Several channels, ranging from TRP receptors to Gap junctions, allow the exchange of small organic solute across cell membrane. However, very little is known about the molecular mechanism of their permeation. Cyclic Nucleotide Gated (CNG) channels, despite their homology with K+ channels and in contrast with them, allow the passage of larger methylated and ethylated ammonium ions like dimethylammonium (DMA) and ethylammonium (EA). We combined electrophysiology and molecular dynamics simulations to examine how DMA interacts with the pore and permeates through it. Due to the presence of hydrophobic groups, DMA enters easily in the channel and, unlike the alkali cations, does not need to cross any barrier. We also show that while the crystal structure is consistent with the presence of a single DMA ion at full occupancy, the channel is able to conduct a sizable current of DMA ions only when two ions are present inside the channel. Moreover, the second DMA ion dramatically changes the free energy landscape, destabilizing the crystallographic binding site and lowering by almost 25 kJ/mol the binding affinity between DMA and the channel. Based on the results of the simulation the experimental electron density maps can be re-interpreted with the presence of a second ion at lower occupancy. In this mechanism the flexibility of the channel plays a key role, extending the classical multi-ion permeation paradigm in which conductance is enhanced by the plain interaction between the ions.

In this paper we present two algorithms for shape recognition. Both algorithms map the contour of the shape to be recognized into a string of symbols. The first algorithm is based on supervised learning using string kernels as often used for text categorization and classification. The second algorithm is very weakly supervised and is based on the procrustes analysis and on the edit distance used for computing the similarity between strings of symbols. The second algorithm correctly recognizes 98.29% of shapes from the MPEG-7 database, i.e. better than any previous algorithms. The second algorithm is able also to retrieve similar shapes from a database

In this paper, a new method for the segmentation of natural images is proposed. Original images g(x, y) are first regularized by using a self-adaptive implementation of the Mumford-Shah functional so that the two parameters alpha and gamma controlling the smoothness and fidelity, automatically adapt to the local scale and contrast of g(x, y). From the regularized image u(x, y) which is piecewise smooth, it is possible to obtain a piecewise constant image sN(x, y) representing a segmentation of the original image g(x, y). Indeed, sN(X, y) is the union of N closed regions, having a constant grey level, preserving thin bars and trihedral junctions present in the original image g(x, y). If the number N of closed regions is too high, closed regions can be merged by minimizing a functional which depends on a parameter n. When n is set equal to 1, a coarse segmentation is obtained with a few tens of distinct regions. With larger values of n, finer segmentations are obtained with about a hundred distinct regions. Therefore, by selecting the value of n it is possible to obtain segmentations at different resolutions. The proposed method for image segmentation was evaluated in two cases where a ground truth segmentation is available. The proposed procedure for image segmentation is rather versatile and depends on only one parameter n and seems suitable for higher level processing, such as categorization, recognition, and scene understanding.

Often an image g(x,y) is regularized and even restored by minimizing the Mumford-Shah functional. Properties of the regularized image u(x,y) depends critically on the numerical value of the two parameters alpha and gamma controlling smoothness and fidelity. When alpha and gamma are constant over the image, small details are lost when an extensive filtering is used in order to remove noise. In this paper, it is shown how the two parameters alpha and gamma can be made self-adaptive. In fact, alpha and gamma are not constant but automatically adapt to the local scale and contrast of features in the image. In this way, edges at all scales are detected and boundaries are well-localized and preserved. In order to preserve trihedral junctions alpha and gamma become locally small and the regularized image u(x,y) maintains sharp and well-defined trihedral junctions. Images regularized by the proposed procedure are well-suited for further processing, such as image segmentation and object recognition.

The present manuscript aims at solving four problems of edge detection: the simultaneous detection of all step edges from a fine to a coarse scale; the detection of thin bars with a width of very few pixels; the detection of trihedral junctions; the development of an algorithm with image-independent parameters. The proposed solution of these problems combines an extensive spatial filtering with classical methods of computer vision and newly developed algorithms. Step edges are computed by extracting local maxima from the energy summed over a large bank of directional odd filters with a different scale. Thin roof edges are computed by considering maxima of the energy summed over narrow odd and even filters along the direction providing maximal response. Junctions are precisely detected and recovered using the output of directional filters. The proposed algorithm has a threshold for the minimum contrast of detected edges: for the large number of tested images this threshold was fixed equal to three times the standard deviation of the noise present in usual acquisition system (estimated to be between 1 and 1.3 gray levels out of 256), therefore, the proposed scheme is in fact parameter free. This scheme for edge detection performs better than the classical Canny edge detector in two quantitative comparisons: the recovery of the original image from the edge map and the structure from motion task. As the Canny detector in previous comparisons was shown to be the best or among the best detectors, the proposed scheme represents a significant improvement over previous approaches.

