Electrodynamic speakers compatible with (functional) magnetic resonance imaging (MRI) are described. The speakers magnets are removed, their function is replaced by the scanner's magnetic field, resulting in an uncommon but efficient operation. The method can be used with headphones as well as woofers. Functional MRI is not associated with any known biological risks, but as a method for visualization of task-specific activation of brain regions it is undesirably noisy. Thus, it requires both noise protection and efficient sound transmission systems for delivering acoustic stimuli to subjects. Woofers could possibly be used in active noise-control systems. The speakers described in this paper can be used for either task.

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Auditory foreground-background decomposition is a pattern recognition process which combines simultaneous and sequential grouping in complex sound sequences. Using functional magnetic resonance imaging with reduced scanner noise and stimulation through a new type of earphones, we investigated the possibility that this process activates topographically distinct areas of human auditory cortex. A basic matching-to-sample task with variable tones (sequential grouping) caused significant activity in three separate landmark-related territories on the supratemporal plane. A similar task in the presence of a strongly masking acoustic background pattern to challenge simultaneous grouping led to the distinction of the subterritory in which foreground signal-related or task-related signal properties were exclusively seen. In contrast to the remainder of territories the level of activity and the periodicity of the signal time-course was resistant to the masking influence of the background. This suggests that auditory foreground-background decomposition involves a specialized non-primary auditory cortex field. Generally, the findings demonstrate functional parcellation of auditory cortex for which the evidence in humans, in contrast to other primates, is only indirect to date.

We develop a qualitative measure for the completeness and complementarity of sets of local features in terms of covering relevant image information. The idea is to interpret feature detection and description as image coding, and relate it to classical coding schemes like JPEG. Given an image, we derive a feature density from a set of local features, and measure its distance to an entropy density computed from the power spectrum of local image patches over scale. Our measure is meant to be complementary to existing ones: After task usefulness of a set of detectors has been determined regarding robustness and sparseness of the features, the scheme can be used for comparing their completeness and assessing effects of combining multiple detectors. The approach has several advantages over a simple comparison of image coverage: It favors response on structured image parts, penalizes features in purely homogeneous areas, and accounts for features appearing at the same location on different scales. Combinations of complementary features tend to converge towards the entropy, while an increased amount of random features does not. We analyse the complementarity of popular feature detectors over different image categories and investigate the completeness of combinations. The derived entropy distribution leads to a new scale and rotation invariant window detector, which uses a fractal image model to take pixel correlations into account.

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