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127results about How to "Less important" patented technology

Single-track absolute grating scale and image coding method thereof

The invention provides a single-track absolute grating scale and an image coding method thereof. The single-track absolute grating scale comprises a light source, a reflector, increment scale gratings, a glass substrate, a mobile diaphragm, a photoelectric receiver and an indication grating, wherein the light source and the reflector are combined into an illumination light path, the increment scale gratings which are the same in width are engraved at equal intervals on the glass substrate, milestone flag bits are engraved in parallel at equal intervals on the glass substrate and below the increment scale gratings, the indication grating is embedded in an opening at the left upper part of the mobile diaphragm, an upper CMOS (complementary metal-oxide-semiconductor transistor) sensor and a lower CMOS sensor are respectively embedded in an upper opening and a lower opening which are arranged symmetrically on the right side of the mobile diaphragm, the indication grating is adhered to the glass substrate, the installation position of the upper CMOS aligns the increment scale gratings, the installation position of the lower CMOS aligns the milestone flag bits, and light of the increment scale gratings and the indication grating forms Moire fringes which are projected to the photoelectric receiver. By the single-track absolute grating scale and the image coding method thereof, the image acquisition speed is improved, the coding measurement accuracy is enhanced, and the effects of reliable coding, convenient decoding and quick result output can be achieved.
Owner:GUANGDONG UNIV OF TECH

A video description method and system based on an information loss function

The invention relates to a video description method and system based on an information loss function, and the method comprises the steps: obtaining a training video, and obtaining the semantic information of each frame of a set training video; Inputting the semantic information of the training video into an LSTM-combined hierarchical attention mechanism model to obtain character description of thetraining video; According to the importance of each word in the character description to the expression video content, performing loss weighting on the words to obtain an information loss function, and taking the information loss function as an objective function to perform back-propagation gradient optimization on the hierarchical attention mechanism model to obtain a video description model; Obtaining a to-be-described video, respectively inputting the to-be-described video into the target detection network, the convolutional neural network and the action recognition network to obtain a setof target features, overall features and motion features of each frame of the to-be-described video as semantic information of the to-be-described video, and inputting the semantic information into the video description model to obtain character description of the to-be-described video.
Owner:INST OF COMPUTING TECH CHINESE ACAD OF SCI
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