penalized: A MATLAB toolbox for fitting generalized linear models with penalties

Yespenalized is a exible, extensible, and e cient MATLAB toolbox for penalized maximum likelihood. penalized allows you to t a generalized linear model (gaussian, logistic, poisson, or multinomial) using any of ten provided penalties, or none. The toolbox can be extended by creating new maximum likelihood models or new penalties. The toolbox also includes routines for cross-validation and plotting

The Canny edge detector revisited

yesCanny (IEEE Trans. Pattern Anal. Image Proc. 8(6):679-698, 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny's work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more accurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph. Models Image Proc. 54:112-133, 1992). In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions

Estimates of edge detection filters in human vision

YesEdge detection is widely believed to be an important early stage in human visual processing. However, there have been relatively few attempts to map human edge detection filters. In this study, observers had to locate a randomly placed step edge in brown noise (the integral of white noise) with a 1/2 power spectrum. Their responses were modelled by assuming the probability the observer chose an edge location depended on the response of their own edge detection filter to that location. The observer’s edge detection filter was then estimated by maximum likelihood methods. The filters obtained were odd-symmetric and similar to a derivative of Gaussian, with a peak-to-trough width of 0.1–0.15 degrees. These filters are compared with previous estimates of edge detectors in humans, and with neurophysiological receptive fields and theoretical edge detectors

Evidence for chromatic edge detectors in human vision using classification images

YesEdge detection plays an important role in human vision, and although it is clear that there are luminance edge detectors, it is not known whether there are chromatic edge detectors as well.We showed observers a horizontal edge blurred by a Gaussian filter (with widths of r ¼ 0.1125, 0.225, or 0.458) embedded in blurred Brown noise. Observers had to choose which of two stimuli contained the edge. Brown noise was used in preference to white noise to reveal localized edge detectors. Edges and noise were defined by either luminance or chromatic contrast (isoluminant L/M and S-cone opponent). Classification image analysis was applied to observer responses. In this analysis, the random components of the stimulus are correlated with observer responses to reveal a template that shows how observers weighted different parts of the stimulus to arrive at their decision.We found classification images for both luminance and isoluminant chromatic stimuli that had shapes very similar to derivatives of Gaussian filters. The widths of these classification images tracked the widths of the edges, but the chromatic edge classification images were wider than the luminance ones. These results are consistent with edge detection filters sensitive to luminance contrast and isoluminant chromatic contrast.Royal Society Travel Grant IE130877 and in part by Canadian Institutes of Health Research (CIHR) grant MOP-1081

Serial correlations and 1/f power spectra in visual search reaction times

In a visual search experiment, the subject must find a target item hidden in a display of other items, and their performance is measured by their reaction time (RT). Here I look at how visual search reaction times are correlated with past reaction times. Target-absent RTs (i.e. RTs to displays that have no target) are strongly correlated with past target-absent RTs and, treated as a time series, have a 1/f power spectrum. Target-present RTs, on the other hand, are effectively uncorrelated with past RTs. A model for visual search is presented which generates search RTs with this pattern of correlations and power spectra. In the model, search is conducted by matching search items up with "categorizers," which take a certain time to categorize each item as target or distractor; the RT is the sum of categorization times. The categorizers are drawn at random from a pool of active categorizers. After each search, some of the categorizers in the active pool are replaced with categorizers drawn from a larger population of unused categorizers. The categorizers that are not replaced are responsible for the RT correlations and the 1/f power spectrum

Classification images for contrast discrimination

YesContrast discrimination measures the smallest difference in contrast (the threshold) needed to successfully tell two stimuli apart. The contrast discrimination threshold typically increases with contrast. However, for low spatial frequency gratings the contrast threshold first increases, but then starts to decrease at contrasts above about 50%. This behaviour was originally observed in contrast discrimination experiments using dark spots as stimuli, suggesting that the contrast discrimination threshold for low spatial frequency gratings may be dominated by responses to the dark parts of the sinusoid. This study measures classification images for contrast discrimination experiments using a 1 cycle per degree sinusoidal grating at contrasts of 0, 25%, 50% and 75%. The classification images obtained clearly show that observers emphasize the darker parts of the sinusoidal grating (i.e. the troughs), and this emphasis increases with contrast. At 75% contrast, observers almost completely ignored the bright parts (peaks) of the sinusoid, and for some observers the emphasis on the troughs is already evident at contrasts as low as 25%. Analysis using a Hammerstein model suggests that the bias towards the dark parts of the stimulus is due to an early nonlinearity, perhaps similar to that proposed by Whittle

A clustering model for item selection in visual search

In visual search experiments, the subject looks for a target item in a display containing different distractor items. The reaction time (RT) to find the target is measured as a function of the number of distractors (set size). RT is either constant, or increases linearly, with set size. Here we suggest a two-stage model for search in which items are first selected and then recognized. The selection process is modeled by (a) grouping items into a hierarchical cluster tree, in which each cluster node contains a list of all the features of items in the cluster, called the object file, and (b) recursively searching the tree by comparing target features to the cluster object file to quickly determine whether the cluster could contain the target. This model is able to account for both constant and linear RT versus set size functions. In addition, it provides a simple and accurate account of conjunction searches (e.g., looking for a red N among red Os and green Ns), in particular the variation in search rate as the distractor ratio is varied