Coretronic Corporation

Cheng-Hsin Lee

Huai-En Wu

Huang-Cheng Chiang

Jia-Hua Lee

An object positioning system and an object positioning method are provided. The object positioning system includes a sensing device, a storage device and a processing device. The sensing device collects point cloud data obtained from a scene including a target object. The processing device inputs surrounding area data centered on a key point and a preset feature descriptor to a neural network to calculate a scene feature descriptor of the scene. The processing device performs feature matching between the scene feature descriptor and the preset feature descriptor, and calculates a position of the target object in an actual space. The invention utilizes the feature extraction capability of the neural network to effectively improve the accuracy and stability of target object identification and positioning.

Object positioning method and object positioning system

A supervised training device and method for training a neural network, and a supervised classification method and device based on the neural network are provided. The training device includes a storage unit and a processor. The processor accesses and executes a sampling module, a labelling module and a training module in the storage unit. The sampling module samples a first image and a second image from a first dataset. The labelling module tags the first image in response to a first control instruction to generate a first tagged image, and generates label data according to the first tagged image and the second image. The training module trains the neural network according to the label data.

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Hsin-Chu, Taiwan

Cheng-Hsin Lee