Delta Electronics, Inc.

Yi-Ting Huang

Yu-Ta Chen

Kuan-Wen Chen

Po-Heng Chen

Zhao-Xu Luo

Chia-Min Li

An image processing method includes analyzing multiple images data based on Illumination-invariant Feature Network (IF-NET) with an image processing device to generate corresponding sets of eigenvector, in which image data includes a first image data related to at least one first feature of the sets of eigenvector, and a second image data related to at least one second feature of the sets of eigenvector; choosing a corresponding first training set of tiles and second training set of tiles from the first image data and second image data with an image processing device based on IF-NET, and computing on both training set of tiles to generate a least one loss value; and adjusting IF-NET based on a least one loss value. An image processing system is also disclosed herein.

Image processing method and image processing system

A map constructing method adopted by a map constructing apparatus and includes steps of: continually obtaining colored image and depth-image information by an image sensor and a depth sensor of the map constructing apparatus while the map constructing apparatus is moving; recognizing the colored image for determining whether a dynamic object exists; labelling an object area corresponding to the location of the dynamic object if the dynamic object exists in the colored image; mapping the object area to a depth-image coordinate adopted by the depth-image information, searching for a corresponding position of the object area in the depth-image information and filtering the object area from the depth-image information for generating an adjusted depth-image information; and, inputting the adjusted depth-image information to a map constructing algorithm for establishing map data.

Map constructing apparatus and map constructing method

A scene model constructing method includes the following steps. According to multiple position parameters in multiple scene materials, classifying the scene materials into multiple position groups. According to scene similarities between the scene materials, classifying the scene materials into multiple first similar image sub-groups and multiple second similar image sub-groups. Establishing a first similar image sub-model and a second similar image sub-model respectively according to the first similar image sub-group and the second similar image sub-group. Combining a first similar image sub-model to a first position model, and combining a second similar image sub-model to a second position model. Finally, combining the first position model and the second position model to a scene model.

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Taoyuan, Taiwan

Yi-Ting Huang