Using Motion and Internal Supervision in Object Recognition
This work addresses the problem of improving unsupervised visual learning for dynamic scenes, potentially benefiting computer vision systems, but it appears incremental as it builds on existing motion-based approaches.
The thesis tackled object recognition by using motion information and internal supervision for unsupervised learning, demonstrating adaptive detection of object-parts and methods for recognizing hands and extracting gaze direction in dynamic scenes.
In this thesis we address two related aspects of visual object recognition: the use of motion information, and the use of internal supervision, to help unsupervised learning. These two aspects are inter-related in the current study, since image motion is used for internal supervision, via the detection of spatiotemporal events of active-motion and the use of tracking. Most current work in object recognition deals with static images during both learning and recognition. In contrast, we are interested in a dynamic scene where visual processes, such as detecting motion events and tracking, contribute spatiotemporal information, which is useful for object attention, motion segmentation, 3-D understanding and object interactions. We explore the use of these sources of information in both learning and recognition processes. In the first part of the work, we demonstrate how motion can be used for adaptive detection of object-parts in dynamic environments, while automatically learning new object appearances and poses. In the second and main part of the study we develop methods for using specific types of visual motion to solve two difficult problems in unsupervised visual learning: learning to recognize hands by their appearance and by context, and learning to extract direction of gaze. We use our conclusions in this part to propose a model for several aspects of learning by human infants from their visual environment.