Roland William Fleming, FRSB (born 1978 in Oxford, UK) is a British and German interdisciplinary researcher specializing in the visual perception of objects and materials.
[13] In 2012, Fleming was awarded the Faculty Research Prize of the Justus Liebig University of Giessen (“Preis der Justus-Liebig-Universität Gießen”) for his work on the visual estimation of 3D shape from image orientations.
[25] His more recent studies on surface appearance have tested whether artificial neural networks can reproduce the patterns of errors and successes that human observers make when judging material properties.
[29] Fleming also led a number of studies on how the visual system infers the mechanical properties of materials, such as compliance,[30] elasticity,[31] and viscosity [32][33] [34] [35] from optical, shape and motion cues.
He and his colleagues have claimed that such representations facilitate disentangling intrinsic material properties from other factors that also contribute to the proximal stimulus[38][34] such as a flowing liquid’s speed, or the force deforming a compliant solid.
[40][41] A proof-of-concept of this theory was demonstrated by training an unsupervised artificial neural network model on a dataset of computer rendered images of bumpy, glossy surfaces.
[42] Fleming and his colleagues found that the model spontaneously learned to disentangle scene variables—such as lighting and surface reflectance—even though it was given no explicit information about the true values of these variables.
[45][43][44][49][46] Fleming and his colleagues have shown that local image orientation signals tend to vary smoothly across curved surfaces in ways that are systematically related to 3D shape properties.
[10] Fleming led a number of studies on how the visual system makes inferences about the processes and transformations that have formed objects or altered their shape.
[58][59][60][61][62][63][64] A recurring theme within this body of work is that an object’s ‘causal history’ leaves traces in its shape, which can be used to identify which of its features are the result of shape-altering transformations.
[66][67] In this context, Fleming and colleagues developed a computational model for predicting the perceived similarity between pairs of two-dimensional (2D) shapes, called ‘ShapeComp’.
[66][67] They have proposed that this involves segmenting the object into parts, and representing their relations in a way that can be modified to synthesize novel variants belonging to the same category as the exemplar.
[74][75] He co-authored a text book entitled “Visual Perception from a Computer Graphics Perspective”[76] Fleming’s work on motor control has focused primarily on the effects of 3D shape[77][78][79] and material properties[78][80][81]—including mass,[80][81] friction[80][82] and rigidity[83]—on grasping.
He and his colleagues developed a computational model for predicting human precision grip (thumb and forefinger) grasp locations on objects with varying 3D shape and materials properties.
His research group has developed methods for measuring the contact regions between hands and objects to capture unconstrained, whole-hand grasping behavior.