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    • 1. 发明专利
    • Image processing
    • GB201616402D0
    • 2016-11-09
    • GB201616402
    • 2016-09-27
    • APICAL LIMITED AND UNIVERSITY OF LEICESTER
    • A method including receiving an indication that a first classifier has identified that an image includes an object of a predetermined class of objects. Image data that relates to the image is processed using a second classifier with a first training state, which determines whether the image data includes the object of the predetermined class of objects. In response to the determining, data relating to the image data is transmitted to a remote system. Update data relating to the transmitted data is received from the remote system. The training state of the second classifier is updated to a second training state in response to the update data such that the second classifier with the second training state would make a different determination of whether future image data similar to the image data includes an object of the predetermined class of objects than the second classifier with the first training state.
    • 5. 发明申请
    • HYBRID MACHINE LEARNING SYSTEMS
    • 混合机器学习系统
    • WO2017009396A1
    • 2017-01-19
    • PCT/EP2016/066698
    • 2016-07-13
    • APICAL LIMITED
    • ROMANENKO, Ilya
    • G06K9/46G06K9/40G06K9/34
    • G06T5/002G06K9/00288G06K9/4609G06K9/4628G06K9/4652G06K9/627G06K9/6286G06K9/6288G06N3/0454G06T7/10G06T7/44G06T2207/20081G06T2207/20084
    • A hybrid machine learning system (100, 200, 300, 400, 500, 600, 700, 800) is for processing image data (110, 210, 310, 410, 510, 610, 710, 810) obtained from an image sensor (105, 205, 305, 405, 505, 605, 705, 805). The system comprises a front end (115, 215, 315, 415, 515, 615, 715, 815) comprising one or more hard-coded filters (120, 220, 320, 420, 520, 620, 720, 820). Each of the one or more hard-coded filters is arranged to perform a set task. The system also comprises a neural network (125, 225, 325, 425, 525, 625, 725, 825) arranged to receive and process output from the front end. The one or more hard-coded filters include one or more hard-coded noise compensation filters that are hard-coded to compensate for a noise profile of the image sensor from which the image data is obtained.
    • 混合机器学习系统(100,200,300,400,500,600,700,800)用于处理从图像传感器获得的图像数据(110,210,310,410,510,610,710,810) 105,205,305,405,505,605,705,805)。 该系统包括包括一个或多个硬编码滤波器(120,220,320,420,520,620,720,820)的前端(115,215,315,415,515,615,715,815)。 一个或多个硬编码滤波器中的每一个被布置成执行设定的任务。 该系统还包括布置成从前端接收和处理输出的神经网络(125,225,325,425,525,675,725,825)。 一个或多个硬编码滤波器包括一个或多个硬编码噪声补偿滤波器,其被硬编码以补偿获得图像数据的图像传感器的噪声分布。