Faith C Robertson, Christopher J Stapleton, Jean-Valery C E Coumans, Federico Nicolosi, Manuela Vooijs, Sarah Blitz, Francesco Guerrini, Giannantonio Spena, Carlo Giussani, Cesare Zoia, Brian V Nahed
Applying objective metrics to neurosurgical skill development with simulation and spaced repetition learning
Journal of neurosurgery
paper address tasks detecting segmenting parsing matching deformable objects use novel probabilistic object model call hierarchical deformable template hdt hdt represents object state variables defined hierarchy typically five levels hierarchy built recursively composing elementary structures form complex structures probability distributiona parameterized exponential modelis defined hierarchy quantify variability shape appearance object multiple scales perform inferenceto estimate probable states hierarchy input imagewe use bottomup algorithm called compositional inference algorithm approximate version dynamic programming approximations made eg pruning ensure algorithm fast maintaining high performance adapt structureperceptron algorithm estimate parameters hdt discriminative manner simultaneously estimating appearance shape parameters precisely specify exponential distribution hdt using dictionary potentials capture appearance shape cues dictionary large require handcrafting potentials instead structureperceptron assigns weights potentials less important potentials receive small weights like soft form feature selection finally provide experimental evaluation hdts different visual tasks including detection segmentation matching alignment parsing show hdts achieve stateoftheart performance different tasks evaluated data sets groundtruth compared alternative algorithms typically specialized task