We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (Learning Object Classes with Unsupervised Segmentation...
— While robot mapping has seen massive strides , higher level abstractions in map representation are still not widespread. Maps containing semantic concepts such as objects and l...
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
Model-based recognition of an object typically involves matching dense 3D range data. The computational cost is directly affected by the amount of data of which a transformation n...
We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts' appearance...