Inventec Corp.

Inventec (pudong) Technology Corporation

Trista Pei-Chun Chen

Wei-Chao Chen

Ying-Sheng Luo

Jonathan Hans Soeseno

Yi-Chun Chen

Daniel Stanley Young Tan

Yu-Sheng Lin

Hao Hsuan Lee

Ke-Min Hu

Chia-Ching Lin

Hsin-Hung Lin

Shih-Sung Lin

Chih Chang Chen

Ching Hui Li

Li-Ya Su

Meng-Chia Hung

Yung-Yu Chuang

Li Te Ko

Ming-feng Lee

An image processing method includes the following steps. A plurality of facial landmarks of a face frame are analyzed. A feature width is calculated according to the facial landmarks, and a head pose is analyzed according to the facial landmarks. The head pose is utilized to update the feature width to generate an updated width. A scale ratio of the updated width to an initial width is calculated. An object distance of a virtual camera is controlled according to the scale ratio. A two-dimensional image is captured from a virtual scene according to the object distance of the virtual camera.

Image processing method and electronic device

An image data augmentation device is provided, which comprises an image capturing circuit and a processor. The processor is configured to execute the following operations: using an object detection model to generate multiple bounding boxes and respective classification labels of the bounding boxes from an image; identifying an overlap ratio between the two bounding boxes, and determining whether the overlap ratio between the two bounding boxes is more than a ratio threshold, where the two bounding boxes have the same classification label; and when the overlap ratio between the two bounding boxes is more than the ratio threshold, deleting one of the two bounding boxes to update the bounding boxes, thereby by using the bounding boxes and the respective classification labels of the bounding boxes for executing machine learning.

Image data augmentation device and method

The present disclosure provides a packing method including following steps. Genetic algorithm is utilized to calculate multiple packing programs. Multiple candidate packing programs including all items are selected from the packing programs. Among each of the candidate packing programs, at least one of the items to be placed earlier is classified into a first subset, and at least another one of the items to be placed later is classified into a second subset. Among each of the candidate packing programs, a first packing for the first subset is maintained, and a second packing for the second subset is recalculated by using a greedy algorithm to generate an updated second packing.

Packing method and electronic device

A keyboard file verification method based on image processing comprises controlling a processor to perform following operations: obtaining a keyboard file; generating, according to the keyboard file, a search index and a feature image; obtaining a template image from a template database according to the search index; performing a calibration operation according to the feature image, wherein the calibration operation comprises: adjusting a resolution of the feature image according to a resolution of the template image; performing a shifting operation according to the feature image, to generate a plurality of candidate images; and comparing a key block of each of the plurality of candidate images with a key block of the template image to generate a difference map and a comparison result.

Keyboard file verification method based on image processing

The present disclosure proposes an electromagnetic susceptibility (EMS) testing method based on computer-vision. The method includes a training stage and a testing stage. During the training stage, the electronic device receives a testing data and the monitor displays a first picture. A camera captures the first picture to generate a template video. The processor generates a plurality of template images at least according to the template video. During the testing stage, an antenna emits an interference signal to the electronic device. The electronic device receives the testing data and the monitor displays a second picture. The camera captures the second picture to generate a testing video. The processor generates a testing image according to the testing video and calculates a difference ratio between the testing image and each template image. The processor sends an alert signal when the difference ratio is greater than a threshold.

Electromagnetic susceptibility testing method based on computer-vision

A processing element array includes N processing elements (PE) arranged linearly, N≥2, and an operating method of the PE array includes: performing a first data transmission procedure, where an initial value of I is 1 and the first data transmission procedure includes: operating, by an iPE, according to a first datum stored in itself, and sending the first datum to other PEs for their operations, adding 1 to I when I<N, and performing the first data transmission procedure again, performing a second data transmission procedure when I is equal to N, which includes: operating, by the JPE, according to a second datum stored in itself, and sending the second datum to other PEs for their operations, reducing J by 1 when J>1 and the (J−1)PE has the second datum, and performing the second data transmission procedure again.

Processing element array and operating method thereof

A method for automatically classifying transition motion includes following steps performed by a computing device: obtaining a plurality of transition motions, with each transition motion being associated with a source motion, a destination motion, and a transition mechanism converting the source motion into the destination motion; extracting a property vector from each transition motion and thereby generating a plurality of property vectors, wherein each property vector includes a plurality of transition properties; and performing a clustering algorithm according to the property vectors to generate a plurality of transition types.

