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    Reduced complexity convolution for convolutional neural networks
    51.
    发明授权
    Reduced complexity convolution for convolutional neural networks 有权

    公开(公告)号：US11048997B2

    公开(公告)日：2021-06-29

    申请号：US15800294

    申请日：2017-11-01

    Applicant: Texas Instruments Incorporated

    Inventor： Manu Mathew ,  Kumar Desappan ,  Pramod Kumar Swami

    IPC: G06N3/04 ,  G06N3/08 ,  G06F7/544 ,  G06F17/15 ,  G06N3/10

    Abstract: A method for convolution in a convolutional neural network (CNN) is provided that includes accessing a coefficient value of a filter corresponding to an input feature map of a convolution layer of the CNN, and performing a block multiply accumulation operation on a block of data elements of the input feature map, the block of data elements corresponding to the coefficient value, wherein, for each data element of the block of data elements, a value of the data element is multiplied by the coefficient value and a result of the multiply is added to a corresponding data element in a corresponding output block of data elements comprised in an output feature map.

    WINDOW GROUPING AND TRACKING FOR FAST OBJECT DETECTION
    52.
    发明申请
    WINDOW GROUPING AND TRACKING FOR FAST OBJECT DETECTION 审中-公开

    公开(公告)号：US20200226415A1

    公开(公告)日：2020-07-16

    申请号：US16836077

    申请日：2020-03-31

    Applicant: Texas Instruments Incorporated

    Inventor： Manu Mathew ,  Soyeb Noormohammed Nagori ,  Shyam Jagannathan

    IPC: G06K9/62

    Abstract: Disclosed examples include image processing methods and systems to process image data, including computing a plurality of scaled images according to input image data for a current image frame, computing feature vectors for locations of the individual scaled images, classifying the feature vectors to determine sets of detection windows, and grouping detection windows to identify objects in the current frame, where the grouping includes determining first clusters of the detection windows using non-maxima suppression grouping processing, determining positions and scores of second clusters using mean shift clustering according to the first clusters, and determining final clusters representing identified objects in the current image frame using non-maxima suppression grouping of the second clusters. Disclosed examples also include methods and systems to track identified objects from one frame to another using feature vectors and overlap of identified objects between frames to minimize computation intensive operations involving feature vectors.

    INTRA/INTER MODE DECISION FOR PREDICTIVE FRAME ENCODING
    56.
    发明申请
    INTRA/INTER MODE DECISION FOR PREDICTIVE FRAME ENCODING 审中-公开

    公开(公告)号：US20170142411A1

    公开(公告)日：2017-05-18

    申请号：US15419512

    申请日：2017-01-30

    Applicant: Texas Instruments Incorporated

    Inventor： Soyeb Nagori ,  Manu Mathew ,  Pramod Kumar Swami

    IPC: H04N19/107 ,  H04N19/176

    CPC classification number: H04N19/107 ,  H04N19/147 ,  H04N19/176 ,  H04N19/50

    Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

    Methods and systems for filtering noise in video data
    57.
    发明授权
    Methods and systems for filtering noise in video data 有权
    用于滤波视频数据中的噪声的方法和系统

    公开(公告)号：US08665376B2

    公开(公告)日：2014-03-04

    申请号：US13857443

    申请日：2013-04-05

    Applicant: Texas Instruments Incorporated

    Inventor： Manu Mathew ,  Shyam Jagannathan ,  Soyeb Noomohammed Nagori ,  Hrushikesh Tukaram Garud

    IPC: H04N5/21

    CPC classification number: H04N5/21 ,  G06T5/002 ,  G06T5/50 ,  G06T2207/10016 ,  H04N19/51 ,  H04N19/567 ,  H04N19/573 ,  H04N19/577 ,  H04N19/85

    Abstract: Several systems and methods for filtering noise from a picture in a picture sequence associated with video data are disclosed. In an embodiment, the method includes accessing a plurality of pixel blocks associated with the picture and filtering noise from at least one pixel block from among the plurality of pixel blocks. The filtering of noise from a pixel block from among the at least one pixel block includes identifying pixel blocks corresponding to the pixel block in one or more reference pictures associated with the picture sequence. Each identified pixel block is associated with a cost value. One or more pixel blocks are selected from among the identified pixel blocks based on associated cost values. Weights are assigned to the selected one or more pixel blocks and set of filtered pixels for the pixel block is generated based on the weights.

