Schütt, Heiko H.

Wichmann, Felix A.

English

Purdue E-Pubs

An Image-Based Model for Early Visual ProcessingHeiko H. Schütt & Felix A. WichmannFigure 3: Oblique masking results. Detectionthreshold in percent against the orientation of a 25%contrast mask with the same phase, spatialfrequency and spatial window.0.25 1 4 16Spatial Frequency [cyc/deg]10 110 2Contrast Sensitivity [1/thresh]Figure 1: Contrast sensitivity function for 1497msHanning windowed presentation time. Black symbolsshow measurements (one symbol per observer), thecontinous line is our model prediction.Figure 2: Contrast discrimination. Incrementthreshold vs. pedestal contrast for gratigs of 8.37cyc/deg and 1497ms presentation time.Early spatial vision was explored extensively over several decades inmany psychophysical detection and discrimination experiments. Thusa large body of such data is available. Goris et al. (2013) integratedthis psychophysical literature and proposed a model based on maximum-likelihood decoding of a neurophysiologically inspired population of modelneurons. Their neural population model (NPM) is unique in its ability topredict several data sets simultaneously, using a single set of parameters.However, the NPM is only one-dimensional, operating on the activityof abstract spatial frequency channels. Thus it cannot be applied toarbitrary images as a generic front-end to explore the influence of earlyvisual processing on mid- or high-level vision.Bradley et al. (2014), on the other hand, recently presented an earlyvision model operating on images. Their model is thus able to makepredictions for arbitrary images. However, compared to the NPM, theirmodel lacks in nonlinear processing, which is replaced by an effectivemasking contrast depending on the detection target. Thus while Bradleyet al. fit a range of detection data they do not fit nonlinear aspects ofearly vision like the dipper function.Here we combine both approaches into a model which includes nonlin-ear processing and statistically efficient decoding and operates on imagesexplicitly calculating channel activities over the image. The model appliesoptical degradation and retinal processing to the image before the imageis passed to a spatial frequency and orientation decomposition followedby divisive inhibition. We tested the predictions of our model against abroad range of early psychophysical experiments and found it predictssome hallmarks of early visual processing like the contrast sensitivityfunction (Figure 1), the dipper function for contrast discrimination (Fig-ure 2) and oblique masking data (Figure 3). The measured data we plotagainst stems from standard 2AFC contrast detection and discriminationexperiments run for Wichmann’s DPhil thesis (1999).Early vision was extensively explored empirically and there are plentyof models which explain different parts of the empirical findings. Theseresults provide detailed information about the first processing steps invisual perception thought to happen in the retina, LGN and V1, whichstimuli can be differentiated or detected using their output and in whatformat the output is available to any higher visual processing. However,research on higher visual processing rarely employs the informationavailable from early vision psychophysics.The main reasons may be that the models described in the earlyspatial vision literature typically do not predict behaviour on arbitraryimages, because they often do not process images, but only predict the(putative) responses of the early visual system to Gabor patches or simplegrating stimuli. We try to overcome these limitations by explaining manypsychophysical observations within a model taking images as input.ReferencesBradley, C., Abrams, J., and Geisler, W. S. (2014). Retina-v1 model ofdetectability across the visual field. Journal of Vision, 14(12):1–22.Goris, R. L. T., Putzeys, T., Wagemans, J., and Wichmann, F. A. (2013).A neural population model for visual pattern detection. PsychologicalReview, 120:472–496.Wichmann, F. A. (1999). Some aspects of modelling human spatial vision:contrast discrimination. DPhil thesis, University of Oxford.1

An Image-Based Model for Early Visual Processing

https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1062&amp;context=modvis

An Image-Based Model for Early Visual Processing

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