Recognition of banknotes is a prevalent problem for blind and visually impaired people. The bionic eyeglass is a mobile platform that integrates several common visual detection and recognition functions. Relevant shapes are extracted from the image flow of the banknote shown to the mobile camera using adaptive thresholding and morphological shape filters. In a twolevel classification scheme different classifiers are used for different categories of patches (portrait, denomination, tactile marks), and the votes are combined through an ensemble decider. The confidence of the decision is established if the same class recurs through several frames. The system has been tested on blind subjects using a cell phone based prototype, and the results showed that they quickly became confident using it.
An Associative Memory is built by three consecutive components: (1) a CMOS preprocessing unit generating input feature vectors from picture inputs, (2) an AM cluster generating signature outputs composed of spintronic oscillator (STO) cells and local spin-wave interactions, as an oscillatory CNN (O-CNN) array unit, applied several times arranged in space, and (3) a classification unit (CMOS). The end to end design of the preprocessing unit, the interacting O-CNN arrays, and the classification unit is embedded in a learning and optimization procedure where the geometric distances between the STOs in the O-CNN arrays play a crucial role. The O-CNN array has an input vector as a 1D array of oscillator frequencies, and the synchronized O-CNN array codes the output as the phases of the output 1D array. The typical O-CNN array has 1-3 rows of STOs. Simplified STO and interaction macro models are used. A typical example is shown using an End-to-end Simulator.
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