A perceptual bias for man-made objects in humans.

Ambiguous images are widely recognized as a valuable tool for probing human perception. Perceptual biases that arise when people make judgements about ambiguous images reveal their expectations about the environment. While perceptual biases in early visual processing have been well established, their existence in higher-level vision has been explored only for faces, which may be processed differently from other objects. Here we developed a new, highly versatile method of creating ambiguous hybrid images comprising two component objects belonging to distinct categories. We used these hybrids to measure perceptual biases in object classification and found that images of man-made (manufactured) objects dominated those of naturally occurring (non-man-made) ones in hybrids. This dominance generalized to a broad range of object categories, persisted when the horizontal and vertical elements that dominate man-made objects were removed and increased with the real-world size of the manufactured object. Our findings show for the first time that people have perceptual biases to see man-made objects and suggest that extended exposure to manufactured environments in our urban-living participants has changed the way that they see the world.

A differential model of the complex cell.

The receptive fields of simple cells in the visual cortex can be understood as linear filters. These filters can be modeled by Gabor functions or gaussian derivatives. Gabor functions can also be combined in an energy model of the complex cell response. This letter proposes an alternative model of the complex cell, based on gaussian derivatives. It is most important to account for the insensitivity of the complex response to small shifts of the image. The new model uses a linear combination of the first few derivative filters, at a single position, to approximate the first derivative filter, at a series of adjacent positions. The maximum response, over all positions, gives a signal that is insensitive to small shifts of the image. This model, unlike previous approaches, is based on the scale space theory of visual processing. In particular, the complex cell is built from filters that respond to the 2D differential structure of the image. The computational aspects of the new model are studied in one and two dimensions, using the steerability of the gaussian derivatives. The response of the model to basic images, such as edges and gratings, is derived formally. The response to natural images is also evaluated, using statistical measures of shift insensitivity. The neural implementation and predictions of the model are discussed.

Anxiety, inhibition, efficiency, and effectiveness. An investigation using antisaccade task.

Effects of anxiety on the antisaccade task were assessed. Performance effectiveness on this task (indexed by error rate) reflects a conflict between volitional and reflexive responses resolved by inhibitory processes (Hutton, S. B., & Ettinger, U. (2006). The antisaccade task as a research tool in psychopathology: A critical review. Psychophysiology, 43, 302-313). However, latency of the first correct saccade reflects processing efficiency (relationship between performance effectiveness and use of resources). In two experiments, high-anxious participants had longer correct antisaccade latencies than low-anxious participants and this effect was greater with threatening cues than positive or neutral ones. The high- and low-anxious groups did not differ in terms of error rate in the antisaccade task. No group differences were found in terms of latency or error rate in the prosaccade task. These results indicate that anxiety affects performance efficiency but not performance effectiveness. The findings are interpreted within the context of attentional control theory (Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7 (2), 336-353).

Constrained Optimization for Plane-Based Stereo.

Depth and surface normal estimation are crucial components in understanding 3D scene geometry from calibrated stereo images. In this paper, we propose visibility and disparity magnitude constraints for slanted patches in the scene. These constraints can be used to associate geometrically feasible planes with each point in the disparity space. The new constraints are validated in the PatchMatch Stereo framework. We use these new constraints not only for initialization, but also in the local plane refinement step of this iterative algorithm. The proposed constraints increase the probability of estimating correct plane parameters, and lead to an improved 3D reconstruction of the scene. Furthermore, the proposed constrained initialization reduces the number of iterations before convergence to the optimal plane parameters. In addition, as most stereo image pairs are not perfectly rectified, we modify the view propagation process by assigning the plane parameters to the neighbors of the candidate pixel. To update the plane parameters in the plane refinement step, we use a gradient free non-linear optimizer. The benefits of the new initialization, propagation, and refinement schemes are demonstrated.

Layered Scene Models from Single Hazy Images.

This paper describes the construction of a layered scene model, based on a single hazy image that has sufficient depth variation. A depth map and radiance image are estimated by standard dehazing methods. The radiance image is then segmented into a small number of clusters, and a corresponding scene plane is estimated for each. This provides the basic structure of a layered scene model, without the need for multiple views, or image correspondences. We show that problems of gap filling and depth blending can be addressed systematically, with respect to the layered depth structure. The final models, which resemble cardboard 'pop-ups', are visually convincing. An implementation is described, and subjective depth preferences are tested in a psychophysical experiment.

A tilt after-effect for images of buildings: evidence of selectivity for the orientation of everyday scenes.

The tilt after-effect (TAE) is thought to be a manifestation of gain control in mechanisms selective for spatial orientation in visual stimuli. It has been demonstrated with luminance-defined stripes, contrast-defined stripes, orientation-defined stripes and even with natural images. Of course, all images can be decomposed into a sum of stripes, so it should not be surprising to find a TAE when adapting and test images contain stripes that differ by 15° or so. We show this latter condition is not necessary for the TAE with natural images: adaptation to slightly tilted and vertically filtered houses produced a 'repulsive' bias in the perceived orientation of horizontally filtered houses. These results suggest gain control in mechanisms selective for spatial orientation in natural images.

Fusion of range and stereo data for high-resolution scene-modeling.

This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. In particular, we combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. Unlike existing schemes that build on MRF optimizers, we infer the disparity map from a series of local energy minimization problems that are solved hierarchically, by growing sparse initial disparities obtained from the depth data. The accuracy of the method is not compromised, owing to three properties of the data-term in the energy function. First, it incorporates a new correlation function that is capable of providing refined correlations and disparities, via subpixel correction. Second, the correlation scores rely on an adaptive cost aggregation step, based on the depth data. Third, the stereo and depth likelihoods are adaptively fused, based on the scene texture and camera geometry. These properties lead to a more selective growing process which, unlike previous seed-growing methods, avoids the tendency to propagate incorrect disparities. The proposed method gives rise to an intrinsically efficient algorithm, which runs at 3FPS on 2.0 MP images on a standard desktop computer. The strong performance of the new method is established both by quantitative comparisons with state-of-the-art methods, and by qualitative comparisons using real depth-stereo data-sets.

Cyclorotation models for eyes and cameras.

The human visual system obeys Listing's law, which means that the cyclorotation of the eye (around the line of sight) can be predicted from the direction of the fixation point. It is shown here that Listing's law can conveniently be formulated in terms of rotation matrices. The function that defines the observed cyclorotation is derived in this representation. Two polynomial approximations of the function are developed, and the accuracy of each model is evaluated by numerical integration over a range of gaze directions. The error of the simplest approximation for typical eye movements is less than half a degree. It is shown that, given a set of calibrated images, the effect of Listing's law can be simulated in a way that is physically consistent with the original camera. This condition is important for robotic models of human vision, which typically do not reproduce the mechanics of the oculomotor system.

Cyclopean geometry of binocular vision.

The geometry of binocular projection is analyzed in relation to the primate visual system. An oculomotor parameterization that includes the classical vergence and version angles is defined. It is shown that the epipolar geometry of the system is constrained by binocular coordination of the eyes. A local model of the scene is adopted in which depth is measured relative to a plane containing the fixation point. These constructions lead to an explicit parameterization of the binocular disparity field involving the gaze angles as well as the scene structure. The representation of visual direction and depth is discussed with reference to the relevant psychophysical and neurophysiological literature.