Content-adaptive pixel processing systems, methods and apparatus
    31.
    发明授权
    Content-adaptive pixel processing systems, methods and apparatus 有权
    内容自适应像素处理系统,方法和装置

    公开(公告)号:US09323988B2

    公开(公告)日:2016-04-26

    申请号:US14446031

    申请日:2014-07-29

    CPC classification number: G06K9/00536 G06T7/20 G06T2207/20012

    Abstract: Embodiments include methods and systems for context-adaptive pixel processing based, in part, on a respective weighting-value for each pixel or a group of pixels. The weighting-values provide an indication as to which pixels are more pertinent to pixel processing computations. Computational resources and effort can be focused on pixels with higher weights, which are generally more pertinent for certain pixel processing determinations.

    Abstract translation: 实施例包括部分地基于每个像素或一组像素的相应加权值的用于上下文自适应像素处理的方法和系统。 加权值提供关于哪些像素与像素处理计算更相关的指示。 计算资源和努力可以集中在具有较高权重的像素上,这通常与某些像素处理确定相关。

    SYSTEMS AND METHODS FOR OBJECT CLASSIFICATION, OBJECT DETECTION AND MEMORY MANAGEMENT
    32.
    发明申请
    SYSTEMS AND METHODS FOR OBJECT CLASSIFICATION, OBJECT DETECTION AND MEMORY MANAGEMENT 有权
    用于对象分类,对象检测和内存管理的系统和方法

    公开(公告)号:US20160063357A1

    公开(公告)日:2016-03-03

    申请号:US14609104

    申请日:2015-01-29

    CPC classification number: G06K9/6269 G06K9/4604 G06N7/00 G06N99/005

    Abstract: A method for object classification by an electronic device is described. The method includes obtaining an image frame that includes an object. The method also includes determining samples from the image frame. Each of the samples represents a multidimensional feature vector. The method further includes adding the samples to a training set for the image frame. The method additionally includes pruning one or more samples from the training set to produce a pruned training set. One or more non-support vector negative samples are pruned first. One or more non-support vector positive samples are pruned second if necessary to avoid exceeding a sample number threshold. One or more support vector samples are pruned third if necessary to avoid exceeding the sample number threshold. The method also includes updating classifier model weights based on the pruned training set.

    Abstract translation: 描述了一种通过电子设备进行物体分类的方法。 该方法包括获得包括对象的图像帧。 该方法还包括从图像帧确定样本。 每个样本表示多维特征向量。 该方法还包括将样本添加到图像帧的训练集合中。 该方法还包括从训练集修剪一个或多个样本以产生修剪的训练集。 首先修剪一个或多个非支持向量负样本。 如果必要,一个或多个非支持向量阳性样本被修剪,以避免超过样本数阈值。 如果需要,一个或多个支持向量样本被修剪为第三,以避免超过样本数阈值。 该方法还包括基于修剪的训练集更新分类器模型权重。

    SYSTEMS AND METHODS FOR MEMORY UTILIZATION FOR OBJECT DETECTION
    33.
    发明申请
    SYSTEMS AND METHODS FOR MEMORY UTILIZATION FOR OBJECT DETECTION 有权
    用于对象检测的存储器使用的系统和方法

    公开(公告)号:US20150058579A1

    公开(公告)日:2015-02-26

    申请号:US14468208

    申请日:2014-08-25

    CPC classification number: G06F12/0802 G06F2212/60 G06K9/00973

    Abstract: A method for memory utilization by an electronic device is described. The method includes transferring a first portion of a first decision tree and a second portion of a second decision tree from a first memory to a cache memory. The first portion and second portion of each decision tree are stored contiguously in the first memory. The first decision tree and second decision tree are each associated with a different feature of an object detection algorithm. The method also includes reducing cache misses by traversing the first portion of the first decision tree and the second portion of the second decision tree in the cache memory based on an order of execution of the object detection algorithm.

    Abstract translation: 描述了一种由电子设备进行存储器利用的方法。 该方法包括将第一决策树的第一部分和第二决策树的第二部分从第一存储器传送到高速缓冲存储器。 每个决策树的第一部分和第二部分被连续地存储在第一存储器中。 第一决策树和第二决策树各自与对象检测算法的不同特征相关联。 该方法还包括通过基于对象检测算法的执行顺序遍历高速缓冲存储器中的第一决策树的第一部分和第二决策树的第二部分来减少高速缓存未命中。

    AUTOMATED GRAPH-BASED PROGRAMMING
    34.
    发明申请
    AUTOMATED GRAPH-BASED PROGRAMMING 有权
    自动图形编程

    公开(公告)号:US20140359559A1

    公开(公告)日:2014-12-04

    申请号:US14034181

    申请日:2013-09-23

    CPC classification number: G06F8/35 G06F8/34 G06F8/42 G06F8/44

    Abstract: A method includes capturing an image of a scene that includes a diagram. The method further includes applying functional block recognition rules to image data of the image to recognize functional blocks of the diagram. The functional blocks include at least a first functional block associated with a first computer operation. The method further includes determining whether the functional blocks comply with functional block syntax rules. A functional graph is computer-generated based on the functional blocks complying with the functional block syntax rules. The functional graph corresponds to the diagram, and the functional graph includes the functional blocks.

