Abstract: A system assists users in time and frequency analysis of magnetoencephalography (MEG) signals. In one aspect, a system includes an analysis module, a configuration module and a user interface. The analysis module performs a time and frequency analysis of the MEG signal, for example a short time Fourier transform (STFT) or a continuous wavelet transform (CWT) analysis. The analysis is parameterized by a parameter set that affects the time and frequency resolution of the analysis, for example window size and overlap size for STFT or center frequency and decay parameter for CWT. The configuration module automatically determines or assists the user to determine correct values for the parameter set.

Abstract: The location of the optical axis of a plenoptic imaging system is determined. In one approach, the plenoptic image is noise filtered, down-sampled, low-pass filtered, and again noise filtered. Slices through this image that have the highest power are used to determine the location of the optical axis. Once the location of the optical axis is determined, corrections for aberrations such as distortion can be applied.

Abstract: The location of the optical axis of a plenoptic imaging system is determined. In one approach, the plenoptic image is noise filtered, down-sampled, low-pass filtered, and again noise filtered. Slices through this image that have the highest power are used to determine the location of the optical axis. Once the location of the optical axis is determined, corrections for aberrations such as distortion can be applied.

Abstract: The condition of the plenoptic imaging system is determined using views capturing a calibration object by the plenoptic imaging system. The plenoptic imaging system accesses at least two views from the plenoptic imaging system and determines a measure of divergence from the reference condition based on the view images associated with each view. Each of the accessed views can have a known relationship when the plenoptic imaging system is in the reference condition. Based on the measure of divergence and the known relationship, the plenoptic imaging system can indicate a variation from the reference condition. The variation can indicate misalignment or degradation of the plenoptic imaging system. This determination of divergence and indication of variation from the reference condition can be included in a variety of calibration procedures.

Abstract: One aspect determines centroids of a plenoptic image, which correspond to the center view of the plenoptic image. A search band spanning a range of different values along a search direction (e.g., range of ?y) is determined. Multiple slices of the plenoptic image are taken at different values of the search coordinate (i.e., different y values). Because the plenoptic image has a periodic structure imposed by the periodicity of the microlens array, each slice will have a strong frequency component at this fundamental frequency. A slice corresponding to the centroid location (i.e., value of y at the centroid) is selected based on analysis of these frequency components. In one approach, the slice with the weakest component at this fundamental frequency is selected as the centroid. A similar approach can be performed in the orthogonal direction to obtain the two-dimensional coordinates of the centroids.

Abstract: Embodiments segment received image data. The received image data may include pixels that have multiple channels of intensity values. The image data is decomposed into albedo and shading components. This may be accomplished using a minimization that enforces a relationship between albedo, shading, and intensity values. The minimization may also include an albedo regularizer to infer albedo in part based on chromaticity and albedo of surrounding pixels. Superpixels are generated based on contiguous regions of pixels having similar image data across channels. These superpixels are then merged based in part on the determined albedo and shading components as well as based on the image data. The channels of image data may include infrared image data used to modify visible channels of the image to create pseudo-image data, which may be used in place of image data for albedo-shading decomposition, superpixel generation, or superpixel merging.

Abstract: Embodiments segment received image data. The received image data may include pixels that have multiple channels of intensity values. The image data is decomposed into albedo and shading components. This may be accomplished using a minimization that enforces a relationship between albedo, shading, and intensity values. The minimization may also include an albedo regularizer to infer albedo in part based on chromaticity and albedo of surrounding pixels. Superpixels are generated based on contiguous regions of pixels having similar image data across channels. These superpixels are then merged based in part on the determined albedo and shading components as well as based on the image data. The channels of image data may include infrared image data used to modify visible channels of the image to create pseudo-image data, which may be used in place of image data for albedo-shading decomposition, superpixel generation, or superpixel merging.