    Abstract translation: 公开了用于从与视频数据相关联的图像序列中的图像中滤除噪声的几种系统和方法。 在一个实施例中，该方法包括访问与图像相关联的多个像素块，并从多个像素块中的至少一个像素块滤除噪声。 从所述至少一个像素块中的像素块的噪声的滤波包括​​识别与所述图像序列相关联的一个或多个参考图像中与所述像素块相对应的像素块。 每个识别的像素块与成本值相关联。 基于相关联的成本值，从所识别的像素块中选择一个或多个像素块。 将权重分配给所选择的一个或多个像素块，并且基于权重生成用于像素块的滤波像素的集合。

    COMBINED PREDICTION WITH VARIABLE WEIGHT IN SCALABLE EXTENSION OF HEVC
    58.
    发明申请
    COMBINED PREDICTION WITH VARIABLE WEIGHT IN SCALABLE EXTENSION OF HEVC 有权
    HEV的可扩展重量的组合预测

    公开(公告)号：US20130271664A1

    公开(公告)日：2013-10-17

    申请号：US13857443

    申请日：2013-04-05

    Applicant: TEXAS INSTRUMENTS INCORPORATED

    Inventor： Manu Mathew ,  Shyam Jagannathan ,  Soyeb Noomohammed Nagori ,  Hrushikesh Tukaram Garud

    IPC: H04N5/21

    CPC classification number: H04N5/21 ,  G06T5/002 ,  G06T5/50 ,  G06T2207/10016 ,  H04N19/51 ,  H04N19/567 ,  H04N19/573 ,  H04N19/577 ,  H04N19/85

    Abstract: Several systems and methods for filtering noise from a picture in a picture sequence associated with video data are disclosed. In an embodiment, the method includes accessing a plurality of pixel blocks associated with the picture and filtering noise from at least one pixel block from among the plurality of pixel blocks. The filtering of noise from a pixel block from among the at least one pixel block includes identifying pixel blocks corresponding to the pixel block in one or more reference pictures associated with the picture sequence. Each identified pixel block is associated with a cost value. One or more pixel blocks are selected from among the identified pixel blocks based on associated cost values. Weights are assigned to the selected one or more pixel blocks and set of filtered pixels for the pixel block is generated based on the weights.

    Abstract translation: 公开了用于从与视频数据相关联的图像序列中的图像中滤除噪声的几种系统和方法。 在一个实施例中，该方法包括访问与图像相关联的多个像素块，并从多个像素块中的至少一个像素块滤除噪声。 从所述至少一个像素块中的像素块的噪声的滤波包括​​识别与所述图像序列相关联的一个或多个参考图像中与所述像素块相对应的像素块。 每个识别的像素块与成本值相关联。 基于相关联的成本值，从所识别的像素块中选择一个或多个像素块。 将权重分配给所选择的一个或多个像素块，并且基于权重生成用于像素块的滤波像素的集合。

    SYSTEMS AND METHODS FOR IDENTIFYING SCALING FACTORS FOR DEEP NEURAL NETWORKS
    60.
    发明公开
    SYSTEMS AND METHODS FOR IDENTIFYING SCALING FACTORS FOR DEEP NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240036816A1

    公开(公告)日：2024-02-01

    申请号：US18193396

    申请日：2023-03-30

    Applicant: TEXAS INSTRUMENTS INCORPORATED

    Inventor： Kumar Desappan ,  Anshu Jain ,  Manu Mathew

    IPC: G06F5/01 ,  G06F7/485

    CPC classification number: G06F5/012 ,  G06F7/485

    Abstract: Disclosed herein are systems and methods for determining the scaling factors for a neural network that satisfy the activation functions employed by the nodes of the network. A processor identifies a saturation point of an activation function. Next, the processor determines a scaling factor for an output feature map based on the saturation point of the activation function. Then, the processor determines a scaling factor for an accumulator based on the scaling for the output feature map and further based on a shift value related to a quantization. Finally, the processor determines a scaling factor for a weight map based on the scaling factor for the accumulator.

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