Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

Peripheral vision and pattern recognition : a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Peripheral vision and pattern recognition:a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Objectives: Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data. Methods: We used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers. Results: The accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03). Conclusions: These results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

Live 3D shape reconstruction, recognition and registration

We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.

Neural networks: a pattern recognition perspective

The majority of current applications of neural networks are concerned with problems in pattern recognition. In this article we show how neural networks can be placed on a principled, statistical foundation, and we discuss some of the practical benefits which this brings.

Neural networks: a pattern recognition perspective

The majority of current applications of neural networks are concerned with problems in pattern recognition. In this article we show how neural networks can be placed on a principled, statistical foundation, and we discuss some of the practical benefits which this brings.

Category learning induces position invariance of pattern recognition across the visual field

Human object recognition is considered to be largely invariant to translation across the visual field. However, the origin of this invariance to positional changes has remained elusive, since numerous studies found that the ability to discriminate between visual patterns develops in a largely location-specific manner, with only a limited transfer to novel visual field positions. In order to reconcile these contradicting observations, we traced the acquisition of categories of unfamiliar grey-level patterns within an interleaved learning and testing paradigm that involved either the same or different retinal locations. Our results show that position invariance is an emergent property of category learning. Pattern categories acquired over several hours at a fixed location in either the peripheral or central visual field gradually become accessible at new locations without any position-specific feedback. Furthermore, categories of novel patterns presented in the left hemifield are distinctly faster learnt and better generalized to other locations than those learnt in the right hemifield. Our results suggest that during learning initially position-specific representations of categories based on spatial pattern structure become encoded in a relational, position-invariant format. Such representational shifts may provide a generic mechanism to achieve perceptual invariance in object recognition.

Edge coding in human vision: a psychophysical and computational investigation

This thesis presents a study of how edges are detected and encoded by the human visual system. The study begins with theoretical work on the development of a model of edge processing, and includes psychophysical experiments on humans, and computer simulations of these experiments, using the model. The first chapter reviews the literature on edge processing in biological and machine vision, and introduces the mathematical foundations of this area of research. The second chapter gives a formal presentation of a model of edge perception that detects edges and characterizes their blur, contrast and orientation, using Gaussian derivative templates. This model has previously been shown to accurately predict human performance in blur matching tasks with several different types of edge profile. The model provides veridical estimates of the blur and contrast of edges that have a Gaussian integral profile. Since blur and contrast are independent parameters of Gaussian edges, the model predicts that varying one parameter should not affect perception of the other. Psychophysical experiments showed that this prediction is incorrect: reducing the contrast makes an edge look sharper; increasing the blur reduces the perceived contrast. Both of these effects can be explained by introducing a smoothed threshold to one of the processing stages of the model. It is shown that, with this modification,the model can predict the perceived contrast and blur of a number of edge profiles that differ markedly from the ideal Gaussian edge profiles on which the templates are based. With only a few exceptions, the results from all the experiments on blur and contrast perception can be explained reasonably well using one set of parameters for each subject. In the few cases where the model fails, possible extensions to the model are discussed.

Spectral pattern, grouping, and the pitches of complex tones and their components

Harmonically related components are typically heard as a unified entity with a rich timbre and a pitch corresponding to the fundamental frequency. Mistuning a component generally has four consequences: (i) the global pitch of the complex shifts in the same direction as the mistuning; (ii) the component makes a reduced contribution to global pitch; (iii) the component is heard out as a separate sound with a pure timbre; (iv) its pitch differs from that of a pure tone of equal frequency in a small but systematic way. Local interactions between neighbouring components cannot explain these effects; instead they are usually explained in terms of the global operation of a single harmonic-template mechanism. However, several observations indicate that separate mechanisms govern the selection of spectral components for perceptual fusion and for the computation of global pitch. First, an increase in mistuning causes a harmonic to be heard out before it begins to be excluded from the computation of global pitch. Second, a single even harmonic added to an odd-harmonic complex is typically more salient than its odd neighbours. Third, the mistuning of a component in frequency-shifted stimuli, or stimuli with a moderate spectral stretch, results in changes in salience and component pitch like those seen for harmonic stimuli. Fourth, the global pitch of frequency-shifted stimuli is predicted well by the weighted fit of a harmonic template, but, with the exception of the lowest component, the fusion of individual partials for shifted stimuli is best predicted by the common pattern of spectral spacing. Fifth, our sensitivity to spectral pattern is surprisingly resistant to random variations in component spacing induced by applying mistunings to several harmonics at once. These findings are evaluated in the context of an autocorrelogram model of the proposed pitch/grouping dissociation. © S. Hirzel Verlag · EAA.

Facial emotion recognition and alexithymia in adults with somatoform disorders

The primary aim of this study was to investigate facial emotion recognition (FER) in patients with somatoform disorders (SFD). Also of interest was the extent to which concurrent alexithymia contributed to any changes in emotion recognition accuracy. Twenty patients with SFD and 20 healthy, age, sex and education matched, controls were assessed with the Facially Expressed Emotion Labelling Test of FER and the 26-item Toronto Alexithymia Scale. Patients withSFD exhibited elevated alexithymia symptoms relative to healthy controls.Patients with SFD also recognized significantly fewer emotional expressions than did the healthy controls. However, the group difference in emotion recognition accuracy became nonsignificant once the influence of alexithymia was controlled for statistically. This suggests that the deficit in FER observed in the patients with SFD was most likely a consequence of concurrent alexithymia. It should be noted that neither depression nor anxiety was significantly related to emotion recognition accuracy, suggesting that these variables did not contribute the emotion recognition deficit. Impaired FER observed in the patients with SFD could plausibly have a negative influence on these individuals’ social functioning.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Topography of visual evoked magnetic responses to pattern shift, pattern onset and flash stimuli

Different visual stimuli may activate separate channels in the visual system and produce magnetic responses from the human bran which originate from distinct regions of the visual cortex. To test this hypothesis, we have investigated the distribution of visual evoked magnetic responses to three distinct visual stimuli over the occipital region of the scalp with a DC-SQUID second-order gradiometer in an ubshielded environment. Patterned stimuli were presented full field and to the right half field, while a flash stimulus was presented full field only, in five normal subjects. Magnetic responses were recorded from 20 to 42 positions over the occipital scalp. Topographic maps were prepared of the major positive component within the first 150ms to the three stimuli, i.e., the P100m (pattern shift), C11m (pattern onset) and P2m (flash). For the pattern shift stimulus the data suggested the source of the P100m was close to the midline with the current directed towards the medial surface. The data for the pattern onset C11m suggested a source at a similar depth but with the current directed away from the midline towards the lateral surface. The flash P2m appeared to originate closer to the surface of the occipital pole than both the patterned stimuli. Hence the pattern shift (which may represent movement), and the pattern onset C11m (representing contrast and contour) appear to originate in similar areas of brain but to represent different asepcts of cortical processing. By contrast, the flash P2m (representing luminance change) appears to originate in a distinct area of visual cortex closer to the occipital pole.