Stereo techniques cannot easily recover reflecting and textureless surfaces by using traditional local regularizers. We propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image.
Deep Stroke-based Sketched Symbol Reconstruction and Segmentation
We propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature
This paper presents the second version of the IMOTION
system, a sketch-based video retrieval engine supporting multiple query
paradigms. For the second version, the functionality and the usability of the system have been improved.
Often, individuals with an Autism Spectrum Condition (ASC) have difficulties in interpreting verbal and non-verbal communication cues during social interactions. We develop a platform for children who have an ASC to learn emotion expression and recognition, through play in the virtual world.
DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn’t require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.