Optical flow is a measure of the change of image brightness in a frame sequence and is commonly used as an approximation of the velocity field, which is the perspective projection of the three-dimensional (3D) real ve...
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Optical flow is a measure of the change of image brightness in a frame sequence and is commonly used as an approximation of the velocity field, which is the perspective projection of the three-dimensional (3D) real velocity on the image plane. One method for evaluating optical flow is based on the Optical Flow Constraint (OFC) equation. Such an equation is associated with each image pixel. Under the assumption that in the immediate neighbourhood of a pixel the optical flow field is smooth, the constraint equations in that neighbourhood should have a common solution. Least-squares techniques are commonly used to derive the solution, which typically is very sensitive to discontinuities. A new method of evaluating the optical flow from the OFC equation is presented, which performs well in the presence of discontinuities. The method proposes to accumulate evidence from the solution of all pairs of constraints in each image segment to provide the most probable value for optical flow, rather than adopting least-squares techniques. The proposed accumulation of evidence is performed using a variant of the Hough transform, the Combinatorial Hough transform. Experimental results under different types of motion are presented. An efficient implementation of the algorithm is discussed on a highly parallel architecture.
Projection of the 3D velocity of real objects on the image plane is often called the 'velocity field'. The estimation of this field is one of the most important research topics in computer vision. In the liter...
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Projection of the 3D velocity of real objects on the image plane is often called the 'velocity field'. The estimation of this field is one of the most important research topics in computer vision. In the literature, there are numerous solutions which adopt a sort of continuity equation called optical flow constrain (OFC). The solution of this constraint equation is usually called the 'optical flow' field, and can be considered equal to the velocity field under particular assumptions. The structure of the OFC equation makes the optical flow estimation an ill-posed problem, like many other inverse problems in early-vision. For this reason, many regularization techniques were used in the past for estimating optical flow. The major drawback of these solutions is the presence of propagation effects which produce the loss of the information associated to the discontinuities. On the other hand, the dicontinuities are very important for estimating precise optical flow fields, and detecting the shape of moving objects. In this paper, we propose a new solution based on variational techniques for optical flow estimation and regularization, which takes into account the discontinuities, and strongly reduces the related problems. The proposed method is called 'discontinuity-dependent variational solution'.
The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation make the problem ill-posed so in the past solut...
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This contribution is a work-in-progress which aims at presenting, in a unified framework, a set of conceptual models devoted to state and solve in an integrated way several decision problems in a manufacturing environ...
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This contribution is a work-in-progress which aims at presenting, in a unified framework, a set of conceptual models devoted to state and solve in an integrated way several decision problems in a manufacturing environment. Each model has to be considered such as the formal statement on which several industrial application and theoretical developments, reported in the enclosed list of references, are based.
The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation make the problem ill-posed so in the past solut...
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The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation make the problem ill-posed so in the past solutions have been proposed some solutions based on regularization. On the contrary, under the assumption that in the immediate neighborhood of a pixel the optical flow field is smooth, the constraint equations in that neighborhood should have a common solution, in this case the problem is not ill-posed. Following this reasoning, an algorithm for evaluating the optical flow, which is suitable for parallel implementations of selected algorithms from the literature, for optical flow estimation, are presented with the intention of comparing their complexity and performance with respect to the proposed approach on a Connection Machine-2.
The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation makes the problem ill-posed so in the past some...
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The gradient-based methods for optical flow estimation are based on a constraint equation which is defined for each image pixel. The structure of the constraint equation makes the problem ill-posed so in the past some solutions have been proposed based on regularization. Under the assumption that in the immediate neighbourhood of a pixel the optical flow field is smooth, the constraint equations in that neighbourhood should have a common solution, in this case the problem is not ill-posed. Following this reasoning, an algorithm for evaluating the optical flow, which is suitable for parallel implementation is proposed in this paper. Moreover, parallel implementations of selected algorithms from the literature, for optical flow estimation, are presented in this paper with the intention of comparing their complexity and performance with respect to the proposed approach on a Connection Machine-2.< >
We (1993) have developed a neural network architecture which learns a forward model of a redundant manipulator (via self-supervised training) as a map of normalized radial basis neurons and inverts the model by means ...
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We (1993) have developed a neural network architecture which learns a forward model of a redundant manipulator (via self-supervised training) as a map of normalized radial basis neurons and inverts the model by means of run-time gradient descent of a task-related potential field. In this paper, we propose a distributed model for the computation of the field, which is consistent with the model-inversion map; and we discuss the problem of self-synchronization between the gradient-descent process and a process for the generation of virtual trajectories of the end-effector.
Impedance control is one of the most effective control methods for the manipulators in contact with their environments. The characteristic of force and motion control, however, is influenced by a desired impedance of ...
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Impedance control is one of the most effective control methods for the manipulators in contact with their environments. The characteristic of force and motion control, however, is influenced by a desired impedance of a manipulator's end-effector, which must be designed according to a given task and an environment. The present paper proposes a new method to regulate the impedance of the end-effector through learning of neural networks. The method can regulate not only stiffness and viscosity but also the inertia and virtual trajectory of the end-effector and can realize a smooth transition from free to contact movements by regulating the impedance parameters before a contact.
Control methods based on using the relative motion between the manipulator and the workpiece are described for controlling the force of a one-dimensional manipulator, in which it is assumed that there are no collision...
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Control methods based on using the relative motion between the manipulator and the workpiece are described for controlling the force of a one-dimensional manipulator, in which it is assumed that there are no collisions between the manipulator and the workpiece and we use a computed force law which is similar to the computed torque law in the trajectory tracking problem of a manipulator. We consider two cases depending on whether the position and velocity of the workpiece (or end-effector) are available or not to calculate the computed force control. The effectiveness of the proposed control methods is illustrated by some computer simulations.
Software performance engineering aims at predicting and improving the performance of applications during development and in production. This paper presents a framework for performance engineering of information system...
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