Optimal defocus estimation in individual natural images

Burge, Johannes; Geisler, Wilson S.

doi:10.1073/pnas.1108491108

Cited by 128 publications

(117 citation statements)

References 37 publications

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“…One possible reason for this similarity is that the elevation-dependent filtering of the outer ear is set to maximize the transfer of naturally available information. This result parallels previous findings in human vision showing a high degree of similarity between the spectra of natural images and the optical transfer function of the eye (18). This might suggest that human spatial hearing is so finely tuned to the environment that even the filtering properties of the outer ear, and hence its convoluted anatomy, evolved to mirror the statistics of natural auditory scenes.…”

Section: Significancesupporting

confidence: 79%

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Natural auditory scene statistics shapes human spatial hearing

Parise

Knorre

Ernst

2014

Proc. Natl. Acad. Sci. U.S.A.

207

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Human perception, cognition, and action are laced with seemingly arbitrary mappings. In particular, sound has a strong spatial connotation: Sounds are high and low, melodies rise and fall, and pitch systematically biases perceived sound elevation. The origins of such mappings are unknown. Are they the result of physiological constraints, do they reflect natural environmental statistics, or are they truly arbitrary? We recorded natural sounds from the environment, analyzed the elevation-dependent filtering of the outer ear, and measured frequency-dependent biases in human sound localization. We find that auditory scene statistics reveals a clear mapping between frequency and elevation. Perhaps more interestingly, this natural statistical mapping is tightly mirrored in both ear-filtering properties and in perceived sound location. This suggests that both sound localization behavior and ear anatomy are fine-tuned to the statistics of natural auditory scenes, likely providing the basis for the spatial connotation of human hearing.frequency-elevation mapping | head-related transfer function | Bayesian modeling | cross-modal correspondence T he spatial connotation of auditory pitch is a universal hallmark of human cognition. High pitch is consistently mapped to high positions in space in a wide range of cognitive (1-3), perceptual (4-6), attentional (7-12), and linguistic functions (13), and the same mapping has been consistently found in infants as young as 4 mo of age (14). In spatial hearing, the perceived spatial elevation of pure tones is almost fully determined by frequency--rather than physical location--in a very systematic fashion [i.e., the Pratt effect (4, 5)]. Likewise, most natural languages use the same spatial attributes, high and low, to describe pitch (13), and throughout the history of musical notation high notes have been represented high on the staff. However, a comprehensive account for the origins of the spatial connotation of auditory pitch to date is still missing. More than a century ago, Stumpf (13) suggested that it might stem from the statistics of natural auditory scenes, but this hypothesis has never been tested. This is a major omission, as the frequency-elevation mapping often leads to remarkable inaccuracies in sound localization (4, 5) and can even trigger visual illusions (6), but it can also lead to benefits such as reduced reaction times or improved detection performance (7-12). ResultsTo trace the origins of the mapping between auditory frequency and perceived vertical elevation, we first measured whether this mapping is already present in the statistics of natural auditory signals. When trying to characterize the statistical properties of incoming signals, it is critical to distinguish between distal stimuli, the signals as they are generated in the environment, and proximal stimuli, the signals that reach the transducers (i.e., the middle and inner ear). In the case of auditory stimuli this is especially important, because the head and the outer ear operate as frequency-and elev...

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Section: Significancesupporting

confidence: 79%

“…1 C and D). Something similar has been found in human vision, where the filtering properties of the eye seem to exaggerate the statistics of natural visual scenes (18). It would be a matter of future research to understand why the brain and the filtering of the outer ear encode the same FEM present in the environment on a different scale.…”

Section: Methodsmentioning

confidence: 83%

Natural auditory scene statistics shapes human spatial hearing

Parise

Knorre

Ernst

2014

Proc. Natl. Acad. Sci. U.S.A.

