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Alessandro Sarti

Publications and source records attributed to Alessandro Sarti.

31 records · Page 2Linked to original sources

Neurogeometry of perception: isotropic and anisotropic aspects

In this paper we first recall the definition of geometical model of the visual cortex, focusing in particular on the geometrical properties of horizontal cortical connectivity. Then we recognize that histograms of edges - co-occurrences are not isotropic distributed, and are strongly biased in horizontal and vertical directions of the stimulus. Finally we introduce a new model of non isotropic cortical connectivity modeled on the histogram of edges - co-occurrences. Using this kernel we are able to justify oblique phenomena comparable with experimental findings.

cs.CV↗

A geometric model of multi-scale orientation preference maps via Gabor functions

In this paper we present a new model for the generation of orientation preference maps in the primary visual cortex (V1), considering both orientation and scale features. First we undertake to model the functional architecture of V1 by interpreting it as a principal fiber bundle over the 2-dimensional retinal plane by introducing intrinsic variables orientation and scale. The intrinsic variables constitute a fiber on each point of the retinal plane and the set of receptive profiles of simple cells is located on the fiber. Each receptive profile on the fiber is mathematically interpreted as a rotated Gabor function derived from an uncertainty principle. The visual stimulus is lifted in a 4-dimensional space, characterized by coordinate variables, position, orientation and scale, through a linear filtering of the stimulus with Gabor functions. Orientation preference maps are then obtained by mapping the orientation value found from the lifting of a noise stimulus onto the 2-dimensional retinal plane. This corresponds to a Bargmann transform in the reducible representation of the $\text{SE}(2)=\mathbb{R}^2\times S^1$ group. A comparison will be provided with a previous model based on the Bargman transform in the irreducible representation of the $\text{SE}(2)$ group, outlining that the new model is more physiologically motivated. Then we present simulation results related to the construction of the orientation preference map by using Gabor filters with different scales and compare those results to the relevant neurophysiological findings in the literature.

q-bio.NC↗

Curvature Integration in a 5D Kernel for Extracting Vessel Connections in Retinal Images

Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems for large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. In this paper, we propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a five-dimensional kernel obtained as the fundamental solution of the Fokker-Planck equation modelling the cortical connectivity in the lifted space of positions, orientations, curvatures and intensity. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy and challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques.

cs.CV↗

Local and global gestalt laws: A neurally based spectral approach

A mathematical model of figure-ground articulation is presented, taking into account both local and global gestalt laws. The model is compatible with the functional architecture of the primary visual cortex (V1). Particularly the local gestalt law of good continuity is described by means of suitable connectivity kernels, that are derived from Lie group theory and are neurally implemented in long range connectivity in V1. Different kernels are compatible with the geometric structure of cortical connectivity and they are derived as the fundamental solutions of the Fokker Planck, the Sub-Riemannian Laplacian and the isotropic Laplacian equations. The kernels are used to construct matrices of connectivity among the features present in a visual stimulus. Global gestalt constraints are then introduced in terms of spectral analysis of the connectivity matrix, showing that this processing can be cortically implemented in V1 by mean field neural equations. This analysis performs grouping of local features and individuates perceptual units with the highest saliency. Numerical simulations are performed and results are obtained applying the technique to a number of stimuli.

cs.CV↗

Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering

Retinal images provide early signs of diabetic retinopathy, glaucoma, and hypertension. These signs can be investigated based on microaneurysms or smaller vessels. The diagnostic biomarkers are the change of vessel widths and angles especially at junctions, which are investigated using the vessel segmentation or tracking. Vessel paths may also be interrupted; crossings and bifurcations may be disconnected. This paper addresses a novel contextual method based on the geometry of the primary visual cortex (V1) to study these difficulties. We have analyzed the specific problems at junctions with a connectivity kernel obtained as the fundamental solution of the Fokker-Planck equation, which is usually used to represent the geometrical structure of multi-orientation cortical connectivity. Using the spectral clustering on a large local affinity matrix constructed by both the connectivity kernel and the feature of intensity, the vessels are identified successfully in a hierarchical topology each representing an individual perceptual unit.

cs.CV↗

Sub-Riemannian mean curvature flow for image processing

In this paper we reconsider the sub-Riemannian cortical model of image completion introduced by Sarti and Citti in 2003. This model combines two mechanisms, the sub-Riemannian diffusion and the concentration, giving rise to a diffusion driven motion by curvature. In this paper we give a formal proof of the existence of viscosity solutions of the sub-Riemannian motion by curvature. Results of completion and enhancement on a number of natural images are shown and compared with other models.