Method of automatically classifying transition motion

A touch-based method for user authentication includes a training stage and an authentication stage. The training stage includes: generating, by a touch interface, a plurality of training touch parameters; and generating, by a processor, a training heat map according to the plurality of training touch parameters. The authentication stage includes: generating, by the touch interface, a plurality of testing touch parameters; generating, by the processor, a testing heat map according to the plurality of testing touch parameters; comparing, by the processor, the testing heat map with the training heat map to generate an error map; and generating, by the processor, one of pass signal and fail signal according to the error map.

Touch-based method for user authentication

A method for establishing a complex motion controller includes following steps: obtaining a source controller and a destination controller, wherein the source controller is configured to generate a source motion, and the destination controller is configured to generate a destination motion; determining a transition tensor between the source controller and the destination controller, wherein the transition tensor has a plurality of indices, one of the plurality of indices corresponds to a plurality of phases of the source motion; calculating a plurality of transition outcomes of the transition tensor and recording the plurality of transition outcomes according to the plurality of indices; calculating a plurality of transition qualities according to the plurality of transition outcomes; and searching for an optimal transition quality from the plurality of transition qualities to establish a complex motion controller for generating a complex motion corresponding to one of the plurality of phases.

Method for establishing complex motion controller

A method for generating reconstruction a reconstructed image is adapted to an input image having a target object. The method comprises converting the input image into a feature map with vectors by an encoder; performing a training procedure according to training images of reference objects to generate feature prototypes associated with the training images and store the feature prototypes to a memory; selecting a part of feature prototypes from the feature prototypes stored in the memory according to similarities between the feature prototypes and the feature vectors; generating a similar feature map according the part of feature prototypes and weights, wherein the weights represents similarities between the part of feature prototypes and the feature vectors; and converting the similar feature map into the reconstructed image by a decoder; wherein the encoder, the decoder and the memory form an auto-encoder.

Method for generating a reconstructed image

A method for training a locomotion controller of a robotic animal comprising obtaining a motion data of a reference animal, an environmental parameter, and a disturbance parameter; generating a plurality of primitive distributions and a first primitive influence according to the motion data by a policy network; selecting a current state of the reference animal from the motion data and set an adapting state of the reference animal; generating a second primitive influence, by the policy network, according to the current state, the adapting state, and the plurality of primitive distributions at least; and training the policy network according to a result determined by a discriminator according to the first primitive influence and the second primitive influence.

Method for training virtual animal to move based on control parameter

A method for generating a loop video comprises: obtaining an input video including multiple frames with a first frame, with each of frames having multiple pixels; extracting a moving object corresponding to a moving pixel region in the first frame with the moving pixel region having at least two pixels; inputting multiple candidate periods to a target function respectively to calculate multiple errors of the moving pixel region; determining a start frame and a loop period of the moving pixel region for each error, with the loop period being associated with one of the candidate periods; generating multiple output frames according to start frames and loop periods; and generating an output frame sequence from the output frames according to a loop parameter, wherein the output frame sequence corresponds to the loop video.

Method for generating a loop video

A method for training a defect detector comprises: obtaining a first reference image of a first reference object, wherein the first reference object has a defect and the first reference image has a first label indicating the defect; training a reconstruction model according to a second reference image of a second reference object associated with the first reference object, wherein a defect level of the second reference object is in a tolerable range with an upper limit; obtaining a target image of a target object associated with the first reference object and the second reference object; generating a second label according to the target image, the reconstruction model and an error calculation procedure, wherein the second label comprises a defect of the target object; and training a defect detector by performing a machine learning algorithm according to the first reference image, the target image and the second label.

Method for training defect detector

A method for labeling image comprises: obtaining a target image of a target object; generating a reconstruction image according to the target image and a reconstruction model, wherein the reconstruction model is trained with a plurality of reference images and a machine learning algorithm, each of the reference images is an image of a reference object whose defect level is in a tolerable range with an upper limit, and each of the reference objects is associated with the target object; generating a first difference image and a second difference image respectively by performing a first difference algorithm and a second difference algorithm respectively according to the target image and the reconstruction image; and generating an output image by performing a pixel-scale operation according to the first difference image and the second difference image, wherein the output image includes a label indicating a defect of the target object.

Method for labeling image

A surface defect detection method applied to a surface of an object is disclosed. The method includes obtaining an image of the surface, performing a deep learning algorithm by a computing device to set a plurality of bounding boxes in the image and to output a plurality of feature parameter sets associated with the plurality of bounding boxes, with each bounding box enclosing a possible defect of the surface, and performing a classifying algorithm by the computing device according to the bounding boxes and the feature parameters to determine whether the surface conforms to a specification.

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Taipei, Taiwan

Trista Pei-Chun Chen