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    • 2. 发明申请
    • METHOD FOR DECODING A BITSTREAM USING INTRA BLOCK COPY WITH EXTRA CACHE MEMORY
    • 使用超高速缓存存储器进行块内复制的方法
    • WO2015137113A1
    • 2015-09-17
    • PCT/JP2015/055307
    • 2015-02-18
    • MITSUBISHI ELECTRIC CORPORATION
    • COHEN, Robert, A.VETRO, AnthonySUGIMOTO, Kazuo
    • H04N19/593H04N19/42
    • H04N19/423H04N19/593
    • A method decodes blocks in pictures of a video in an encoded bitstream by storing previously decoded blocks in a buffer. The previously decoded blocks are displaced less than a predetermined range relative to a current block being decoded. Cached blocks are maintained in a cache. The cached blocks include a set of best matching previously decoded blocks that are displaced greater than the predetermined range relative to the current block. The bitstream is parsed to obtain a prediction indicator that determines whether the current block is predicted from the previously decoded blocks in the buffer or the cached blocks in the cache. Based on the prediction indicator, a prediction residual block is generated, and in a summation process, the prediction residual block is added to a reconstructed residual block to form a decoded block as output.
    • 一种方法通过将先前解码的块存储在缓冲器中来解码编码比特流中视频的图像中的块。 先前解码的块相对于被解码的当前块移位小于预定范围。 缓存块保存在缓存中。 缓存的块包括相对于当前块移位大于预定范围的一组最佳匹配的先前解码的块。 对比特流进行解析以获得预测指示符,该预测指示符根据缓冲器中的先前解码的块或高速缓存中的高速缓存块来确定当前块是否被预测。 基于预测指标,生成预测残差块,在求和处理中,将预测残差块加到重构残差块,形成解码块作为输出。
    • 6. 发明申请
    • METHOD FOR COMPRESSING POINT CLOUD
    • 压缩点云的方法
    • WO2017126314A1
    • 2017-07-27
    • PCT/JP2016/089223
    • 2016-12-21
    • MITSUBISHI ELECTRIC CORPORATION
    • COHEN, RobertTIAN, DongVETRO, Anthony
    • G06T9/00
    • H04N19/91G06T3/40G06T9/00H04N19/136H04N19/176H04N19/184H04N19/593H04N19/61
    • A method compresses a point cloud composed of a plurality of points in a three-dimensional (3D) space by first acquiring the point cloud with a sensor, wherein each point is associated with a 3D coordinate and at least one attribute. The point cloud is partitioned into an array of 3D blocks of elements, wherein some of the elements in the 3D blocks have missing points. For each 3D block, attribute values for the 3D block are predicted based on the attribute values of neighboring 3D blocks, resulting in a 3D residual block. A 3D transform is applied to each 3D residual block using locations of occupied elements to produce transform coefficients, wherein the transform coefficients have a magnitude and sign. The transform coefficients are entropy encoded according the magnitudes and sign bits to produce a bitstream.
    • 一种方法通过首先利用传感器获取点云来压缩由三维(3D)空间中的多个点组成的点云,其中每个点与3D坐标相关联,并且在 至少有一个属性。 点云被划分成3D元素块的阵列,其中3D块中的一些元素具有缺失点。 对于每个3D块,基于相邻3D块的属性值预测3D块的属性值,产生3D残差块。 使用占用元素的位置将3D变换应用于每个3D残差块以产生变换系数,其中变换系数具有幅度和符号。 根据幅度和符号位对变换系数进行熵编码以产生比特流。
    • 9. 发明申请
    • SYSTEM AND METHOD FOR PROCESSING INPUT POINT CLOUD HAVING POINTS
    • 用于处理具有点的输入点云的系统和方法
    • WO2018083963A1
    • 2018-05-11
    • PCT/JP2017/037287
    • 2017-10-06
    • MITSUBISHI ELECTRIC CORPORATION
    • TIAN, DongCHEN, SihengFENG, ChenVETRO, Anthony
    • G06F17/10G06K9/00G06T15/08G06T17/00
    • Systems and methods for determining a pattern in time series data representing an operation of a machine. A memory to store and provide a set of training data examples generated by a sensor of the machine, wherein each training data example represents an operation of the machine for a period of time ending with a failure of the machine. A processor configured to iteratively partition each training data example into a normal region and an abnormal region, determine a predictive pattern absent from the normal regions and present in each abnormal region only once, and determine a length of the abnormal region. Outputting the predictive pattern via an output interface in communication with the processor or storing the predictive pattern in memory, wherein the predictive pattern is a predictive estimate of an impending failure and assists in management of the machine.
    • 用于确定代表机器操作的时间序列数据中的模式的系统和方法。 存储器提供由机器的传感器产生的一组训练数据实例,其中每个训练数据实例代表机器在机器故障结束的一段时间内的操作。 处理器,被配置为将每个训练数据示例迭代地划分成正常区域和异常区域,确定正常区域缺失并且仅出现在每个异常区域一次的预测模式,并且确定异常区域的长度。 通过与处理器通信的输出接口输出预测模式或将预测模式存储在存储器中,其中预测模式是对即将发生的故障的预测性估计,并有助于管理机器。