The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing general rule-of-thumb performance limits for gradient-based image registration techniques.

Introduction Image registration is the process of finding a transformation that aligns one image to another. Within this broad area of research, medical image registration has emerged as a particularly active field (see e.g., [4, 8] for general surveys). This activity is due in part to the many clinical applications including diagnosis, longitudinal studies, and surgical planning, and to the need for registration across different imaging modalities (e.g., MRI, CT, PET, X-RAY, etc.). Medical image registration, however, still presents many challenges. Several notable difficulties are 1.) the transformation between images can vary widely and be highly nonlinear in nature 2.) the transformation between images acquired from different modalities may differ significantly in overall appearance and resolution; 3.) each imaging modality introduces its own unique challenges, making it difficult to develop a single generic registration algorithm. We have developed a general registration algorit

Abstract. This volume is intended to help advance the field of artificial neural networks along the lines of complexity present in animal brains. In particular, we are interested in examining the biological phenomena of modularity and specialized learning. These topics are already the subject of research in another area of artificial intelligence. The design of complete autonomous agents (CAA), such as mobile robots or virtual reality characters, has been dominated by modular architectures and context-driven action selection and learning. In this chapter, we help bridge the gap from neuroscience to artificial neural networks (ANN) by incorporating CAA. We do this both directly, by using CAA as a metaphor to consider requirements for ANN, and indirectly, by using CAA research to better understand and model neuroscience. We discuss the strengths and the limitations of these forms of modeling, and propose as future work extensions to CAA inspired by neuroscience.

Abstract. The weighted sum of squared differences cost function is often minimized to align two images with overlapping fields of view. If one image is shifted with respect to the other, the cost function can be written as a sum involving convolutions. This paper demonstrates that performing these convolutions in the frequency domain saves a significant amount of processing time when searching for a global optimum. In addition, the method is invariant under linear intensity mappings. Applications include medical imaging, remote sensing, fractal coding, and image photomosaics. 1

We introduce functional MRI methods for estimating the neuronal population receptive field (pRF). These methods build on conventional visual field mapping that measures responses to ring and wedge patterns shown at a series of visual field locations and estimates the single position in the visual field that produces the largest response. The new method computes a model of the population receptive field from responses to a wide range of stimuli and estimates the visual field map as well as other neuronal population properties, such as receptive field size and laterality. The visual field maps obtained with the pRF method are more accurate than those obtained using conventional visual field mapping, and we trace with high precision the visual field maps to the center of the foveal representation. We report quantitative estimates of pRF size in medial, lateral and ventral occipital regions of human visual cortex. Also, we quantify the amount of input from ipsi- and contralateral visual fields. The human pRF size estimates in V1–V3 agree well with electrophysiological receptive field measurements at a range of eccentricities in corresponding locations within monkey and human visual field maps. The pRF method is non-invasive and can be applied to a wide range of conditions when it is useful to link fMRI signals in the visual pathways to neuronal receptive fields.

organization for delayed saccades in human posterior parietal

maps of visual spatial attention in human parietal cortex. J Neurophysiol

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Registration using the least-squares cost function is sensitive to the intensity fluctuations caused by the blood oxygen level dependent (BOLD) signal in functional MRI (fMRI) experiments, resulting in stimulus-correlated motion errors. These errors are severe enough to cause false-positive clusters in the activation maps of datasets acquired from 3T scanners. This paper presents a new approach to resolving the coupling between registration and activation. Instead of treating the two problems as individual steps in a sequence, they are combined into a single least-squares problem and are solved simultaneously. Robustness tests on a variety of simulated 3D EPI datasets show that the stimulus-correlated motion errors are removed, resulting in a substantial decrease in false-positive and false-negative activation rates. The new method is also shown to decorrelate the motion estimates from the stimulus by testing it on different in vivo fMRI datasets acquired from two different 3T scanners.

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