Abstract: The present invention provides an automated optical inspection and classification apparatus based on a deep learning system, comprising a camera and a processor. The processor executes a deep learning system after loading data from a storage unit and the processor, and comprises an input layer, a neural network layer group, and a fully connected-layer group. The neural network layer group is for extracting to an input image and thereby obtaining a plurality of image features. The fully connected-layer group is for performing weight-based classification and outputting an inspection result.

Abstract: An instrumentation image analyzing system includes an image capturing device, a processor connected to the image capturing device, and a communication module connected to the processor, which replaces visual observation work of human by image analyzing technique for saving costs of manual visual observation as well as reducing the error rate of detection and enhancing the efficiency of detection. The image capturing device captures an image of at least one instrumentation to obtain an instrumentation image. The processor loads a program to perform an instrumentation identifying module which identifies the instrumentation image to generate an instrumentation information. The communication module transmits the instrumentation information to an external device.

Abstract: An optical inspection method for an optical inspection device comprising an optical lens is provided according to an embodiment of the disclosure. The optical inspection method includes: obtaining a first image of an object by the optical lens; performing an edge detection on the first image to obtain a second image comprising an edge pattern; and performing a defect inspection operation on the second image based on a neural network architecture to inspect a defect pattern in the second image. In addition, an optical inspection device and an optical inspection system are provided according to embodiments of the disclosure.

Abstract: A defect inspection system, connected to an automatic visual inspection device, is provided, including the followings. A re-inspection server (VRS) receives a defect image and a defect location. A training terminal stores trained modules. A classification terminal receives the defect image and the defect location, reads a target trained module corresponding to the defect image, classifies the defect image according to the target trained module to obtain a labeled defect image, and sends the labeled defect image to the VRS. A re-inspection terminal receives the labeled defect image from the VRS, and sends a verified operation corresponding to the labeled defect image to the VRS. A labeling re-inspection terminal receives the verified operation and the labeled defect image, and a labeling result corresponding to the labeled defect image. The VRS sends the labeling result and the labeled defect image to the training terminal to train a corresponding training module.

Abstract: The present invention is an automated optical inspection method using deep learning, comprising the steps of: providing a plurality of paired image combinations, wherein each said paired image combination includes at least one defect-free image and at least one defect-containing image corresponding to the defect-free image; providing a convolutional neural network to start a training mode of the convolutional neural network; inputting the plurality of paired image combinations into the convolutional neural network, and adjusting a weight of at least one fully connected layer of the convolutional neural network through backpropagation to complete the training mode of the convolutional neural network; and performing an optical inspection process using the trained convolutional neural network.

Abstract: A method for evaluating an efficiency of a manual inspection for a defect pattern is provided according to an embodiment of the disclosure, which comprises: enabling an evaluation program; loading a test image automatically by the enabled evaluation program and displaying the test image in a user interface; detecting a user behavior of a user after the user watches the test image; generating original data according to the user behavior, wherein the original data reflects at least one of whether the user identifies the defect pattern in the test image and a type of the defect pattern identified by the user; and performing a quantitative operation on the original data to generate evaluation data corresponding to the efficiency of the manual inspection, wherein the evaluation data reflects an evaluation result corresponding to the efficiency of the manual inspection.

Abstract: A semiconductor defects inspection apparatus for inspection of bubble defects of an object is provided. The semiconductor defects inspection apparatus includes a carrier, an optical system, an infrared image capturing device, and a processing unit. The carrier is adapted for bearing the object. The optical system provides an illumination beam to the object to produce an image beam. The infrared image capturing device is disposed on a transmission path of the image beam. The infrared image capturing device is adapted for receiving the image beam to be transformed into an image information. The processing unit is electrically connected to the infrared image capturing device and adapted for analyzing the object according to the image information.

Abstract: Robotic arm processing method and system based on 3D image are provided. The processing method includes: providing robotic arm 3D model data and processing environment 3D model data; obtaining workpiece 3D model data, and generating a processing path consisting of contact points according to the workpiece 3D model data, wherein a free end of a robotic arm moves along the processing path to complete a processing procedure; generating a posture candidate group according to a relationship according to each one of the contact points corresponding to the free end of the robotic arm; selecting an actual moving posture from the posture candidate group; moving the free end of the robotic arm to each corresponding one of the contact points according to the selected actual moving posture; and moving the free end of the robotic arm along the processing path according to the actual moving postures to perform the processing procedure.

Abstract: An eye movement traces authentication system comprises an image capturing device, a display device, and a control unit. The image capturing device is for capturing a user's image. The display device is for providing a display interface for the user to watch. The control unit includes a facial feature recognition module or hand feature recognition module, an eye-movement analyzing module, an eye movement traces unlocking module, and a volitional confirmation module. The eye movement traces authentication system uses the user's facial features to confirm the user's identity and determines the user's watching direction and eye movement, by recording user's eye movement traces from the user's watching direction to verify the user's identity and by analyzing the user's eye movement to confirm, according to the user's eye movement, whether the user is operating volitionally.

Abstract: The present invention is a dynamic graphic eye-movement authentication system, comprising an image capturing device, a display device, and a control unit. The image capturing device is provided for capturing images of a user's eyes to obtain the user's gaze direction. The display device is for providing a display interface to user. The control unit has a graphic producing module, an eye-movement analyzing module, and a graphic authentication module, wherein the graphic producing module provides a plurality of dynamic graphics on the display interface, wherein the eye-movement analyzing module determines the user gaze direction according to the images of the user's eyes, wherein the graphic authentication module generates authentication-oriented input information according to the dynamic graphic selected by the user through the gaze direction and compares the authentication-oriented input information with user authentication information in an authentication database.

