01257nas a2200229   4500000000100000000000100001008004100002260001200043653002000055653002200075653001800097653002000115653001900135100002000154700003900174245005800213856009800271300001000369490000600379520062800385022001401013       2014                            d  c12/201410aComputer vision10aImage Recognition10aMobile Device10aGraphical Model10aNeural Network1 aWilliam Raveane1 aMaría Angélica González Arrieta00aNeural Networks through Shared Maps in Mobile Devices  uhttp://www.ijimai.org/journal/sites/default/files/files/2014/11/ijimai20143_1_4_pdf_46255.pdf  a28-350 v33 aWe introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition.  a1989-1660