A vision inspection system includes a vision inspection controller receiving images form an imaging device. The vision inspection controller includes a binary classification tool and a multi-classification tool. The vision inspection controller processes each of the images through the binary classification tool to detect for the defects to determine primary inspection results including a PASS result if no defects are detected and a FAIL result if defects are detected. The vision inspection controller processes each of the images associated with the FAIL result through the multi-classification tool to determine secondary inspection results including identification of a type of defect. The vision inspection system may include a display configured to display the primary and secondary inspection results to an operator.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for swapping one or more faces without any explicit training on the one or more faces. The proposed method can be further implemented in real time.

An analysis device for visualizing an accuracy of a trained determination device includes an acquisition unit acquiring an image pair of a non-defective product image and a defective product image, an extraction unit extracting an image region of a defective part of the defective product, a generation unit generating a plurality of image regions of pseudo-defective parts, a compositing unit synthesizing each of the image regions of the plurality of pseudo-defective parts with the non-defective product image to generate a plurality of composite images having different feature quantities, an unit outputting the plurality of composite images to the determination device and acquiring a label corresponding to each of the plurality of composite images from the determination device, and a display control unit displaying an object indicating the label corresponding to each of the plurality of composite images in an array based on the feature quantities.

A vehicular vision system includes at least one color camera disposed at a vehicle and having an image sensor operable to capture image data. A first system on chip (SoC) includes an image signal processor that receives captured image data and converts the received image data to converted data that is in a format suitable for machine vision processing. A second system on chip (SoC) receives captured image data and communicates display data to a display disposed in the vehicle, with the display data being in a format suitable for display of video images at the display. At startup of the vehicle, video images derived from the display data are displayed by the display within a time period following startup of the vehicle and machine vision data processing of converted data does not commence until after the display time period has elapsed following startup of the vehicle.

Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.

A mounting device for selectively positioning an optical element within a field-of-view of an optical sensor of a vehicle includes: a housing defining an opening sized to fit over an aperture of the optical sensor; a holder for the optical element connected to the housing and positioned such that, when the holder is in a first position, the optical element is at least partially within the field-of-view of the optical sensor; and a motorized actuator. The motorized actuator can be configured to move the holder to adjust the position of the optical element relative to the field-of-view of the optical sensor.

A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.

Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.

A method of improving an efficiency of forming the golden samples obtains an image with a chip. Position information of the chip in the image is obtained based on the image. A target region on the image is labeled based on the position information. The target region is a region of the image covered by the chip. The target region is cut from the image to obtain a golden sample. An image process apparatus is also provided.