math.AP↗

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell level and geometric processing by means of cells connectivity. We present a geometric model of such connectivities in the space of detected features associated to spatio-temporal visual stimuli, and show how they can be used to obtain low-level object segmentation. The main idea is that of defining a spectral clustering procedure with anisotropic affinities over datasets consisting of embeddings of the visual stimuli into higher dimensional spaces. Neural plausibility of the proposed arguments will be discussed.

cs.CV↗

The constitution of visual perceptual units in the functional architecture of V1

Scope of this paper is to consider a mean field neural model which takes into account the functional neurogeometry of the visual cortex modelled as a group of rotations and translations. The model generalizes well known results of Bressloff and Cowan which, in absence of input, accounts for hallucination patterns. The main result of our study consists in showing that in presence of a visual input, the eigenmodes of the linearized operator which become stable represent perceptual units present in the image. The result is strictly related to dimensionality reduction and clustering problems.

cs.CV↗

A Gauge Field Model of Modal Completion

Perceptual completion of figures is a basic process revealing the deep architecture of low level vision. In this paper a complete gauge field Lagrangian is proposed allowing to couple the retinex equation with neurogeometrical models and to solve the problem of modal completion, i.e. the pop up of the Kanizsa triangle. Euler-Lagrange equations are derived by variational calculus and numerically solved. Plausible neurophysiological implementations of the particle and field equations are discussed and a model of the interaction between LGN and visual cortex is proposed.

q-bio.NC↗

A cortical-inspired geometry for contour perception and motion integration

In this paper we develop a geometrical model of functional architecture for the processing of spatio-temporal visual stimuli. The model arises from the properties of the receptive field linear dynamics of orientation and speed-selective cells in the visual cortex, that can be embedded in the definition of a geometry where the connectivity between points is driven by the contact structure of a 5D manifold. Then, we compute the stochastic kernels that are the approximations of two Fokker Planck operators associated to the geometry, and implement them as facilitation patterns within a neural population activity model, in order to reproduce some psychophysiological findings about the perception of contours in motion and trajectories of points found in the literature.

math.AP↗

How Uncertainty bounds the shape index of simple cells

We propose a theoretical motivation to quantify actual physiological features, such as the shape index distributions measured by Jones and Palmer in cats and by Ringach in macaque monkeys. We will adopt the Uncertainty Principle associated to the task of detection of position and orientation as the main tool to provide quantitative bounds on the family of simple cells concretely implemented in primary visual cortex.

q-bio.CB↗

Coherent states of the Euclidean group and activation regions of primary visual cortex

The uncertainty principle of SE(2) allows to construct a coherent states transform that is strictly related to the Bargmann transform for the second Heisenberg group H2. The corresponding target space is characterized constructively and related to the almost complex structure of SE(2) as a contact manifold. Such a coherent state transform provides a model for neural activity maps in the primary visual cortex, that are then described in terms of minimal uncertainty states. The results of the model are compared with the experimental measurements.

math.RT↗

An uncertainty principle underlying the pinwheel structure in the primary visual cortex

The visual information in V1 is processed by an array of modules called orientation preference columns. In some species including humans, orientation columns are radially arranged around singular points like the spokes of a wheel, that are called pinwheels. The pinwheel structure has been observed first with optical imaging techniques and more recently by in vivo two-photon imaging proving their organization with single cell precision. In this research we provide evidence that pinwheels are de facto optimal distributions for coding at the best angular position and momentum. In the last years many authors have recognized that the functional architecture of V1 is locally invariant with respect to the symmetry group of rotations and translations SE(2). In the present study we show that the orientation cortical maps used to construct pinwheels can be modeled as coherent states, i.e. the configurations best localized both in angular position and angular momentum. The theory we adopt is based on the well known uncertainty principle, introduced by Heisenberg in quantum mechanics and later extended to many other groups of invariance. Here we state a corresponding principle in the cortical geometry with SE(2) symmetry, and by computing its minimizers we obtain a model of orientation activity maps in the cortex. As it is well known the pinwheels configuration is directly constructed from these activity maps, and we will be able to formally reproduce their structure starting from the group symmetries of the functional architecture of the visual cortex. The primary visual cortex is then modeled as an integrated system in which the set of simple cells implements the SE(2) group, the horizontal connectivity implements its Lie algebra and the pinwheels implement its minimal uncertainty states.

math-ph↗