Object recognition using spatiotemporal signatures
نویسندگان
چکیده
منابع مشابه
Object recognition using spatiotemporal signatures
The sequence of images generated by motion between observer and object specifies a spatiotemporal signature for that object. Evidence is presented that such spatiotemporal signatures are used in object recognition. Subjects learned novel, three-dimensional, rotating objects from image sequences in a continuous recognition task. During learning, the temporal order of images of a given object was...
متن کاملTimecourse of neural signatures of object recognition.
How long does it take for the human visual system to recognize objects? This issue is important for understanding visual cortical function as it places constraints on models of the information processing underlying recognition. We designed a series of event-related potential (ERP) experiments to measure the timecourse of electrophysiological correlates of object recognition. We find two distinc...
متن کاملSpatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition.
Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning...
متن کاملDecomposing the spatiotemporal signature in dynamic 3D object recognition.
The current study investigated the long-term representation of spatiotemporal signature (J. V. Stone, 1998) and its coding nature in a dynamic object recognition task. In Experiment 1, the observers' recognition performance was impaired by an overall reversal of the studied objects' learning view sequences even when they were unsmooth, suggesting that the spatiotemporal appearance of the object...
متن کاملObject recognition using eigenvectors
A method for object recognition and pose estimation for robotic bin picking is presented. The approach discussed is a variant on current approaches to eigenimage analysis. Compared to traditional approaches which use object geometry only (shape invariants), the implementation described uses the eigenspace determined by processing the eigenvalues and eigenvectors of the image set. The image set ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Vision Research
سال: 1998
ISSN: 0042-6989
DOI: 10.1016/s0042-6989(97)00301-5