System and methods used to inspect a moving web (112) include a plurality of image capturing devices (113) that image a portion of the web at an imaging area. The image data captured by each of the image capturing devices at the respective imaging areas is combined to form a virtual camera data array (105) that represents an alignment of the image data associated with each of the imaging areas to the corresponding physical positioning of the imaging areas relative to the web. The image output signals generated by each of the plurality of image capturing devices may be processed by a single image processor, or a number of image processors (114) that is less than the number of image capturing devices. The processor or processors are arranged to generate the image data forming the virtual camera array.

A method of synchronizing a line scan camera. The method comprises: obtaining line scan data of a region of interest (ROI) of a travelling surface from the line scan camera, the line scan camera being oriented perpendicular to a direction of travel of the travelling surface, the line scan data comprising a plurality of lines; identifying occurrences of a major frequency of a repeated texture on the travelling surface using characterized line scan data for each line in the plurality of lines of the line scan data; determining a period of the major frequency; and changing a line rate of the line scan camera when the determined period is different than a reference period.

A shape inspection apparatus for inspecting a strip-shaped body includes: a line sensor camera; a first illumination light source and a second illumination light source; a measurement control unit; and a data processing unit. The measurement control unit controls the lighting timings and light emission time periods as well as the line image acquisition timing based on a line speed so that overlapping of photographing ranges does not occur between a first line image acquired within a light emission time period of the first illumination light source and a second line image acquired within a light emission time period of the second illumination light source. The data processing unit calculates an inclination of the surface of the strip-shaped body based on a differential line image obtained based on the first line image and the second line image.

A shape inspection apparatus for inspecting a strip-shaped body includes: a line sensor camera; a first illumination light source and a second illumination light source; a measurement control unit; and a data processing unit. The measurement control unit controls the lighting timings and light emission time periods as well as the line image acquisition timing based on a line speed so that overlapping of photographing ranges does not occur between a first line image acquired within a light emission time period of the first illumination light source and a second line image acquired within a light emission time period of the second illumination light source. The data processing unit calculates an inclination of the surface of the strip-shaped body based on a differential line image obtained based on the first line image and the second line image.

A shape inspection apparatus includes N illumination light sources, a line sensor camera, a measurement control unit, and a data processing unit. The measurement control unit controls the illumination light sources to modulate luminescence intensities at a frequency that is 1/N of a frequency of a scan rate of the line sensor camera, and to emit lights by sequentially repeating N different patterns of illumination intensity ratios. The data processing unit generates a first separated image and a second separated image based on a photographed image, generates a first mixing elimination image acquired by removing an unnecessary illumination component from the first separated image, and a second mixing elimination image acquired by removing an unnecessary illumination component from the second separated image, and calculates an inclination of the surface of the strip-shaped body based on a difference between the first mixing elimination image and the second mixing elimination image.

An optical unit for an optical sorter can simplify the assembling operation and the installing operation of the optical sorter through unifying components of an optical section of the optical sorter for sorting objects to be sorted such as a granular object or a sheet object. The optical unit includes an optical detection means for detecting the object to be sorted, an ejector means for ejecting the object to be sorted, a discrimination means for processing detection signal from the optical detection means to make a quality discrimination of the sorted objects, and an ejector driving means for driving the ejector means based on the quality discrimination of the discrimination means. The optical detection means, the ejector means, the discrimination means, and the ejector driving means are integrated into a housing to unify them to provide the optical unit.

A cardboard sheet defect detection device is for detecting a defect of a single-faced cardboard sheet guided by a guide member outside a corrugated core. The cardboard sheet defect detection device includes an irradiation device that irradiates the core with light at an irradiation angle inclined as much as a preset predetermined angle with respect to the single-faced cardboard sheet; an imaging device that images an irradiated portion of the light in the core; an image processing device that defines a bright section or a dark section along a transport direction of the single-faced cardboard sheet, based on a captured image captured by the imaging device; and a determination device that determines a quality by comparing a length of the bright section or a length of the dark section which is defined by the image processing device with a preset determination value.

The present specification provides a film defect detection system. The film defect detection system may comprise an image acquisition unit configured to acquire an image of a film in a manufacturing process of the film; a defect detection unit configured to detect defects in the film by analyzing the acquired image of the film, using a machine learning algorithm learned to detect a defect in advance, when receiving the acquired image of the film; and an information output unit configured to output information on the defects in the film detected by the defect detection unit.

The invention relates to a method, comprising capturing an image of an object to be monitored at a first image capturing frequency by an image sensor of a machine vision system, transmitting said captured image data to an image data processing device and analyzing said received image data by said image data processing device, and wherein if the image data is detected to comprise a deviation, a trigger signal is transmitted for triggering an image sensor for reconfiguring it to capture an image burst and for transmitting the captured image burst data to said image data processing device for further analysis. The invention further relates to a machine vision system and a computer program product performing the method.

A method for detection of distinctive features in a web being transported in a moving direction during a web manufacturing process including the steps of: a) acquiring an image of the web, including a plurality of pixels P i with i{1; . . . ; p}, b) identifying a plurality of regions of interest each corresponding to a distinctive feature by processing the plurality of pixels P i by: c) selecting a local pixel unit including a subset P j with jS{1; . . . ; p} of the plurality of pixels, the subset i) being representative of a subregion of the digital image, and ii) different from previously selected local pixel units, d) deciding whether the local pixel unit is of interest or not, i) if the local pixel unit is of interest, 1) identifying whether the local pixel unit is located within an impact area A k of a previously identified region of interest R k with kA.Math.{1; . . . ; n}, 2) if the local pixel unit is not located within any impact area A k of any previously identified region of interest R k with kA.Math.{1; . . . ; n}, or no regions of interest have previously been identified, (a) identifying the local pixel unit as a new region R n+1 of interest; (b) initializing an impact area A n+1 for the new region R n+1 of interest; (c) incrementing a counter n representative of the number of previously identified regions of interest; if the local pixel unit is located within an impact area A k 0 of a previously identified region of interest R k 0, (a) merging, depending on a merging condition, the local pixel unit with the previously identified region of interest R k 0, (b) if the merging condition is fulfilled, updating the impact area A k 0 of the region of interest R k 0; ii) preferably, if the local pixel unit is not of interest, identifying whether the local pixel unit is located within an impact area A k of a previously identified region of interest R k with k{1; . . . ; n},if the local pixel unit is located within an impact area A k 0 of a previously identified region of interest R k 0, updating the impact area A k 0; e) repeating steps b) through d) until at least essentially all pixels of the image have been processed.