Felice Andrea Pellegrino

Edge detection revisited

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.

Automatic visual recognition of deformable objects for grasping and manipulation

This paper describes a vision-based system that is able to automatically recognize deformable objects, to estimate their pose, and to select suitable picking points. A hierarchical self-organized neural network is used to segment color images based on texture information. A morphological analysis allows the recognition of the objects and the picking points extraction. The proposed approach is useful in all of the situations where texture properties are significant for detecting regions of interest on deformable objects. Several tests on a large number of images, acquired in real operative working conditions, demonstrate the effectiveness of the system.

Self-adaptive regularization

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 /spl alpha/ and /spl beta/ controlling smoothness and fidelity. When /spl alpha/ and /spl beta/ 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 /spl alpha/ and /spl beta/ can be made self-adaptive. In fact, /spl alpha/ and /spl beta/ 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 /spl alpha/ and /spl beta/ 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.

Enhancing Controller Performance for Robot Positioning in a Constrained Environment

The paper considers a novel technique for manipulator motion in a constrained environment due to the presence of obstacles. The basic problem is that of avoiding collisions of the manipulator with the obstacles. The main idea is to cover the free space (i.e. the points of the configurations space in which no collisions are possible) by a connected family of polyhedral sets which are controlled-invariant. Each of these polyhedral regions includes some crossing points to the confining regions. The tracking control is hierarchically structured. A high-level controller establishes a connected chain of regions to be crossed to reach the one in which the reference is included. A low-level control solves the problem of tracking, within a region, the crossing point to the next confining region and, eventually, tracking the reference whenever it is included in the current one. The scheme assures that the reference is asymptotically tracked and that the transient trajectory is completely included in the admissible configuration space. A connection graph associated with the cluster of regions, and the high-level control is achieved by solving a minimum-path problem. As far as the low-level control is concerned, we consider both speed-control and torque-control. We propose two types of controllers. The first type is based on a linear stabilizing feedback which is suitably adapted to achieve a local tracking controller. Such a controller is computed by the plane representation of the sets which is more natural and useful then the vertex representation considered in previous work. The second is a speed-saturated type of controller which considerably improves the performance of linear-based control laws. Both these controllers have a speed-control and torque-control version. Experimental results on a laboratory Cartesian robot are provided.

Approximate model predictive control laws for constrained nonlinear discrete-time systems: analysis and offline design

The objective of this work consists in the offline approximation of possibly discontinuous model predictive control laws for nonlinear discrete-time systems, while enforcing hard constraints on state and input variables. Obtaining an offline approximation of the receding horizon control law may lead to a very significant reduction of the online computational burden with respect to algorithms based on iterated optimization, thus allowing the application to fast dynamics plants. The proposed approximation scheme allows to cope with discontinuous control laws, such as those arising from constrained nonlinear finite horizon optimal control problems. A detailed stability analysis of the closed-loop system driven by the approximated state-feedback controller shows that the devised technique guarantees the input-to-state practical stability with respect to the (non-fading) approximation-induced errors. Two examples are provided to show the effectiveness of the method when the approximator is chosen either as a discontinuous nearest point function or as a smooth neural network.

Relatively Optimal Control: A Static Piecewise-Affine Solution

A relatively optimal control is a stabilizing controller that, without initialization nor feedforwarding and tracking the optimal trajectory, produces the optimal (constrained) behavior for the nominal initial condition of the plant. In a previous work, for discrete-time linear systems, we presented a linear dynamic relatively optimal control. Here we provide a static solution, namely a deadbeat piecewise-affine state-feedback controller based on a suitable partition of the state space into polyhedral sets. The vertices of the polyhedra are the states of the optimal trajectory; hence a bound for the complexity of the controller is known in advance. We also show how to obtain a controller that is not deadbeat by removing the zero terminal constraint while guaranteeing stability. Finally, we compare the proposed static compensator with the dynamic one.

HAB Buoy: a new instrument for in situ monitoring and early warning of harmful algal bloom events

A new microplankton imaging and analysis instrument, HAB Buoy, is described. It integrates a high-speed camera for in-flow image acquisition with automatic specimen labelling software, known as DiCANN (Dinoflagellate Categorisation by Artificial Neural Network). Some preliminary results are presented together with a rationale for its use.

Relatively optimal control with characteristic polynomial assignment and output feedback

A relatively optimal control is a stabilizing controller such that, if initialized at its zero state, produces the optimal (constrained) behavior for the nominal initial condition of the plant (without feedforwarding and tracking the optimal trajectory). In this paper, we prove that a relatively optimal control can be obtained under quite general constraints and objective function, in particular without imposing 0-terminal constraints as previously done. The main result is that stability of the closed-loop system can be achieved by assigning an arbitrary closed-loop characteristic stable polynomial to the plant. An explicit solution is provided. We also show how to choose the characteristic polynomial in such a way that the constraints (which are enforced on a finite horizon) can be globally or ultimately satisfied (i.e., satisfied from a certain time on). We provide conditions to achieve strong stabilization (stabilization by means of a stable compensator) precisely, we show how to assign both compensator and closed-loop poles. We consider the output feedback problem, and we show that it can be successfully solved by means of a proper observer initialization (based on output measurements only). We discuss several applications of the technique and provide experimental results on a cart-pendulum system.

Image processing issues in a social assistive system for the blind

We systematically analyse the design of the low-level vision components of a real-time system able to help a blind person in his/her social interactions. We focus on the acquisition and processing of the video sequences that are acquired by a wearable sensor (a smartphone camera or a Webcam) for the detection of faces in the scene. We review some classical and some very recent techniques that seem appropriate to the requirements of our goal.

Towards More Natural Social Interactions of Visually Impaired Persons

We review recent computer vision techniques with reference to the specific goal of assisting the social interactions of a person affected by very severe visual impairment or by total blindness. We consider a scenario in which a sequence of images is acquired and processed by a wearable device, and we focus on the basic tasks of detecting and recognizing people and their facial expression. We review some methodologies of Visual Domain Adaptation that could be employed to adapt existing classification strategies to the specific scenario. We also consider other sources of information that could be exploited to improve the performance of the system.