Given that the movement patterns will be the result of multiple moving parts, the geometry and the number of parts need to be carefully considered. Rather than speculate, a pilot study was undertaken to test the impact of part geometry and numerical scale. Research on the visual mechanisms of human perception has informed the selection. While there is still debate on how human vision processes motion, the consensus is that the primary factor is feature recognition.18 In the context of a greyscale image on a computer monitor, controlled experiments by Derrington et al. indicate feature recognition will be determined by edge detection and relative shading.19
Figure 6.2 illustrates the fi nal outcome of the pilot study. Each of the fi ve shapes has variation in number of edges and relative shading. A sphere, when shaded, produces the most accurate depth perception, but because there is a circular profi le, edge detection is diffi cult. Edge detection is improved with the case of a cir- cular disc, with the front and back edges being distinct. Rectangular shapes increase the number of edges but result in less distinctive relative shading. Further shapes involving more edge detection and less ambiguous shading were generated. The triangular shape provided good edge detection and reasonable shading detection, but when combined and rotated to provide a closely packed area, distracting horizontal bands occur. The hexagon shape gave a good combination of edge detection and contrast between shaded areas. The trials showed that increasing the number of edges beyond six proved counterproductive, as edge differentiation became harder. Moreover, the hexagon provided a relatively neutral orientation when a large number were combined in an offset, closely packed confi guration. There was no horizontal
Figure 6.2 Trials of different geometry undertaken as a pilot study. Hexagonal parts provided the best mix of edge detection and shading depth for motion detection
or vertical emphasis, and lateral movements were more easily recognized, as com- pared to the orthogonal grid that is the outcome of multiple rectilinear shapes. In summary, the trials demonstrated that a hexagon provided the best combination of edge detection and relative shading contrast, and can be closely packed, without privileging rectilinear pattern formation.
The aspect ratio, viewing angle and geometry of parts have been determined. The remaining issue to be resolved is the number of parts utilized to represent movement patterns. When composing groups of objects, the generally agreed threshold for distinguishing individual parts is between fi ve and seven.20 At seven and below, dependent on individual visual acuity, each entity can be tracked as a separate event. Above seven the parts are tracked as groups. For example, eight objects are tracked as groups of four, or as asymmetrical groups dependent on relations between entities. This sets a threshold at a lower limit to the number of parts at eight. Is there a logic for determining the upper limit? If we consider the control variables outlined previously, the proposal to use methods such as cellular automata (CA) has implications for numerical scale. The patterns that result from CA and fl ocking are reliant on interaction between multiple parts, but there is no established threshold of parts, below which pattern emergence is unlikely. Viewing published examples of the life-like and cyclic CA, typically the number of cells is in the region of 300–1000.21 The other factor affecting the upper limit of numerical scale is the fi delity of the computer monitor. Animation trials were undertaken, and there is a threshold above which the fi ne scale of the animations produces moiré-like pat- terns that interfere with the visualization. The trials revealed that, once the numerical scale got into the 500+ range, display interference was signifi cant. It is proposed to undertake the experiment using a close packed 4:3 array of hexagon shapes of 21 × 19 which gives a numerical scale of 399.
Stages
A staged approach to design is undertaken, where the results of the preceding are reviewed and determine the emphasis of subsequent exploration. The aim is to produce a wide range of animations through a mix of methodical and intuitive tactics. The strategy is to review and select the most distinctive examples from each stage for further iterations. In this way, it is anticipated that redundancy can be minimized, as repetitive outcomes can be foreseen and avoided. As important as avoiding repetition, is the intuitive identifi cation of latency within individual patterns, which can guide more detailed experimentation. In the fi rst stage the objective is to produce variations of base and compound kinetics at the scale of a part, with the objective being to select distinctive compound kinetic types. The number of kinetic types selected in this fi rst stage will have a signifi cant impact on the total number of animations produced, and hence the most distinctive of the various twist, roll and yaw combinations will be selected. For the second stage of the experiment, a matrix will be used to produce an indexing of kinetic type and control variable. Stage 3 is reliant on a close review of this methodically produced index of type to control. From observation of the impact type has on pattern formation, one type will be selected to allow concentration on control variables. By intuitive manipulation of combinations
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of control variables, vague clustering may be thickened or gradient pattern disinte- grated. This third stage accepts the (desirable) human factor in this experiment and warps the initial methodical objectivity into the realms of intuitive design speculation.
[S1] Compound kinetics
The number of compound kinetic types can be reduced by considering the impact of orientation in three-dimensional space. The decision to select a fi xed orthographic viewing position means it can be anticipated that one of the coordinate variations will be redundant. The facade study will be viewed from an orthographic camera view perpendicular to the x,y plane, which negates the detection of movement in the Z coordinate. When the duplicate combinations are removed, this results in 18 possible compound types, many of which, however, are likely to be very similar. Through observation and intuitive experimentation, only the most distinctive kinetic types will be selected.