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The image retrieval and recognition are thus done simultaneously, in a single step, and it is possible to search for thousands of targets with extraordinary efficiency.This approach could also be useful in conventional data retrieval, for finding records that match particular criteria.
In recent years, CNN-based methods have performed well in image retrieval and recognition tasks.
This kernel was successfully used as a similarity measure for image retrieval and recognition tasks [38, 39].
The CVLT is an established test of verbal learning and of recall (encoding and retrieval) and recognition of the learned content.
By adopting smart choices in gesture traces processing and comparing, it was possible to obtain very good results in the retrieval and recognition of simple command gestures, from complete or even partial hand trajectories.
These two dimensions (i.e. concreteness and imageability; [41]) are known to affect word retrieval and recognition in both healthy and brain-damaged individuals [42], [43]: the more concrete and imaginable the word is, the faster it will be processed.
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The RAVLT consists of word list learning, spontaneous retrieval, and recognition-type retrieval with the aid of written word lists.
Fuzzy logic theory is widely adopted in various fields, such as speech recognition, image retrieval and pattern recognition.
In recent years, CNN features have achieved outstanding performance in image retrieval, classification, and recognition.
It suggests that subjects who slept during the post-training night have engaged more cerebral areas known for their contribution in active retrieval [42] and recognition processes [37] in goal-oriented navigation than sleep-deprived subjects.
However, the process of data retrieval, process and recognition is the foundation for effective communication and order entry and thus we felt a logical place to begin.
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