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This special issue presents two novel methods for recognition of individuals based on sample signatures.
Now for better comparison, the results of counterpart methods for recognition and also dataset used are presented in Table 2.
Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images.
Extensive experimental results illustrate that the proposed deep learning approach outperforms state-of-the-art methods for recognition of cattle on muzzle point image database.
Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition.
These results are contrasted with benchmark methods for recognition in a systematic validation revealing, in particular, an improved performance for mixture model prediction utilizing segments.
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This section explains the proposed method for recognition.
Xu et al. [13] proposed a new method for recognition.
This paper presents a method for recognition of Activities of Daily Living (ADL) in smart homes.
We present a method for recognition of agglomerates in images acquired during the coating process of pharmaceutical pellets.
Based on this fluorescence switch characteristics of PMTH, a new method for recognition and determination of SAM was established.
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