Visual inferences of material changes: color as clue and distraction

Abstract

In a snapshot, a scene consists of things, but across time, the world consists of processes. Some are cyclical, for example, trees changing foliage through the seasons, surfaces getting wet and drying out; others are unidirectional, for example, fruit ripening and then decaying, or dust accumulating on surfaces. Chemical and physical properties of objects provide them with specific surface patterns of colors and textures. When endogenous and exogenous forces alter these colors and textures over time, the ability to identify these changes from appearances can have great utility in judging the composition, state, and history of objects. This short review presents thoughts on studying visual inferences of the properties of materials and their changes, including how to acquire calibrated images of time-varying materials, how to model time-varying appearance changes, how to measure observers' identification abilities, and how to parse out the perceptual qualities that help or hinder in recognizing materials and their states. For instance, if color information is removed, observers do significantly worse at recognizing materials and their changes, especially for organic materials. The role of color in object and scene recognition is still being debated, so elucidating color's role in material identification may also help to resolve the wider issue. This review introduces material change as an object of study in human perception and cognition, because the visual traces of changes are integral components of material and object identity. Visually based judgments of materials share the property of propensity with mental inferences, and conscious or unconscious visual imagery may play a role in setting expectancies for object shapes and properties. WIREs Cogni Sci 2011 2 686-700 DOI: 10.1002/wcs.148 This article is categorized under: Psychology > Perception and Psychophysics.

FIGURE 1

(a) Dirt accumulated on building. (b) Rust from chain on flagstones. (c) Diagram illustrates how dust on a vertical façade is washed down by rain and redeposited as dirt lower on the surface, based on surface geometry and absorption. (Reprinted with permission from Ref . Copyright 1996 ACM Press)

FIGURE 2

(a) Bananas ripening and decaying (Google Images). (b) Top: Samples from five materials. Middle: Red input over time as the material changes. Bottom: Traces from middle panel superimposed on single curves using dynamic time-warping. (Reprinted with permission from Ref . Copyright 2006 Association for Computing Machinery)

FIGURE 3

(Top) Images of a glossy teapot and cup as dust accumulates. (Bottom) Simplified schematic of possible light paths through dust, water-color, oil-paint, and clear liquid covering a moderately rough surface. (Reprinted with permission from Ref . Copyright 2006 European Association for Computer Graphics)

FIGURE 4

(a) A multi-light, multi-camera system designed to acquire simultaneous images of changing materials from different lighting angles and viewpoints, thus providing fine temporal and angular resolution. (b) Four cameras mounted on robot arms that permit rapid small movements coupled with a light-source that can move inside a semi-circle. Correction added on May 25 2011, after first online publication. Figure 4 was incomplete and has been replaced.

FIGURE 5

(a) Initial and last fronto-parallel images of the 26 material changes listed in Table 1. (b) Same images, but chromatic information has been removed.

FIGURE 6

(a) Percent correct for material identification for initial images, initial plus final image, and complete sequence of images. (b) Odds ratios comparing performance with and without color information.

FIGURE 7

Percent correct identifications for material categories in the three conditions.

FIGURE 8

(a)–(h) What are these materials? What are the changes they are undergoing? See text for answers.

FIGURE 8

(a)–(h) What are these materials? What are the changes they are undergoing? See text for answers.

FIGURE 9

Percent correct for change identification for initial and last images, and for the whole sequence. Odds ratios compare performances with and without color information.

FIGURE 10

(a) Spheres made from a 100 different materials, demonstrating a large number of perceptual qualities: redness, greenness, blueness, specularness, diffuseness, glossiness, metallic-like, plastic-like, roughness, silverness, gold-like, fabriclike, acrylic-like, greasiness, dustiness, rubber-like, and others. (b) Simulations of increases in redness, silver, gold, and specularity from left to right, in first to fourth row, respectively. (Reprinted with permission from Ref . Copyright 2003 Association for Computing Machinery)

FIGURE 11

(a) Similar objects made of different materials. (b) Different objects made of similar materials.

FIGURE 12

(a) Paper chair; (b) plaster robe.

FIGURE 13

What are the materials of these shiny spheres? (a) Glass or metal? (b) Wood or stone? (c) Plastic or mud?

FIGURE 14

What are the materials of these older spheres? (a) Glass or metal? (b) Wood or stone? (c) Plastic or mud?

FIGURE 15

Based just on visual form, which of these drapes would be predicted to be softer to the touch? (a) Cloth drapes. (b) Limestone ‘draperies’ in King Solomon’s Cave, Mole Creek Karst National Park, Tasmania. (c) Concrete cloth.