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    • 98. 发明授权
    • Smoke and fire detection in aircraft cargo compartments
    • 飞机货舱中的烟雾和火灾探测
    • US07688199B2
    • 2010-03-30
    • US11555992
    • 2006-11-02
    • Wei ZhangChao-Hsin Lin
    • Wei ZhangChao-Hsin Lin
    • G08B1/08G08B21/00G08B25/00
    • G08B17/00A62C3/08G08B31/00
    • A detection system may include at least one sensor located in an enclosable space, each sensor being configured to detect at least one environmental feature and provide a corresponding at least one environmental feature signal. The system may process the at least one environmental feature signal and provide at least one processed feature signal, the at least one processed feature signal corresponding to a transformed at least one environmental feature signal. The system may further provide a hosted function configured to provide instructions for processing, the hosted function comprising a computational algorithm adapted to perform numerical transformation operations based on the at least one environmental feature signal, the hosted function being configured to provide a map image based on the at least one processed feature signal.
    • 检测系统可以包括位于可封闭空间中的至少一个传感器,每个传感器被配置成检测至少一个环境特征并提供对应的至少一个环境特征信号。 所述系统可以处理所述至少一个环境特征信号并提供至少一个经处理的特征信号,所述至少一个经处理的特征信号对应于经变换的至少一个环境特征信号。 所述系统还可以提供被配置为提供用于处理的指令的托管功能,所述托管功能包括适于基于所述至少一个环境特征信号执行数字变换操作的计算算法,所述托管功能被配置为基于 所述至少一个经处理的特征信号。
    • 99. 发明申请
    • LEARNING-BASED PARTIAL DIFFERENTIAL EQUATIONS FOR COMPUTER VISION
    • 用于计算机视觉的基于学习的部分差分方程
    • US20100074551A1
    • 2010-03-25
    • US12235488
    • 2008-09-22
    • Zhouchen LinWei Zhang
    • Zhouchen LinWei Zhang
    • G06K9/40
    • G06K9/40G06T5/001G06T7/10G06T2207/20081
    • Partial differential equations (PDEs) are used in the invention for various problems in computer the vision space. The present invention provides a framework for learning a system of PDEs from real data to accomplish a specific vision task. In one embodiment, the system consists of two PDEs. One controls the evolution of the output. The other is for an indicator function that helps collect global information. Both PDEs are coupled equations between the output image and the indicator function, up to their second order partial derivatives. The way they are coupled is suggested by the shift and rotational invariance that the PDEs should hold. The coupling coefficients are learnt from real data via an optimal control technique. The invention provides learning-based PDEs that make a unified framework for handling different vision tasks, such as edge detection, denoising, segementation, and object detection.
    • 局部微分方程(PDE)用于本发明的计算机视觉空间中的各种问题。 本发明提供了一种用于从实际数据学习PDE系统以完成特定视觉任务的框架。 在一个实施例中,系统由两个PDE组成。 一个控制输出的演变。 另一个是用于帮助收集全球信息的指标功能。 两个PDE是输出图像和指示符函数之间的耦合方程,直到它们的二阶偏导数。 它们耦合的方式是由PDE应该保持的移动和旋转不变性来提出的。 通过最优控制技术从实数数据中学习耦合系数。 本发明提供了基于学习的PDE,其构成用于处理不同视觉任务的统一框架,例如边缘检测,去噪,分割和对象检测。
    • 100. 发明授权
    • Processing medical image information to detect anatomical abnormalities
    • 处理医学图像信息以检测解剖异常
    • US07672494B2
    • 2010-03-02
    • US11419980
    • 2006-05-23
    • Takeshi DoiWei Zhang
    • Takeshi DoiWei Zhang
    • G06K9/00A61B6/04
    • G06T7/0012G06T2207/30068
    • A method, system, and related computer program products are provided for processing a medical image of a body part according to a computer-aided detection (CAD) algorithm, the medical image having an image border, the body part comprising imaged tissue appearing inside the image border and outlying tissue not appearing in the medical image, wherein likely anatomical abnormalities in the outlying tissue near the imaged tissue border are detected by the CAD algorithm. In one example, the detected likely abnormalities in the outlying tissue are located within a first distance from the imaged tissue border, wherein the first distance corresponds to a spatial ambit of a neighborhood-based feature computed by the CAD algorithm.
    • 提供了一种根据计算机辅助检测(CAD)算法来处理身体部位的医学图像的方法,系统和相关的计算机程序产品,所述医学图像具有图像边界,所述身体部分包括出现在内部的成像组织 图像边界和不存在于医学图像中的外围组织,其中通过CAD算法检测在成像组织边界附近的外围组织中可能的解剖异常。 在一个示例中,所检测到的外围组织中可能的异常位于距成像组织边界的第一距离内,其中第一距离对应于由CAD算法计算的基于邻域的特征的空间范围。