207

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“…These signals include but are not limited to figureground cues (Burge et al 2010), defocus blur (Burge and Geisler 2011;Held et al 2012), motion parallax (Wallace 1959), and looming (Beverley 1973;Beverley and Regan 1973). Binocular disparity is thus not the only source of information relevant for estimating depth, but it is a source of information that many animals exploit.…”

Section: Discussionmentioning

confidence: 99%

Binocular integration and disparity selectivity in mouse primary visual cortex

Scholl

Burge

Priebe

2013

Journal of Neurophysiology

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Scholl B, Burge J, Priebe NJ. Binocular integration and disparity selectivity in mouse primary visual cortex. J Neurophysiol 109: 3013-3024, 2013. First published March 20, 2013 doi:10.1152/jn.01021.2012.-Signals from the two eyes are first integrated in primary visual cortex (V1). In many mammals, this binocular integration is an important first step in the development of stereopsis, the perception of depth from disparity. Neurons in the binocular zone of mouse V1 receive inputs from both eyes, but it is unclear how that binocular information is integrated and whether this integration has a function similar to that found in other mammals. Using extracellular recordings, we demonstrate that mouse V1 neurons are tuned for binocular disparities, or spatial differences, between the inputs from each eye, thus extracting signals potentially useful for estimating depth. The disparities encoded by mouse V1 are significantly larger than those encoded by cat and primate. Interestingly, these larger disparities correspond to distances that are likely to be ecologically relevant in natural viewing, given the stereo-geometry of the mouse visual system. Across mammalian species, it appears that binocular integration is a common cortical computation used to extract information relevant for estimating depth. As such, it is a prime example of how the integration of multiple sensory signals is used to generate accurate estimates of properties in our environment. primary visual cortex; mouse binocularity; disparity tuning; ocular dominance TO ENABLE ACCURATE ESTIMATES of behaviorally relevant properties of the environment, sensory systems integrate information from multiple sources. For example, in the visual system, signals from the left and right eyes are integrated to provide information about depth. In mammals, left and right eye signals first converge in primary visual cortex (V1). The different vantage points of the eyes create local spatial offsets in the retinal images, offsets known as binocular disparities. Binocular disparity changes with the depths of objects in the environment. Neurons that encode retinal image information relevant for estimating binocular disparity therefore provide information relevant for binocular depth perception (Barlow et al. 1967;Blakemore 1969;Hubel and Wiesel 1973;Joshua 1970;Nikara et al. 1968;Pettigrew et al. 1968). Binocular disparity selectivity can be observed in individual neurons; some binocular stimuli elicit large increases in responses, while others reduce responses, relative to monocular stimulation alone (Hubel and Wiesel 1962;Ohzawa and Freeman 1986;Pettigrew et al. 1968).Disparity-selective binocular neurons have been reported in many animals, including carnivores and primates. Such neurons have not, however, been reported in rodents. In recent years, mice have become an increasingly important model for the study of visual processing and cortical plasticity. Genetic techniques are now available to dissect underlying circuitry and its emergence during development. Here, we rep...

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“…The number of peaks in histograms grows with scale S . After selection of pixels lying on the object's edges using (10), the histograms of gradients ) (S…”

Section: S J Imentioning

confidence: 99%

“…Basic principles of blur estimation to be taken in consideration as guidelines for fast algorithm of best focus position search have been published in [6][7][8][9][10]. In present paper we propose a method of best focus position search which is based on linear-scale differential analysis (LSDA) of digital image.…”

Section: Introductionmentioning

confidence: 99%

Fast calculation of the best focus position

Bezzubik¹,

Belashenkov

Vdovin

2015

SPIE Proceedings

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New computational technique based on linear-scale differential analysis (LSDA) of digital image is proposed to find the best focus position in digital microscopy by means of defocus estimation in two near-focal positions only. The method is based on the calculation of local gradients of the image on different scales using its convolution with a number of differential filters of linearly varying sizes, consequent removal of noisy pixels out of consideration, and selection of pixels at the edges of objects. It is shown that the mean values of the selected gradients decrease while the scale increases thus the rate of change of these mean values of gradients unambiguously determines the magnitude of digital image defocus as a function of scale. Using this method the value and sign of defocus can be found if the result of LSDA of captured images is compared with pre-defined look-up table. The robustness of the proposed method to spatial noise is achieved by ignoring pixels that are corrupted by spatial noise within the areas of the image outside the edges of objects. Most computational operations of the method are based on integer arithmetic that simplifies its practical implementation and significantly improves the performance. The latter aspect is particularly important for practical use in real-time imaging systems.

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Optimal defocus estimation in individual natural images

Cited by 128 publications

References 37 publications

Natural auditory scene statistics shapes human spatial hearing

Natural auditory scene statistics shapes human spatial hearing

Binocular integration and disparity selectivity in mouse primary visual cortex

Fast calculation of the best focus position

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