公开(公告)号：US20130162871A1

公开(公告)日：2013-06-27

申请号：US13722949

申请日：2012-12-20

Applicant: STMicroelectronics S.r.l.

Inventor： Angelo BOSCO ,  Arcangelo Ranieri BRUNA ,  Davide GIACALONE

IPC: H04N5/217

CPC classification number: H04N5/2176 ,  G06T5/005 ,  G06T2207/10024 ,  G06T2207/20012 ,  H04N5/367 ,  H04N9/045

Abstract: Image defects in digital images are easily detectable by the human eye but may be difficult to detect in a computer-implemented fashion. In an embodiment of a digital-image-acquisition device, defects are removed on the CFA domain before color interpolation takes place. In order to allow cancellation of couplets of defective pixels, a two pass embodiment is presented. Such embodiment presents methods and systems that can remove both couplets and singlets without damaging the image. The system includes a ring corrector that detects a defect in the ring of pixels that surround a central pixel, a singlet corrector that detects and corrects the central pixel and removes a couplet if the ring corrector is activated, whereas if the ring corrector is switched off, the singlet corrector only removes singlets, and a peak-and-valley detector that avoids overcorrection by avoiding correcting signal peaks or valleys in case of spikes or drops in signal.

Abstract translation: 数字图像中的图像缺陷易于被人眼检测，但可能难以以计算机实现的方式检测。 在数字图像采集设备的实施例中，在进行颜色插值之前，在CFA域上去除缺陷。 为了允许消除对偶的有缺陷的像素，提出了两步实施例。 这种实施例提出了可以在不损坏图像的情况下移除对联和单个的方法和系统。 该系统包括环形校正器，其检测环绕中心像素的像素环中的缺陷;单校正器，其检测和校正中心像素，并且如果环形校正器被激活则去耦合，而如果环形校正器被关闭 ，单线校正器仅消除单峰，以及峰谷检测器，通过避免在信号峰值或信号下降时纠正信号峰值或谷值，避免过度校正。

公开(公告)号：US20140168516A1

公开(公告)日：2014-06-19

申请号：US14133381

申请日：2013-12-18

Applicant: STMicroelectronics S.R.L.

Inventor： Filippo NACCARI ,  Angelo BOSCO ,  Mirko GUARNERA

IPC: H04N9/31

CPC classification number: H04N9/3173 ,  H04N9/3182 ,  H04N9/3194 ,  H04N9/67

Abstract: An embodiment relates to a method for converting a digital image from a first color space to a second color space. The first color space is associated with an electronic source device, and the second color space is associated with a projection apparatus of digital images on a screen and coupled to the source device. The method includes: performing a first conversion on a first triad of parameters associated to the first color space by a first conversion matrix to map such a first triad in a third triad of parameters; the third triad is representative of a color space independent from the first and the second color spaces; performing a second conversion on the third triad of parameters by a second conversion matrix to map such a third triad of parameters in a second triad of parameters representative of the second color space. An embodiment of the step of performing the second conversion includes a step of computing the coefficients of the second conversion matrix based on at least one first piece of information representative of a variable distance between the projection apparatus and the screen.

Abstract translation: 实施例涉及将数字图像从第一颜色空间转换为第二颜色空间的方法。 第一颜色空间与电子源设备相关联，并且第二颜色空间与屏幕上的数字图像的投影设备相关联并耦合到源设备。 该方法包括：通过第一转换矩阵对与第一颜色空间相关联的参数的第一三元组执行第一转换，以将第三三元组的参数映射到第一三元组; 第三个三元组代表与第一和第二颜色空间无关的颜色空间; 通过第二转换矩阵在第三三元组参数上执行第二转换，以在表示第二颜色空间的参数的第二三元组中映射这样的第三三元组参数。 执行第二转换的步骤的实施例包括基于表示投影设备和屏幕之间的可变距离的至少一个第一信息来计算第二转换矩阵的系数的步骤。

公开(公告)号：US20190147338A1

公开(公告)日：2019-05-16

申请号：US16189264

申请日：2018-11-13

Applicant: STMICROELECTRONICS S.r.l.

Inventor： Danilo Pietro PAU ,  Emanuele PLEBANI ,  Fabio Giuseppe DE AMBROGGI ,  Floriana GUIDO ,  Angelo BOSCO

IPC: G06N3/08 ,  G06N3/04 ,  G06N5/00

Abstract: A neural network classifies an input signal. For example, an accelerometer signal may be classified to detect human activity. In a first convolutional layer, two-valued weights are applied to the input signal. In a first two-valued function layer coupled at input to an output of the first convolutional layer, a two-valued function is applied. In a second convolutional layer coupled at input to an output of the first two-valued functional layer, weights of the second convolutional layer are applied. In a fully-connected layer coupled at input to an output of the second convolutional layer, two-valued weights of the fully connected layer are applied. In a second two-valued function layer coupled at input to an output of the fully connected layer, a two-valued function of the second two-valued function layer is applied. A classifier classifies the input signal based on an output signal of second two-valued function layer.