Direct evidence for the existence of energy-based texture mechanisms

Synergy of spatial frequency and orientation bandwidth in texture segregation

Defining target textures by increased bandwidths in spatial frequency and orientation, we observed strong cue combination effects in a combined texture figure detection and discrimination task. Performance for double-cue targets was better than predicted by independent processing of either cue and even better than predicted from linear cue integration. Application of a texture-processing model revealed that the oversummative cue combination effect is captured by calculating a low-level summary statistic (\(\Delta CE_m\)), which describes the differential contrast energy to target and reference textures, from multiple scales and orientations, and integrating this statistic across channels with a winner-take-all rule. Modeling detection performance using a signal detection theory framework showed that the observers' sensitivity to single-cue and double-cue texture targets, measured in \(d^{\prime }\) units, could be reproduced with plausible settings for filter and noise parameters. These results challenge models assuming separate channeling of elementary features and their later integration, since oversummative cue combination effects appear as an inherent property of local energy mechanisms, at least for spatial frequency and orientation bandwidth-modulated textures.

Pooling of first-order inputs in second-order vision

The processing of texture patterns has been characterized by a model that first filters the image to isolate one texture component, then applies a rectifying nonlinearity that converts texture variation into intensity variation, and finally processes the resulting pattern with mechanisms similar to those used in processing luminance-defined images (spatial-frequency- and orientation-tuned filters). This model, known as FRF for filter rectify filter, has the appeal of explaining sensitivity to second-order patterns in terms of mechanisms known to exist for processing first-order patterns. This model implies an unexpected interaction between the first and second stages of filtering; if the first-stage filter consists of narrowband mechanisms tuned to detect the carrier texture, then sensitivity to high-frequency texture modulations should be much lower than is observed in humans. We propose that the human visual system must pool over first-order channels tuned to a wide range of spatial frequencies and orientations to achieve texture demodulation, and provide psychophysical evidence for pooling in a cross-carrier adaptation experiment and in an experiment that measures modulation contrast sensitivity at very low first-order contrast.

Texture modulation detection by probability summation among orientation-selective and isotropic mechanisms

Substantial evidence has accumulated for the notion that modulations of second-order properties in the visual scene are processed by mechanisms which detect contrast variations within narrow orientation/spatial frequency channels. It has also been suggested that mechanisms exist which detect contrast modulations across all orientations. Many naturally occurring texture variations (e.g., modulations in orientation and/or spatial frequency) involve simultaneous contrast modulations in multiple channels. Contrasting conclusions have been drawn regarding the manner in which the information carried in multiple channels is combined. In a series of two experiments it is shown that simultaneous contrast modulations in two narrow orientation bands are detected by three mechanisms, two of which detect contrast modulations within the modulated bands only, the third of which integrates contrast across orientations in order to detect modulations of overall contrast. The three mechanisms combine their efforts by probability summation.