Systems and methods for detecting the cracks in illuminated electronic device screens are disclosed. In one embodiment, the method includes receiving an image of an electronic device screen and retrieving a plurality of kernels, each having values corresponding to a line region and a non-line region, with the orientation of the line region and the non-line region differing for each kernel. At least some of the kernels are applied to the image to obtain, at various locations of the image, values corresponding to the line regions and the non-line regions. Based on the values corresponding to the line regions and the non-line regions, cracks are automatically identified in the electronic device screen.

In order to detect defects in display panels by an automatic way accurately and quickly, the present invention proposes a method combining image feature extraction and classifier model. It calculates the histograms of oriented gradient (HOG) of images of the display panel collected by an industrial camera of a detection apparatus as feature vectors. Then use them as input samples to train the classifier model to recognize the defects of the display panel.

Systems and methods for detecting the presence or absence of screen covers on electronic device screens are disclosed. In one embodiment, the method includes obtaining an image of a front side of an electronic device and automatically identifying line segments in the image. For each identified line segment, the method includes calculating the angle of the identified line segment. The method further includes determining an amount of the line segments having vertical or horizontal orientations. If the amount of identified vertical or horizontal line segments exceeds a predetermined threshold amount, then the presence of a screen cover is indicated.

A method of detecting a defect in an electronic display includes taking a digital image of the electronic display; and electronically determining if an area of the electronic display is defective based on data of the digital image.

Some embodiments provide a method for calculating parasitic parameters for a pattern to be manufactured on an integrated circuit (IC) substrate. The method receives a definition of a wire structure as input. The method rasterizes the wire structure (e.g., produces pixel-based definition of the wire structure) to produce several images. Before rasterizing the wire structure, the method in some embodiments decomposes the wire structure into several components (e.g., several wires, wire segments or wire structure portions), which it then individually rasterizes. The method then uses the images as inputs to a neural network, which then calculates parasitic parameters associated with the wire structure. In some embodiments, the parasitic parameters include unwanted parasitic capacitance effects exerted on the wire structure. Conjunctively, or alternatively, these parameters include unwanted parasitic resistance and/or inductance effects on the wire structure.

A display defect detection system and a detection method thereof are disclosed. A display defect detection system includes a preprocessing circuit receiving a capture image of a test pattern, displayed by a display panel, as a panel image including diagonal Mura and preprocessing the panel image to output a preprocessing image and a Mura detection circuit calculating a final feature value of the diagonal Mura by multiplying a first feature value, in which a luminance difference of the diagonal Mura is reflected, by a second feature value in which a shape ratio of the diagonal Mura is reflected, on the basis of an edge map of the preprocessing image, and detecting a display position of the diagonal Mura on the basis of the final feature value of the diagonal Mura.

A monitor accuracy management system includes a monitor apparatus and a tablet terminal. The monitor apparatus includes a generation unit configured to generate a display pattern that is based on identification information and a display control unit configured to display the generated display pattern on a display screen. The tablet terminal includes an outward-facing camera which is disposed on a surface of a housing and which is configured to capture the display pattern displayed on the display screen in a state with the surface of the housing placed onto the display screen of the monitor apparatus, an acquisition unit configured to acquire identification information of the monitor apparatus based on the captured display pattern, an identification unit configured to use the acquired identification information to identify the monitor apparatus to be managed, and an evaluation unit configured to evaluate the image quality of the identified monitor apparatus.

A system of automatically monitoring an electronic billboard to immediately responding a production abnormality and a method thereof are disclosed. In the system, a monitoring device performs an image pattern analysis on a monitoring image to analyze at least one analysis area, and captures the at least one analysis area as a to-be-analyzed image; at least one image analysis method is set to transform the to-be-analyzed image into an image characteristic according to an image pattern of the to-be-analyzed image. When a comparison between the image characteristic and a preset characteristic indicates an abnormality, a monitoring device generates, records and transmits a warning message and/or issues an alarm, thereby achieving the technical effect of automatically monitoring the electronic billboard and immediately responding a production abnormality.

Methods for matching features in patterns for electronic designs include inputting a set of pattern data for semiconductor or flat panel displays, where the set of pattern data comprises a plurality of features. Each feature in the plurality of features is classified, where the classifying is based on a geometrical context defined by shapes in a region. The classifying uses machine learning techniques.

A repair method for a flexible panel. The method includes: obtaining first image information of a flexible panel to be repaired; determining a first classification tag of the flexible panel to be repaired based on a deep learning model and the first image information; and determining an operating parameter of a repair device according to the first classification tag, and repairing the flexible panel to be repaired based on the operating parameter.