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    • 73. 发明申请
    • Avocado skinning and pulping device
    • 鳄梨皮和制浆装置
    • US20130025474A1
    • 2013-01-31
    • US13443802
    • 2012-04-10
    • Richard Moore
    • Richard Moore
    • A23N7/08
    • A23N1/02A23N7/08A23N7/10
    • An Avocado Skinning and Pulping Device. The device uses a single cutting blade to cut through both the seed and meat of incoming avocados. The device has a feature that allows for the cutting blade to be sharpened while in operating condition (without the need for removing the blade from the machine). To increase cut uniformity, there are a series of guides, elements and other apparatus that will place the incoming avocados in a consistent orientation relative to the cutting blade before they are cut. The device includes a set of custom-shaped ramps designed to guide and transport the cut avocado halves down to the moving exit conveyor so that the halves land face-down on the exit conveyor. The disassembly and reassembly can be completed rapidly and without the need for additional tools or equipment.
    • 鳄梨皮和造纸设备。 该设备使用单个切割刀片切割进入的鳄梨的种子和肉类。 该装置具有允许切割刀片在操作状态下被磨削的特征(而不需要从机器上去除刀片)。 为了增加切割均匀性,有一系列的导向件,元件和其他装置,将进入的鳄梨在切割前相对于切割刀片将保持一致的方向。 该装置包括一组定制的斜面,其设计用于将切割的鳄梨半部引导和运送到移动的出口输送机,使得半部分在出口输送机上面朝下地落下。 拆卸和重新组装可以快速完成,无需额外的工具或设备。
    • 77. 发明申请
    • PROXY TRAINING DATA FOR HUMAN BODY TRACKING
    • 代码训练数据用于人体跟踪
    • US20110228976A1
    • 2011-09-22
    • US12727787
    • 2010-03-19
    • Andrew FitzgibbonJamie ShottonMat CookRichard MooreMark Finnochio
    • Andrew FitzgibbonJamie ShottonMat CookRichard MooreMark Finnochio
    • G06K9/62G06K9/00
    • G06K9/6256G06K9/00335G06K9/6206
    • Synthesized body images are generated for a machine learning algorithm of a body joint tracking system. Frames from motion capture sequences are retargeted to several different body types, to leverage the motion capture sequences. To avoid providing redundant or similar frames to the machine learning algorithm, and to provide a compact yet highly variegated set of images, dissimilar frames can be identified using a similarity metric. The similarity metric is used to locate frames which are sufficiently distinct, according to a threshold distance. For realism, noise is added to the depth images based on noise sources which a real world depth camera would often experience. Other random variations can be introduced as well. For example, a degree of randomness can be added to retargeting. For each frame, the depth image and a corresponding classification image, with labeled body parts, are provided. 3-D scene elements can also be provided.
    • 为身体关节跟踪系统的机器学习算法生成合成身体图像。 来自运动捕捉序列的帧被重定向到几种不同的身体类型,以利用运动捕捉序列。 为了避免向机器学习算法提供冗余或相似的帧,并且为了提供紧凑但高度变化的图像集合,可以使用相似性度量来识别不同的帧。 相似性度量用于根据阈值距离定位足够明显的帧。 对于现实主义,基于真实世界深度相机经常会遇到的噪声源,将噪声添加到深度图像。 也可以引入其他随机变化。 例如,可以添加一定程度的随机性来重定向。 对于每个帧,提供深度图像和具有标记的身体部分的相应分类图像。 也可以提供3-D场景元素。