Polygon-Based Drawing Accuracy Analysis and Positive/Negative Space

Abstract

The study of drawing generally depends on ratings by human critics and self-reported expertise of the drawers. To complement those approaches, we developed an objective continuous performance-based measure of drawing accuracy. This measure represents drawings as sets of landmark points and analyses features of particular research interest by comparing polygons of those features’ landmark points with their counterpart polygons in a veridical image. This approach produces local accuracy measures (for each polygon), a global accuracy measure (the mean across several polygons), and four distinct properties of a polygon for analysis: its size, its position, its orientation and the proportionality of its shape. We briefly describe the method and its potential research applications in drawing education and visual perception, then apply it to a specific research question: Are we more accurate when drawing in the so-called ‘positive space’ (or figure)? In a polygon-based accuracy analysis of 34 representational drawings, expert drawers outperformed less experienced participants on overall accuracy and every dimension of polygon error. Comparing polygons in the positive and negative space revealed an apparent trade-off on the different dimensions of polygon error. People were more accurate at proportionality and position in the positive space than in the negative space, but more accurate at orientation in the negative space. The contribution is the use of an objective, performance-based analysis of geometric deformations to study the accuracy of drawings at different levels of organization, here, in the positive and negative space.

The genesis of errors in drawing

“…This suggests that the development of artistic expertise involves an ability to see familiar object and scenes as if they were unfamiliar, in order to reduce the influence of biasing categorical schemas. In support of this contention, Carson, Millard, Quehl and Danckert (2014) found that the orientation of negative space (the space between objects or elements of the visual scene) is drawn more accurately than regions of (identifiable) positive space, while the proportionality of positive space is drawn more accurately than negative space ( Figure 5). The effect of familiarity on drawing errors may be due to the fact that familiar objects and their relations are coded categorically when being rendered.…”

Drawing portraits and still lifes from 2D and 3D sources.

“…One of the common strategies used by artists (Chamberlain, 2012) and recommended by teachers (Edwards, 1989, p. 97; Mendelowitz et al, 2007, p. 50) is to attend to the negative space, that is, the shapes of the meaningless regions between foreground objects. Carson et al (2014) demonstrated that the pattern of drawing errors does indeed differ significantly between shapes in the positive and the negative space, the meaningful and the meaningless.…”

“…To produce objective measures of drawing accuracy, the GT photographs and drawings were manually annotated using a Matlab script to locate unambiguous landmarks for geometric analysis. We conducted a geometric analysis of drawing accuracy using an approach that has been reported previously (Carson et al, 2014). For information, we summarize the method here.…”

“…Early studies used qualitative methods based on the subjective judgment of human raters (e.g., Chamberlain & Wagemans, 2015; Cohen, 2005; Kozbelt, Seidel, ElBassiouny, Mark, & Owen, 2010). Some recent studies have applied quantitative methods based on objective repeatable measurements of drawing geometry compared with a standard (e.g., Carson, Millard, Quehl, & Danckert, 2014; Hayes & Milne, 2011; Perdreau & Cavanagh, 2014). Researchers have established that objectively measured drawing accuracy is consistent with the expertise of the artist (Carson & Allard, 2013) and with the judgment of human raters (Chamberlain, McManus, Riley, Rankin, & Brunswick, 2014).…”