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1.发明授权Object-of-interest detection and recognition with split, full-resolution image processing pipeline 有权利用分割，全分辨率图像处理流水线的感兴趣物体检测和识别公开(公告)号：US09251431B2
公开(公告)日：2016-02-02
申请号：US14292776
申请日：2014-05-30
Applicant: Apple Inc.
Inventor： Frank Doepke , Kevin Hunter
CPC classification number: H04N5/23245 , G06K9/00449 , G06K9/00463 , G06K9/4604 , G06K2009/363
Abstract: Differing embodiments of this disclosure may employ one or all of the several techniques described herein to utilize a “split” image processing pipeline, wherein one part of the “split” image processing pipeline runs an object-of-interest recognition algorithm on scaled down (also referred to herein as “low-resolution”) frames received from a camera of a computing device, while the second part of the “split” image processing pipeline concurrently runs an object-of-interest detector in the background on full resolution (also referred to herein as “high-resolution”) image frames received from the camera. If the object-of-interest detector detects an object-of-interest that can be read, it then crops the object-of-interest out of the “high-resolution” camera buffer, optionally performs a perspective correction, and/or scaling on the object-of-interest to make it the desired size needed by the object-of-interest recognition algorithm, and then sends the scaled, high-resolution representation of the object-of-interest to the object-of-interest recognition algorithm for further processing.
Abstract translation: 本公开的不同实施例可以使用本文所描述的几种技术中的一种或全部来利用“分割”图像处理流水线，其中“分割”图像处理流水线的一部分按缩小的方式运行兴趣对象识别算法（ 这里也称为“低分辨率”）帧，而“分割”图像处理流水线的第二部分同时在全分辨率的背景下运行感兴趣的检测器（也称为“低分辨率”） 这里称为“高分辨率”）从相机接收的图像帧。 如果感兴趣的感兴趣的检测器检测到可以读取的感兴趣的物体，那么它然后将感兴趣的物体从“高分辨率”相机缓冲器中进行裁剪，可选地执行透视校正和/或缩放 利益感兴趣的目标，使其成为目标感兴趣识别算法所需的大小，然后将感兴趣的目标的缩放的高分辨率表示发送到感兴趣的对象识别算法 进一步处理。
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2.发明申请Object-Of-Interest Detection And Recognition With Split, Full-Resolution Image Processing Pipeline 有权利用分割，全分辨率图像处理流水线的感兴趣的检测和识别公开(公告)号：US20150347861A1
公开(公告)日：2015-12-03
申请号：US14292776
申请日：2014-05-30
Applicant: APPLE INC.
Inventor： Frank Doepke , Kevin Hunter
CPC classification number: H04N5/23245 , G06K9/00449 , G06K9/00463 , G06K9/4604 , G06K2009/363
Abstract: Differing embodiments of this disclosure may employ one or all of the several techniques described herein to utilize a “split” image processing pipeline, wherein one part of the “split” image processing pipeline runs an object-of-interest recognition algorithm on scaled down (also referred to herein as “low-resolution”) frames received from a camera of a computing device, while the second part of the “split” image processing pipeline concurrently runs an object-of-interest detector in the background on full resolution (also referred to herein as “high-resolution”) image frames received from the camera. If the object-of-interest detector detects an object-of-interest that can be read, it then crops the object-of-interest out of the “high-resolution” camera buffer, optionally performs a perspective correction, and/or scaling on the object-of-interest to make it the desired size needed by the object-of-interest recognition algorithm, and then sends the scaled, high-resolution representation of the object-of-interest to the object-of-interest recognition algorithm for further processing.
Abstract translation: 本公开的不同实施例可以使用本文所描述的几种技术中的一种或全部来利用“分割”图像处理流水线，其中“分割”图像处理流水线的一部分按缩小的方式运行兴趣对象识别算法（ 这里也称为“低分辨率”）帧，而“分割”图像处理流水线的第二部分同时在全分辨率的背景下运行感兴趣的检测器（也称为“低分辨率”） 这里称为“高分辨率”）从相机接收的图像帧。 如果感兴趣的感兴趣的检测器检测到可以读取的感兴趣的物体，那么它然后将感兴趣的物体从“高分辨率”相机缓冲器中进行裁剪，可选地执行透视校正和/或缩放 利益感兴趣的目标，使其成为目标感兴趣识别算法所需的大小，然后将感兴趣的目标的缩放的高分辨率表示发送到感兴趣的对象识别算法 进一步处理。
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