We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.

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This research deals with the problem of range image registration for the purpose of building surface models of three-dimensional objects. The registration task involves nding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces, an integrated surface representation of the object. The approach taken is to express the registration task as an optimization problem. We de ne a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances between a set of control points on one of the surfaces to corresponding points on the other. The strength of this approach resides in the method used to determine point correspondences across range images. It is based on reversing the range nder calibration process, resulting in a set of equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in three-dimensional space. A stochastic optimization technique, Very Fast Simulated Reannealing (VFSR), is used to minimize the cost function. Dual-view registration experiments yielded excellent results in very reasonable computational time. A multiview registration experiment was also performed, but a large processing time was required. A complete surface model of a typical 3D object was then constructed from the integration of its multiple partial views. The e ectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed.

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National Clinical Trial Number NCT02286141.