This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a TV camera mounted on a car moving in usual outdoor scenarios. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.

When a plane undergoes a deformation that can be represented by a planar linear vector field, the projected vector field on the image plane of an optical device is at most quadratic. This 2D motion field has one singular point, with eigenvalues identical to those of the singular point describing the deformation. As a consequence, the nature of the singular point of the deformation is a projective invariant. When the plane moves and experiences a linear deformation at the same time, the associated 2D motion field is still quadratic with at most 3 singular points. In the case of a normal rototranslation, i.e. when the angular velocity is normal to the plane, and of a linear deformation, the 2D motion field has at most one singular point and substantial information on the rigid motion and on the deformation can be recovered from it. Experiments with simulated deformations and real deformable objects show that the proposed analysis can provide accurate results and information on more general 3D deformations.>

The robot moves in a simplified world and performs tasks of increasing complexity, such as the exploration of a maze, the recovery of its structure and the planning of some nearly optimal trajectories through the maze. There are three layers of robot motion control: the lowest layer is essentially an optomotor reflex; the second layer has a short term memory and operates when the visual information is not complete; the third layer has a long term memory and reasoning capabilities. The vision and motor systems are assumed to be finite state automata and the control architecture is based on transitions between states of these automata. The intelligent behavior of the robot is primarily caused by the memory and the capability to create simple and useful representations of the world.>

This paper describes some experiments on control strategies of a mobile robot. The robot has a simple vision system and moves in a simplified world, but the controlling architecture performs tasks of increasing complexity, such as the exploration of a maze, the recovery of its structure and the planning of some nearly optimal trajectories through the maze. There are three layers of control of robot motion: the lowest layer analyses images, controls the speed of robot wheels and is essentially an optomotor reflex; the second layer has a short term memory and operates when the visual information is not complete or when the robot has lost its way; the third layer has a long term memory and reasoning capabilities, monitors the achievement of robot tasks and schedules the timing of completion of the different tasks. The vision and motor systems and other supervising units are assumed to be finite state automata and the control architecture is based on transitions between states of these automata. The control system is flexible and new capabilities are easily acquired by simply introducing new states. The intelligent behaviour of this artificial system is primarily caused by the memory and the capability to create simple and useful representations of the world.>

The authors investigate the computational time complexity of the labeling problem for line drawings of polyhedral scenes. It is found that line drawings can be labeled in time proportional to the number of segments once the vanishing points associated to the possible directions for the edges are known. The vanishing points can be given a priori, otherwise they can in many cases be detected by standard techniques from the line drawing itself. The NP-completeness of the labeling problem for line drawings of trihedral scenes (Kirousis and Papadimitriou, 1988) is then due to the lack of knowledge about the vanishing points, which is equivalent to the knowledge of the possible directions for the edges. These results help draw a more accurate boundary between the problems in the interpretation of line drawings that are polynomially solvable and those that are NP-complete.>

The accuracy and the dependence on parameters of a general scheme for the analysis of time-varying image sequences are discussed. The approach is able to produce vector fields from which it is possible to recover 3-D motion parameters such as time-to-collision and angular velocity. The numerical stability of the computed optical flow and the dependence of the recovery of 3-D motion parameters on spatial and temporal filtering is investigated. By considering optical flows computed on subsampled images or along single scanlines, it is also possible to recover 3-D motion parameters from reduced optical flows. An adequate estimate of time-to-collision can be obtained from sequences of images with spatial resolution reduced to 128*128 pixels or from sequences of single scanlines passing near the focus of expansion. The use of Kalman filtering increases the accuracy and the robustness of the estimation of motion parameters. The proposed approach seems to be able to provide not only a theoretical background but also practical tools that are adequate for the analysis of time-varying image sequences.>

Insects and a lot of other animals use the optical flow to control the direction of their motion and to avoid obstacles. This paper describes experiments suggesting the possible use of the optical flow for the navigation of a robot moving in indoor and outdoor environments. In indoor scenes, such as corridors, offices and laboratories, the optical flow is used to detect and localize obstacles. These routines are based on the computation of a reduced optical flow. Almost real time performance was obtained with standard workstations, such as SUN 3 or SUN Sparcstation 1. The mobile vehicle is usually able to avoid large obstacles such as a chair or a human, but it is not able to avoid thin obstacles such as a rod or a bar. The avoidance performances of the proposed algorithm critically depend on the feedback loop between the vision module and the motor system. In outdoor scenes the optical flow can be used to understand the egomotion, that is to obtain information on the absolute velocity of the moving vehicle. The optical flow is corrected for shocks and vibration present during image acquisition. Regions of the image are extracted, where the optical flow is reliable, and the information on egomotion is recovered from the optical flow here obtained. These results suggest that the optical flow can be successfully used by biological and artificial systems for controlling their motion and for avoiding obstacles.

A general procedure for the understanding of a class of indoor scenes is proposed. The suggested algorithm makes use of an extensive 2-D processing of the image that provides a faithful line drawing of the viewed scene. It also uses procedures adequate for the understanding of the 3-D structure and for the recognition of several items in the scene. The approach is specific for indoor scenes, such as corridors, offices, and laboratory rooms, where long straight edges are predominant and vanishing points can be detected.>

Two aspects of edge detection are analyzed, namely accuracy of localization and sensitivity to noise. The detection of corners and trihedral vertices is analyzed for gradient schemes and zero-crossing schemes. It is shown that neither scheme correctly detects corners of trihedral vertices, but that the gradient schemes are less sensitive to noise. A simple but important conclusion is that the noise present in digital images of typical indoor scenes is small and the signal-to-noise ratio is high. The noise present in digital images is so small as to make the performances of a variety of filters almost indistinguishable. As a consequence small filters can be used and the exact shape of the filter is not critical.>

Mathematical results on ill-posed and ill-conditioned problems are reviewed and the formal aspects of regularization theory in the linear case are introduced. Specific topics in early vision and their regularization are then analyzed rigorously, characterizing existence, uniqueness, and stability of solutions. A fundamental difficulty that arises in almost every vision problem is scale, that is, the resolution at which to operate. Methods that have been proposed to deal with the problem include scale-space techniques that consider the behavior of the result across a continuum of scales. From the point of view of regulation theory, the concept of scale is related quite directly to the regularization parameter lambda . It suggested that methods used to obtained the optimal value of lambda may provide, either directly or after suitable modification, the optimal scale associated with the specific instance of certain problems.>

Edge detection is the process that attempts to characterize the intensity changes in the image in terms of the physical processes that have originated them. A critical, intermediate goal of edge detection is the detection and characterization of significant intensity changes. This paper discusses this part of the edge detection problem. To characterize the types of intensity changes derivatives of different types, and possibly different scales, are needed. Thus, we consider this part of edge detection as a problem in numerical differentiation. We show that numerical differentiation of images is an ill-posed problem in the sense of Hadamard. Differentiation needs to be regularized by a regularizing filtering operation before differentiation. This shows that this part of edge detection consists of two steps, a filtering step and a differentiation step. Following this perspective, the paper discusses in detail the following theoretical aspects of edge detection. 1) The properties of different types of filters-with minimal uncertainty, with a bandpass spectrum, and with limited support-are derived. Minimal uncertainty filters optimize a tradeoff between computational efficiency and regularizing properties. 2) Relationships among several 2-D differential operators are established. In particular, we characterize the relation between the Laplacian and the second directional derivative along the gradient. Zero crossings of the Laplacian are not the only features computed in early vision. 3) Geometrical and topological properties of the zero crossings of differential operators are studied in terms of transversality and Morse theory.

Interaction between nerve cells, mediated by electrical coupling, is a common feature of the nervous system. When such electrical interactions occur within an entire population of similar neurons, the dynamic behavior of each individual cell is influenced by the properties of the whole network. It is shown that when the membrane impedance of such cells can be linearized to a satisfactory approximation and the zeros and poles of the linearized impedance are known, analytical solutions to the network equations can be found that describe the dynamics of the responses of any given cell to one-dimensional and two-dimensional stimuli. This analysis applied to the network of photoreceptors and horizontal cells in the vertebrate retina may be helpful in understanding the initial stages in the processing of visual information.