency, enabling precise prediction under limited human labeling. We explore the relationship between the importance of an object in a particular image and the area, centrality, and saliency of the corresponding image patches. Furthermore, our data shows that many words are associated with even simple environments, and that few frequently appearing objects are shared across environments

A novel system architecture for real-time low-level vision

A novel system architecture that exploits the spatial locality in memory access that is found in most low-level vision algorithms is presented. A real-time feature selection system is used to exemplify the underlying ideas, and an implementation based on commercially available Field Programmable Gate Arrays (FPGA’s) and synchronous SRAM memory devices is proposed. The peak memory access rate of a system based on this architecture is estimated at 2.88 G-Bytes/s, which represents a four to five times improvement with respect to existing reconfigurable computers

Depth from Brightness of Moving Images

In this note we describe a method for recursively estimating the depth of a scene from a sequence of images. The input to the estimator are brightness values at a number of locations of a grid in a video image, and the output is the relative (scaled) depth corresponding to each image-point. The estimator is invariant with respect to the motion of the viewer, in the sense that the motion parameters are not part of the state of the estimator and therefore the estimates do not depend on motion as long as there is enough parallax (the translational velocity is nonzero). This scheme is a "direct" version of an other algorithm previously presented by the authors for estimating depth from point-feature correspondence independent of motion