4results about How to "Eliminate mismatch" patented technology

The application relates to the technical field of image detection, and discloses a scale-invariant contour matching method and device based on machine vision and a storage medium, the method comprising the following steps: extracting a target contour from a template and a query image and detecting key points; constructing a multi-scale contour context descriptor for each key point, the descriptor being composed of chord length ratios and chord angle features under multi-scale in a local neighborhood; performing feature matching, estimating scale and rotation transformation parameters according to chord length ratios and chord angles of a matched descriptor pair, and combining key point coordinates to calculate a translation parameter to generate a similarity transformation hypothesis; clustering all hypotheses as points in a high-dimensional parameter space to obtain candidate instances; for each candidate instance, fitting an accurate similarity transformation model and eliminating false matching by using a random sample consensus algorithm through matched point pairs associated with the candidate instance, and outputting final positioning parameters of a target object. The application realizes efficient, accurate and scale- and rotation-invariant contour matching.

This invention discloses a fine motor imagery decoding system based on PINN and multimodal fusion. The system employs a fine motor imagery decoding method based on PINN and multimodal fusion, and includes a multimodal physiological signal synchronous acquisition module, a data preprocessing and correlation analysis module, a dual-stream decoding network module, a physical manifold constraint layer module, an adaptive gradient balance optimization module, and a classification decision module. By deeply embedding biophysical mechanisms into a deep learning framework, this invention addresses the ill-posedness of the source localization inverse problem, overcomes overfitting in small-sample scenarios, and significantly improves the decoding accuracy, robustness, and physiological interpretability of fine hand movements.

The invention provides a mechanical arm trajectory tracking control method fusing model prediction and sliding mode control. The mechanical arm trajectory tracking control method comprises the steps that a discrete sliding mode controller is constructed, and a discrete sliding mode surface based on trajectory tracking errors is designed; calculating a sliding mode control law; constructing a dynamics prediction model based on an echo state network, and performing online learning and updating by taking historical state data and a sliding mode control law of the mechanical arm as input so as to predict a state track of the mechanical arm in a future time domain; constructing a model prediction controller embedded with sliding mode control, and introducing a prediction state and a sliding mode control law into an optimization objective function of the model prediction controller; and under the model prediction controller, an optimization problem is converted into a quadratic programming problem to be solved, a smooth optimization control quantity meeting physical constraints is obtained, and the control quantity acts on the mechanical arm system. The high-frequency buffeting problem of sliding mode control is effectively solved, and meanwhile high-precision trajectory tracking of the mechanical arm under strong nonlinear interference is guaranteed.

The application discloses a kind of compressed computing spectral imaging reconstruction method and system based on pulse weighted network, it is related to computing imaging technical field, it can be from compressed measurement value with low energy consumption and high quality Reconstruct HSI data, can greatly improve the energy efficiency ratio of reconstruction algorithm.The scheme is specifically as follows: using coded aperture snapshot spectral imager CASSI obtains the two-dimensional compressed measurement value of known target scene three-dimensional hyperspectral imaging technology HIS data cube.The two-dimensional compressed measurement value obtained is preprocessed, image features are extracted, and the initial value of the reconstruction result of the HSI cube of the known target scene is obtained.The initial value of the reconstruction result is spliced with the coded pattern of the compressed spectral imaging system, and is sent into the trained multi-time-step reconstruction network to obtain the output result of the network, i.e., the reconstruction result of the hyperspectral data of the target scene to be measured.