Look Further to Recognize Better: Learning Shared Topics and CategorySpecific Dictionaries for OpenEnded 3D Object Recognition Service robots are expected to operate effectively in humancentric environments for long periods of time. In such realistic scenarios, finegrained object categorization is as important as basiclevel object categorization. We tackle this problem by proposing an openended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and categoryspecific dictionaries. The general topics encode the common patterns of all categories, while the categoryspecific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous stateoftheart approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a realworld setting. Regarding computation time, the best result was obtained with a BagofWords method followed by a variant of the Latent Dirichlet Allocation approach.

Object Proposal Algorithms in the Wild: Are they Generalizable to Robot Perception? The recent emergence of object proposal algorithms in the computer vision community shows great promise to addressing difficult problems in robotic such as object discovery and salient object detection. However, it is difficult to determine how these algorithms actually perform for realworld robot vision applications, because the standard evaluation protocol uses datasets which do not adequately account for realworld noise (motion blur, occlusion, etc.). We evaluated several stateoftheart object proposal algorithms using naturalistic datasets from the robotics community, and found a substantial performance drop across all algorithms. This suggests that many object proposal algorithms are not as generalizable as the computer vision literature purports, which can have a significant impact on how they are applied to robotics. We also conducted a study on how each algorithm is influenced by specific kinds of realworld robot vision challenges, including variable brightness, gamma correction, Gaussian blur, and Gaussian noise. Our results provide insight into certain weaknesses of object proposal algorithms, which can be used to gauge how they might be suitable for different robotics applications. It is our intent that this work will motivate future research about how to design more flexible and robust object proposal algorithms for the robotics community.