Coretronic Corporation

Huai-En Wu

Yen-Yi Chen

Cheng-Hsin Lee

Pei-Chi Hsiao

Huang-Cheng Chiang

Jyun-Siang Fan

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

The invention provides an image segmentation method and an electronic device. The image segmentation method includes the following steps. Regression analysis is performed on a first gray-scale image to obtain a residual image having an object backbone area. A pixel value of each pixel in the object backbone area is defined as an average gray-scale value of the object backbone area in the residual image, and a second gray-scale image having the object backbone area is generated. It is recursively determined whether a residual polarity of each adjacent pixel adjacent to edge pixels of the object backbone area in the residual image is the same as a residual polarity of the corresponding edge pixel, and whether a pixel value of each adjacent pixel is greater than a first threshold, so as to expand the object backbone area in the second gray-scale image, which is extracted as a target object.

Image segmentation method and electronic device

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.

Training method of neural network and classification method based on neural network and device thereof

A method and an electronic apparatus for stitching a three-dimensional spherical panorama are provided. The method includes: obtaining a first image and a second image; projecting the first and seconds image onto a virtual spherical plane to form a first equirectangular panorama and a second equirectangular panorama; duplicating the first equirectangular panorama to generate a third equirectangular panorama, and duplicating the second equirectangular panorama to generate a fourth equirectangular panorama; performing image enhancement on the third and fourth equirectangular panoramas to generate a first enhanced equirectangular panorama and a second enhanced equirectangular panorama; calculating an optical flow for the first and second enhanced equirectangular panoramas; generating the three-dimensional spherical panorama according to the first and second equirectangular panoramas and the optical flow. The method and the electronic apparatus for stitching the three-dimensional spherical panorama may stitch, into the three-dimensional spherical panorama, images output by a radially designed panoramic camera.

Method and electronic apparatus for stitching three-dimensional spherical panorama

An electronic device having a neural network framework for estimating optical flow is provided. The electronic device is connected to an image acquiring unit, which acquires images to be analyzed. The electronic device includes a storage unit, a feature extraction unit, an optical flow estimation unit and a refining unit. The storage unit stores a feature extraction module. The feature extraction unit is connected to the image acquiring unit and the storage unit. The optical flow estimation unit is connected to the feature extraction unit to generate an estimated optical flow. The refining unit is connected to the optical flow estimation unit to input the estimated optical flow to a refining module to obtain an estimated optical flow result. A method for estimating optical flow is also provided to reduce the number of training parameters required for estimating optical flow, thereby reducing a training time and improving training stability.

Electronic device and method for estimating optical flow

A method and a system for inspecting a display image are provided. A test image is displayed on a display surface through a display device. An optical inspection image is generated by photographing the test image on the display surface by an image capturing device. A brightness channel image and a color channel image of the optical inspection image are obtained by performing color space transformation on the optical inspection image. Background estimation is applied to the color channel image to obtain a color channel background image. At least one color nonuniformity region in the test image is obtained by respectively comparing an inspection reference value with multiple color component pixel values of the color channel background image.

Method and system for inspecting display image

A projection system including a first projection device and a first image capturing device is provided. The first projection device projects a first projection image. The first projection image includes a plurality of different color lights and has color blocks of different brightnesses formed by the plurality of different color lights. The first image capturing device captures the first projection image to generate a first captured image. The first image capturing device includes a first processor. The first processor converts the first captured image into a first converted image according to a first conversion matrix. A color gradation adjustment operation is performed on the first converted image to output an adjustment signal, and the first projection device adjusts the projected first projection image according to the adjustment signal. An image color correction method is also provided.

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Hsin-Chu, Taiwan

Huai-En Wu