    Abstract translation: 一种方法包括捕获包括图的场景的图像。 该方法还包括将功能块识别规则应用于图像的图像数据以识别该图的功能块。 功能块至少包括与第一计算机操作相关联的第一功能块。 该方法还包括确定功能块是否符合功能块语法规则。 基于符合功能块语法规则的功能块,计算机生成功能图。 功能图对应于图,功能图包括功能块。

    System and method for performing semantic image segmentation

    公开(公告)号:US12141981B2

    公开(公告)日:2024-11-12

    申请号:US17669040

    申请日:2022-02-10

    Abstract: Systems and techniques are provided for performing semantic image segmentation using a machine learning system (e.g., including one or more cross-attention transformer layers). For instance, a process can include generating one or more input image features for a frame of image data and generating one or more input depth features for a frame of depth data. One or more fused image features can be determined, at least in part, by fusing the one or more input depth features with the one or more input image features, using a first cross-attention transformer network. One or more segmentation masks can be generated for the frame of image data based on the one or more fused image features.

    FRAME-BASED VIDEO SEGMENTATION
    36.
    发明公开

    公开(公告)号:US20240233140A9

    公开(公告)日:2024-07-11

    申请号:US18049473

    申请日:2022-10-25

    Abstract: Methods and systems of frame based image segmentation are provided. For example, a method for feature object tracking between frames of video data is provided. The method comprises receiving a first frame of video data, extracting a mask feature for each of one or more objects of the first frame, adjusting the first frame by applying each initial mask and corresponding identification to a respective object of the first frame, and outputting the adjusted first frame. The method further comprises tracking the one or more objects in one or more consecutive frames. The tracking comprises extracting a masked feature for each of one or more objects in the consecutive frame, adjusting the consecutive frame by applying each initial mask and corresponding identification for the consecutive frame to the respective object of the one or more objects of the consecutive frame, and outputting the adjusted consecutive frame.

    Systems and methods for reconstructing a three-dimensional object

    公开(公告)号:US11158119B2

    公开(公告)日:2021-10-26

    申请号:US16738982

    申请日:2020-01-09

    Abstract: A method performed by an electronic device is described. The method includes receiving first optical data and first depth data corresponding to a first frame. The method also includes registering the first depth data to a first canonical model. The method further includes fitting a three-dimensional (3D) morphable model to the first optical data. The method additionally includes registering the 3D morphable model to a second canonical model. The method also includes producing a 3D object reconstruction based on the registered first depth data and the registered 3D morphable model.

    SYSTEMS AND METHODS FOR RECONSTRUCTING A THREE-DIMENSIONAL OBJECT

    公开(公告)号:US20210217228A1

    公开(公告)日:2021-07-15

    申请号:US16738982

    申请日:2020-01-09

    Abstract: A method performed by an electronic device is described. The method includes receiving first optical data and first depth data corresponding to a first frame. The method also includes registering the first depth data to a first canonical model. The method further includes fitting a three-dimensional (3D) morphable model to the first optical data. The method additionally includes registering the 3D morphable model to a second canonical model. The method also includes producing a 3D object reconstruction based on the registered first depth data and the registered 3D morphable model.

    PERSONALIZED EYE OPENNESS ESTIMATION
    40.
    发明申请

    公开(公告)号:US20200218878A1

    公开(公告)日:2020-07-09

    申请号:US16239352

    申请日:2019-01-03

    Abstract: Methods, systems, and devices for personalized (e.g., user specific) eye openness estimation are described. A network model (e.g., a convolutional neural network) may be trained using a set of synthetic eye openness image data (e.g., synthetic face images with known degrees or percentages of eye openness) and a set of real eye openness image data (e.g., facial images of real persons that are annotated as either open eyed or closed eyed). A device may estimate, using the network model, a multi-stage eye openness level (e.g., a percentage or degree to which an eye is open) of a user based on captured real time eye openness image data. The degree of eye openness estimated by the network model may then be compared to an eye size of the user (e.g., a user specific maximum eye size), and a user specific eye openness level may be estimated based on the comparison.

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