Shading Flow Research Articles

Local Light Alignment for Multi‐Scale Shape Depiction

AbstractMotivated by recent findings in the field of visual perception, we present a novel approach for enhancing shape depiction and perception of surface details. We propose a shading‐based technique that relies on locally adjusting the direction of light to account for the different components of materials. Our approach ensures congruence between shape and shading flows, leading to an effective enhancement of the perception of shape and details while impairing neither the lighting nor the appearance of materials. It is formulated in a general way allowing its use for multiple scales enhancement in real‐time on the GPU, as well as in global illumination contexts. We also provide artists with fine control over the enhancement at each scale.

Hue Flows and Shading Flows: emergent properties from their interaction

Hue Flows and Shading Flows: emergent properties from their interaction

Distinguishing between texture and shading flows for 3D shape estimation

The visual system can infer 3D shape from orientation flows arising from both texture and shading patterns. However, these two types of flows provide fundamentally different information about surface structure. Texture flows, when derived from distinct elements, mainly signal first-order features (surface slant), whereas shading flow orientations primarily relate to second-order surface properties. It is therefore crucial for the brain to identify whether flow patterns originate from shading or texture to correctly infer shape. One possible approach would be to use 'surface appearance' (e.g. smooth gradients vs. fine-scale texture) to distinguish texture from shading. However, the structure of the flow fields themselves may indicate whether a given flow is more likely due to first- or second-order shape information. Here we test these two possibilities. We generated irregular objects ('blobs') using sinusoidal perturbations of spheres. We then derived two new objects from each blob: One whose shading flow matched the original object's texture flow, and another whose texture flow matched the original's shading flow. Using high and low frequency environment maps to render the surfaces, we were able to manipulate surface appearance independently from the flow structure. This provided a critical test of how appearance interacts with orientation flow when estimating 3D shape and revealed some striking illusions of shape. In an adjustment task, observers matched the perceived shape of a standard object to each comparison object by morphing between the generated shape surfaces. In a 2AFC task, observers were shown two manipulations for each blob and indicated which one matched a briefly flashed standard. Performance was compared to an orientation flow based model and confirmed that observers' judgments agreed with texture and shading flow predictions. Both the structure of the flow and overall appearance are important for shape perception, but appearance cues determine the inferred source of the observed flowfield. Meeting abstract presented at VSS 2015

Texture-shading flow interactions and perceived reflectance.

The appearance of surface texture depends on the identification of edge contours in an image generated by local variations in reflectance. These edges in the image need to be distinguished from diffuse shading gradients caused by the interaction of light with surface relief. To understand how the brain performs this separation, we generated textures with orientation flows that were initially congruent with the diffuse shading flow of planar surfaces. We found that rotating textures relative to shading increased the appearance of surface pigmentation, which was well explained by an increase in the variation of local orientation fields with increasing offset of texture gradients (Experiment 1). We obtained similar findings when rotating texture flow relative to the diffuse shading of spherical surfaces with global curvature (Experiment 2). In a second set of experiments, we found that perceived pigmentation of spherical surfaces depended on the perceived orientation of the light field; rotating images of spherical surfaces reduced both perceived pigmentation (Experiment 3) and perceived global texture contrast in an objective task (Experiment 4). The dependence of perceived texture on image orientation suggests that the separation of texture flow from shading depends on an assumed light source from above bias. These findings support the view that separation of texture flow from shading, and thus perceived pigmentation, depend not only on the local structure of orientation fields in an image, but also on midlevel representations of shading and illuminance flow.

When Shading Flows With Color: Grounding Shape and Material Inference

When Shading Flows With Color: Grounding Shape and Material Inference

Inferring Shape from Shading Flow: light source(s) as an emergent property

Inferring Shape from Shading Flow: light source(s) as an emergent property

Orientation fields in shape from shading

The interaction of shape, material and lighting creates complex patterns of orientation in the image, which play an important role in 3D surface estimation. Depending on the nature of these orientation fields, different cues can be extracted: the orientation fields produced by textured objects carry information about the 3D orientation of the surface (i.e. first order properties), while shading is compressed and stretched depending on the second-order (curvature) properties of the shape. Here, we investigate the relation between shading flows and the perception of shape. We show that by locally modifying the orientations in images of shaded surfaces, we are able to substantially alter the perceived shape. We performed two experiments in which subjects had to match the perceived shape of a test stimulus. In the first experiment, stimuli were generated by adding high frequency patterns (HFPs) on top of diffuse shading. HFPs were oriented along the principal curvature directions of the surface, by 'smearing' noise using line integral convolution. Results show that, depending on the smearing intensity and on the chosen curvature direction (i.e., minimal or maximal), surface features appeared to be either sharpened or rounded respectively. In the second experiment, we used an image processing operator to control the behavior of low-frequencies (LF) shading orientations. By distorting LF gradient patterns on a surface according to curvature information, we are able to precisely control the orientation field in an image. We show that the more a feature is aligned with the shading orientation, the less subjects are able to perceive it correctly. These experiments show that there is a direct relationship between shading orientation flows and our perception of surface curvature, implying that image orientation measurements play a key role in the estimation of shape from shading.

Validation of Halasz’s non-dimensional model using cross-flow evaporative coolers used in greenhouses

Evaporative cooling devices are used extensively in greenhouses in arid countries to reduce excessive ambient temperatures for cultivating high value crops. Accordingly, predicting outlet air temperature and humidity from the greenhouse evaporative cooler is an essential step in determining greenhouse design and operating parameters, such as the percentage of shading and air flow rate. Halasz (1998) developed a non-dimensional model capable of predicting the outlet air temperature and humidity, and outlet water temperature for evaporative coolers. This study assesses the accuracy of Halasz’s model when applied to a typical evaporative cooler used in greenhouses in Oman. A cross-fluted design of impregnated cellulose pads was used in the validation experiments. Combinations of two air flow rates and two water flow rates were studied. When model predictions were compared to measured results, it was found that the model accurately predicted the outlet air and water conditions. Within the range of operating parameters investigated, the average percentage predictive error for the outlet air and water temperatures ranged from 1.16 to 2.86% (SD ≤ 1.84%) and −4.91 to −1.30% (SD ≤ 2.29%), respectively. The average percentage predictive error for the outlet humidity ratio was between −2.21 and 0.43% (SD ≤ 4.82%).

Motion and structure from multiple cues; image motion, shading flow, and stereo disparity

We investigate the recovery of 3-D motion and structure from the stereo images of a stationary environment. A Kalman filter-based framework is proposed for the reconstruction of 3-D structure from multiple visual cues, through the integration of image motion and stereo disparity with the shading flow that is induced by the rotational motion of the source. A common scenario involves the coupled motion of artificial source(s) and stereo cameras that are installed on mobile submersible vehicles. Utilizing shading flow with the image motion leads to devising a more robust 3-D motion estimation algorithm, in addition to the important role in depth recovery/refinement by constraining the local surface gradients. Collectively, use of multiple cues enhances robustness with respect to perturbation in any of the cues. Results of experiments with synthetic and real imagery are presented to evaluate the performance of the proposed algorithm, including the construction of a composite 3-D depth map of an underwater scene from a sequence of stereo pairs.