Abstract: An identity verification method, apparatus and system and a computer program product are provided. An eye movement tracking algorithm is executed for a face image sequence of a user to detect an eye movement status of the user. A password string is input through the input interface based on the eye movement state to start an eye movement verification procedure. And an autonomous determination procedure is executed based on the eye movement state to determine whether a user is in an autonomous state. When it is determined that the user is in the autonomous state, an operation procedure is started. When it is determined that the user is in a non-autonomous state, a warning signal is generated and the operation procedure is started.

Abstract: A semiconductor defects inspection apparatus for inspection of bubble defects of an object is provided. The semiconductor defects inspection apparatus includes a carrier, an optical system, an infrared image capturing device, and a processing unit. The carrier is adapted for bearing the object. The optical system provides an illumination beam to the object to produce an image beam. The infrared image capturing device is disposed on a transmission path of the image beam. The infrared image capturing device is adapted for receiving the image beam to be transformed into an image information. The processing unit is electrically connected to the infrared image capturing device and adapted for analyzing the object according to the image information.

Abstract: A defect inspection system, connected to an automatic visual inspection device, is provided, including the followings. A re-inspection server (VRS) receives a defect image and a defect location. A training terminal stores trained modules. A classification terminal receives the defect image and the defect location, reads a target trained module corresponding to the defect image, classifies the defect image according to the target trained module to obtain a labeled defect image, and sends the labeled defect image to the VRS. A re-inspection terminal receives the labeled defect image from the VRS, and sends a verified operation corresponding to the labeled defect image to the VRS. A labeling re-inspection terminal receives the verified operation and the labeled defect image, and a labeling result corresponding to the labeled defect image. The VRS sends the labeling result and the labeled defect image to the training terminal to train a corresponding training module.

Abstract: An optical inspection method for an optical inspection device comprising an optical lens is provided according to an embodiment of the disclosure. The optical inspection method includes: obtaining a first image of an object by the optical lens; performing an edge detection on the first image to obtain a second image comprising an edge pattern; and performing a defect inspection operation on the second image based on a neural network architecture to inspect a defect pattern in the second image. In addition, an optical inspection device and an optical inspection system are provided according to embodiments of the disclosure.

Abstract: A labeling system for defect classification having a storage unit and a processing unit is provided. The storage unit stores a defect classification module, a defect labeling module, and a catalog generation module. The processing unit performs the modules aforementioned. The defect classification module is configured to provide a defect type information. The defect labeling module marks a defect type label to at least one test object image according to an image feature and the defect type information on the at least one test object image. The catalog generation module receives the labeled test object images and moves test object images having the same defect type label to a corresponding file catalog.

Abstract: A method for evaluating an efficiency of a manual inspection for a defect pattern is provided according to an embodiment of the disclosure, which comprises: enabling an evaluation program; loading a test image automatically by the enabled evaluation program and displaying the test image in a user interface; detecting a user behavior of a user after the user watches the test image; generating original data according to the user behavior, wherein the original data reflects at least one of whether the user identifies the defect pattern in the test image and a type of the defect pattern identified by the user; and performing a quantitative operation on the original data to generate evaluation data corresponding to the efficiency of the manual inspection, wherein the evaluation data reflects an evaluation result corresponding to the efficiency of the manual inspection.

Abstract: The present invention provides a dynamic automatic focus tracking system, comprising an image capturing device for capturing an image of a workpiece in a target picture-taking region; a driving device for adjusting a spacing between the image capturing device and the workpiece; and a focal length adjustment module coupled to the image capturing device and the driving device to generate a control signal according to a figure feature and a predefined figure feature in the image of the workpiece and send the control signal to the driving device, thereby adjusting a position of the image capturing device with the driving device.

Abstract: A workpiece measuring machine adapted to measure a workpiece under test is provided. The workpiece under test includes an assembly surface and an appearance surface. The workpiece measuring machine includes a bearing base, a height measuring device and a calculation device. The bearing base is adapted to bear the workpiece under test. The height measuring device is movably disposed at a side of the bearing base and adapted to measure heights of the assembly surface and the appearance surface. The calculation device is electrically connected to the height measuring device so as to calculate a height difference between the assembly surface and the appearance surface. A measuring method and a calibration method of sectional difference of casing of workpiece are also provided.

Abstract: A profile measuring method used to measure a profile of an object-under-test includes the following steps. A light source, a light-transmissive projection film, and an image capturing device are provided, where the light-transmissive projection film is located between the light source and the image capturing device. The object-under-test is placed between the light source and the light-transmissive projection film, and a light beam is provided toward the light-transmissive projection film by the light source, to form an object-under-test projection of the object-under-test on the light-transmissive projection film. An image of the object-under-test projection is captured by the image capturing device, to obtain a projection size of the object-under-test projection. A measuring size of the object-under-test is calculated according to the projection size of the object-under-test projection. In addition, a profile measuring apparatus and a deformation detecting apparatus are also provided.

Abstract: An identity verification method and an identity verification apparatus are provided. A face image sequence of a user is analyzed to determine whether a biological feature conforms to a preset feature. An input interface is displayed after the biological feature conforms to the preset feature. An eye tracking detection is executed for a face image sequence to detect a blinking movement of the user. And a mental verification is executed by using the blinking movement. The invention combines the biological feature with the eye tracking detection for identity verification. The invention not only tightens security but also diversifies operation.