1/9/2023 0 Comments Pictorial modelThis likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation. This enables image likelihood models that are more discriminative than previous PS likelihoods. A key advantage of such a model is that it more accurately models object boundaries. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. Download scientific diagram Pictorial Model of Construct Validity from publication: Validation Guidelines for IS Positivist Research The issue of. Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Yet these models have not been shown to lead to state-of-the-art performance in human pose estimation, likely because they rely on a pose representation that is.
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