Abstract: A device including at least one medical electrode; a radio-frequency (RF) tag with an identifier (ID) that identifies the at least one medical electrode; and at least one substrate that attaches the at least one medical electrode to the RF tag.

Abstract: A device including at least one medical electrode; a radio-frequency (RF) tag with an identifier (ID) that identifies the at least one medical electrode; and at least one substrate that attaches the at least one medical electrode to the RF tag.

Abstract: A device including at least one medical electrode; a radio-frequency (RF) tag with an identifier (ID) that identifies the at least one medical electrode; and at least one substrate that attaches the at least one medical electrode to the RF tag.

Abstract: A method for classifying objects from one or more images comprising generating a trained classification process and using the trained classification process to classify objects in the images. Generating the trained classification process can include extracting features from one or more training images and clustering the features into one or more groups of features termed visual words; storing data for each of the visual words, including color and texture information, as descriptor vectors; and generating a vocabulary tree to store clusters of visual words with common characteristics. Using the trained classification process to classify objects can include extracting features from the images and clustering the features into groups of features termed visual words; searching the vocabulary tree to determine the closest matching clusters of visual words; and classifying objects based on the closest matching clusters of visual words in the vocabulary tree.

Abstract: A method for classifying objects from one or more images comprising generating a trained classification process and using the trained classification process to classify objects in the images. Generating the trained classification process can include extracting features from one or more training images and clustering the features into one or more groups of features termed visual words; storing data for each of the visual words, including colour and texture information, as descriptor vectors; and generating a vocabulary tree to store clusters of visual words with common characteristics. Using the trained classification process to classify objects can include extracting features from the images and clustering the features into groups of features termed visual words; searching the vocabulary tree to determine the closest matching clusters of visual words; and classifying objects based on the closest matching clusters of visual words in the vocabulary tree.