Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.

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Abstract-We present a novel approach for the transmission of haptic data in telepresence and teleaction systems. The goal of this work is to reduce the packet rate between an operator and a teleoperator without impairing the immersiveness of the system. Our approach exploits the properties of human haptic perception and is, more specifically, based on the concept of just noticeable differences. In our scheme, updates of the haptic amplitude values are signaled across the network only if the change of a haptic stimulus is detectable by the human operator. We investigate haptic data communication for a 1 degree-of-freedom (DoF) and a 3 DoF teleaction system. Our experimental results show that the presented approach is able to reduce the packet rate between the operator and teleoperator by up to 90% of the original rate without affecting the performance of the system.

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ABSTRACT:We propose a single-frame, learning-based super-resolution restoration technique by using the wavelet domain to define a constraint on the solution. Wavelet coefficients at finer scales of the unknown high-resolution image are learned from a set of high-resolution training images and the learned image in the wavelet domain is used for further regularization while super-resolving the picture. We use an appropriate smoothness prior with discontinuity preservation in addition to the wavelet-based constraint to estimate the superresolved image. The smoothness term ensures the spatial correlation among the pixels, whereas the learning term chooses the best edges from the training set. Because this amounts to extrapolating the high-frequency components, the proposed method does not suffer from oversmoothing effects. The results demonstrate the effectiveness of the proposed approach.

We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene.