Including Efficient Object Recognition Capabilities in Online Robots: From a Statistical to a Neural-Network Classifier  

Oleh:

Sanz, Pedro J. ; Marin, Raul ; Sanchez, Jose S.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 35 no. 1 (Feb. 2005), page 87-96.
Topik: Incremental Learning; Neural Networks; Object Recognition; Online Robots.

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

For those situations in which the user wants to interact with the system by using, for example, voice commands, it would be convenient to refer to the objects by their names (e.g., “cube”) instead of other types of interactions (e.g., “grasp object 1”). Thus, automatic object recognition is the first step in order to acquire a higher level of interaction between the user and the robot. Nevertheless, applying object recognition techniques when the camera images are being transmitted through the web is not an easy task. In this situation, images cannot have a very high resolution, which affects enormously the recognition process due to the inclusion of more errors while digitalizing the real image. Some experiments with the Universitat Jaume I Online Robot evaluate the performance of different neural-network implementations, comparing it to that of some distance-based object recognition algorithms. Results will show which combination of object features, and algorithms (both statistical and neural networks) is more appropriate to our purpose in terms of both effectiveness and computing time.

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