A projection apparatus and a keystone correction method thereof are provided. The projection apparatus includes a display processing circuit including an image processing circuit, a first keystone correction circuit, a rotation circuit, a second keystone correction circuit, and a video output circuit. The first keystone correction circuit performs first keystone correction processing on a processed image frame based on horizontal scaling processing to obtain a first corrected image frame. The rotation circuit performs rotation processing on the first corrected image frame to write a rotated image frame into a memory. The second keystone correction circuit reads the rotated image frame from the memory and performs second keystone correction processing on the rotated image frame based on the horizontal scaling processing to obtain a second corrected image frame. The video output circuit transmits the second corrected image frame to a projection module through a data transmission interface.

Location-based binning can separate defects on different rows of channel holes in a 3D NAND structure to corresponding bins. A one-dimensional projection of an image is generated and a one-dimensional curve is formed. A mask is generated from the one-dimensional curve. Defects in the image are detected using the mask and location-based binning is performed.

A measurement apparatus measures position information of a measurement target in a first direction. The apparatus comprises a scope configured to capture an image of the measurement target and generate image data, and a processor configured to obtain, based on the image data, the position information of the measurement target in the first direction. The processor is configured to determines the position information of the measurement target in the first direction based on: provisional position information of the measurement target in the first direction obtained from the image data, and using a correction value which is output from a model by inputting, in the model, a feature quantity, of the image data, related to a second direction different from the first direction.

An image processing system is disclosed, comprising a multiple-lens camera, a vertex list generator and an image processing apparatus. The multiple-lens camera captures a X-degree horizontal field of view (FOV) and a Y-degree vertical FOV to generate multiple lens images, where X<=360, Y<=180. The vertex list generator generates a first main vertex list according to a correspondence table, and generates a first region of interest (ROI) vertex list according to the first main vertex list and a position information of the ROI when the ROI overlaps at least one measuring region inside an overlap region in a projection image. The image processing apparatus generates the projection image according to the multiple lens images and a second main vertex list related to first main vertex list in a rendering mode.

Systems and methods are provided for depth estimation from monocular images using a depth model with sparse range sensor data and uncertainty in the range sensor as inputs thereto. According to some embodiments, the methods and systems comprise receiving an image captured by an image sensor, where the image represents a scene of an environment. The method and systems also comprise deriving a point cloud representative of the scene of the environment from range sensor data, and deriving range sensor uncertainty from the range sensor data. Then a depth map can be derived for the image based on the point cloud and the range sensor uncertainty as one or more inputs into a depth model.

Provided is an information processing apparatus including an image supply unit that supplies a plurality of input images showing corresponding objects to an image processing unit and obtains a plurality of object images as an image processed result from the image processing unit, and a display control unit that synchronously displays the plurality of object images that have been obtained. The object images are regions including the corresponding objects extracted from the plurality of input images, and orientations, positions, and sizes of the corresponding objects of the plurality of object images are unified.

A broken wheel detection system for detecting broken wheels on rail vehicles even when such vehicles are moving at a high rate of speed by determining the positions, lengths, or orientations of structured light lines projected against passing wheels.

A method and a system for vision-based defect detection are proposed. The method includes the following steps. A test audio signal is outputted to a device-under-test (DUT), and a response signal of the DUT with respect to the test audio signal is received to generate a received audio signal. Signal processing is performed on the received audio signal to generate a spectrogram, and whether the DUT has an unacceptable defect with respect to the predefined auditory standard is determined by analyzing a distribution of signal strength according to the spectrogram.

A method, apparatus, and system for filtering obstacle candidates determined based on outputs of a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. One or more obstacle candidates are determined based on the point cloud. The one or more obstacle candidates are filtered to remove a first set of obstacle candidates in the one or more obstacle candidates that correspond to noise based at least in part on characteristics associated with points that correspond to each of the one or more obstacle candidates. One or more recognized obstacles comprising the obstacle candidates that have not been removed are determined. Operations of an autonomous vehicle are controlled based on the recognized obstacles.

An inspection terminal device includes an imager, a displayer, a feature extractor, and an evaluator. The imager images an inspection object. The displayer displays an image of the inspection object imaged by the imager. The feature extractor extracts a feature of the inspection object in the image. The evaluator evaluates the inspection object based on the feature. The displayer displays an evaluation result of the evaluator to correspond to a position of the inspection object in the image.