Industrial Technology Research Institute

Yao-Yang Tsai

Jay Huang

Ko-Shyang Wang

Chih-Pin Liao

Chih-Kai Chiu

Chung-Li Tai

Shuo-Peng Liang

Po-Lung Chen

Hian-Kun Tenn

Wen-Yang Wang

Yang-Lun Liu

Yu-Lin Tsai

Cheng-Chieh Wu

A machining parameter automatic generation system includes a geometric data capturing module, a feature recognition learning network and a machining parameter learning network. The geometric data capturing module captures a geometric shape of a workpiece to generate a candidate feature list. The feature recognition learning network trains the candidate feature list according to a first neural network model to obtain an applicable feature list. The machining parameter learning network trains the applicable feature list and the candidate machining parameter according to a second neural network model to obtain an applicable machining parameter. The applicable machining parameter is used to generate a machining program, and the machining program is read by a machine tool for processing.

Machining parameter automatic generation system

A calibration method applicable for an automation machining apparatus includes building a first stereoscopic characteristic model corresponding to an object, obtaining a stereoscopic image of the object, building a second stereoscopic characteristic model corresponding to the object based on the stereoscopic image, obtaining at least one error parameter corresponding to the second stereoscopic characteristic model by comparing the second stereoscopic characteristic model with the first stereoscopic characteristic model, and calibrating a machining parameter of the automation machining apparatus based on the at least one error parameter.

Calibration method and automation machining apparatus using the same

A method for vision machine inspection comprises providing depth information of a target acquired by an image capturing system, determining real-time three-dimensional information of a target object in a predetermined inspecting area based on depth information of at least one real-time image of the target. The method further comprises projecting color pixel information of a real-time color image of the target object to a three-dimensional virtual model based on the real-time three-dimensional information. The real-time color image may be acquired by a color camera system. The method further comprises generating a color three-dimensional virtual model. The color three-dimensional virtual model may comprise the color pixel information.

Inspection apparatus, method, and computer program product for machine vision inspection

A method and an apparatus for reconstructing a three dimensional model of an object are provided. The method includes the following steps. A plurality of first depth images of an object are obtained. According to a linking information of the object, the first depth images are divided into a plurality of depth image groups. The linking information records location information corresponding to a plurality of substructures of the object. Each depth image group includes a plurality of second depth images, and the substructures correspond to the second depth images. According to the second depth image and the location information corresponding to each substructure, a local module of each substructure is built. According to the linking information, the local models corresponding to the substructures are merged, and the three-dimensional model of the object is built.

Method and apparatus for reconstructing three dimensional model

A depth detection method includes the following steps. First, first and second video data are shot. Next, the first and second video data are compared to obtain initial similarity data including r&#xd7;c&#xd7;d initial similarity elements, wherein r, c and d are natural numbers greater than 1. Then, an accumulation operation is performed, with each similarity element serving as a center, according to a reference mask to obtain an iteration parameter. Next, n times of iteration update operations are performed on the initial similarity data according to the iteration parameter to generate updated similarity data. Then, it is judged whether the updated similarity data satisfy a character verification condition. If yes, the updated similarity data is converted into depth distribution data.

Depth detection method and system using thereof

A method for recognizing three-dimensional control points and a computer readable medium using the same are disclosed. The method for recognizing three-dimensional control points comprises the following steps. A depth information item corresponding to an image captured by an image capturing apparatus is received. A three-dimensional block information item corresponding to a three-dimensional block is generated according to the depth information. At least one reference plane is generated according to the depth information. At least one connection group is generated according to the three-dimensional block information and the reference plane. A three-dimensional block nearest to the image capturing apparatus is selected as a control point from the connection group.

Method for recognizing three-dimensional control point and computer readable medium using the same thereof

A system for estimating a location of an occluded skeleton, a method for estimating a location of an occluded skeleton and a method for reconstructing an occluded skeleton are provided. The method for estimating a location of an occluded skeleton comprises the following steps: Firstly, a trace of a reference central point of a body is estimated according to a plurality of continuously moving images. Next, a human movement state is estimated according to the trace and a motion information of the continuously moving images free of skeleton occlusion. Then, a possible range of the occluded skeleton for maintaining human balance is calculated according to the human movement state. Afterwards, a current motion level of the occluded skeleton is predicted according to a historic motion information of the occluded skeleton. Lastly, the location of the occluded skeleton is estimated according to the current motion level and the possible range.

System for estimating location of occluded skeleton, method for estimating location of occluded skeleton and method for reconstructing occluded skeleton

Growing community of inventors

Kaohsiung, Taiwan

Yao-Yang Tsai