Abstract: A method for detecting a texture of Takifugu obscurus, including: (S1) removing internal organs and skin of the Takifugu obscurus followed by washing and drying; and removing a head and cutting a flesh on two sides of the Takifugu obscurus along a spine; (S2) cutting a plurality of cylindrical samples with the same shape and size along both sides of the flesh followed by transfer to a pre-cooled petri dish; allowing a cross section of the samples to cling to the petri dish followed by fixing with a petri dish cover; and storing the petri dish in seal at 0-4° C.; (S3) detecting, by a texture analyzer, a texture of each cylindrical sample; and averaging detection results of the samples as a result of the texture of the Takifugu obscurus.

Abstract: An automated mushroom harvesting system for mounting to a vertical mushroom rack comprises a robot having a frame mounted to a vertical carriage assembly. A SCARA arm is slidably mounted to the vertical carriage assembly by a vertical stage, operable to move the SCARA arm along a vertical mast. The SCARA arm moves the end effector in a horizontal plane for harvesting mushrooms, above the surface of the mushroom bed and into and out of the confines of the mushroom rack, and the vertical stage moves the SCARA arm in a vertical direction so as to access the mushrooms in a bed and to access mushroom beds on different levels of the vertical mushroom rack. An end effector having a helically reinforced neck and a graduated elasticity modulus, with a lower elasticity modulus in the neck and a higher elasticity in the cup, is also provided.

Abstract: This application provides a method for detecting images of testing object using hyperspectral imaging. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image and obtaining corresponded feature values for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

Abstract: A fruit picking method based on a three-dimensional parameter prediction model for a fruit comprises: performing first-time image acquisition processing on a to-be-picked fruit to obtain a first image; determining a first range; controlling a manipulator to perform first-time moving processing; performing intermittent gas injection treatment to lead to forced vibration of the to-be-picked fruit; performing second-time image acquisition processing many times to obtain a plurality of second images; screening out, by taking the first image as an reference object, two appointed second images deviating from an equilibrium position to the maximum extent; jointly inputting the images into a preset three-dimensional parameter prediction model for the fruit so as to obtain predicted three-dimensional parameters; controlling the manipulator to perform second-time moving processing; and performing cutting processing on a fruit stem position to make the to-be-picked fruit fall onto the manipulator.

Abstract: A computer-implemented method of determining the location of a mobile device comprising a camera. The method comprises the steps of capturing, using the camera, a sequence of images over a period of time; for pairs of consecutive images from the sequence of images, determining, using a first neural network, features indicative of the motion of the device between the time the first image of the pair of images was captured and the time the second image of the pair of images was captured; for a sequence of consecutive images, determining, using a second neural network, features indicative of the location of the device from the features determined by the first neural network; and for a sequence of consecutive images, determining the location of the device from the features determined by the second neural network.

Abstract: A vegetation index calculation apparatus (10) is provided with a learning model generation unit (11) that generates a learning model, by using an image of a crop targeted for calculation of a vegetation index and an image of plants other than the crop to learn a feature amount of the image of the crop, an image acquisition unit (12) that acquires an aerial image of a target region where the crop is being grown, a specification unit (13) that applies the aerial image acquired by the image acquisition unit (12) to the learning model generated by the learning model generation unit (11), and specifies the image of the crop in the aerial image acquired by the image acquisition unit (12), and a vegetation index calculation unit (14) that calculates the vegetation index of the crop, using the image of the crop specified by the specification unit (13).

Abstract: The present invention is for an autonomous aerial vehicle that enables near real-time computation of harvest yield data. Generally, the autonomous aerial vehicle receives combine harvest data from a harvesting vehicle, generates high-resolution yield data based on sensor suite that is on-board the autonomous vehicle, obtains edge compute data from an edge computing device at the edge of the network, and segments the received combine harvest data, the generated high-resolution yield data, and the obtained edge compute data. The aerial vehicle applies data normalization models to the segmented data and computes a normalized harvest yield for at least a portion a land tract. In this manner, the data delivery vehicles computes normalized data that otherwise can by noisy and unreliable.

Abstract: The present invention discloses a method and an apparatus for measuring leaf nitrogen content (LNC), and belongs to the spectral analysis and artificial intelligence (AI) field. The method includes the following steps: (1) obtaining a single-band image of a target leaf illuminated by a light source in a single feature band; (2) repeating step (1), to collect image information in four feature bands; (3) combining the collected images in the four feature bands into a four-channel spectral image; (4) training a deep learning model by using the spectral image and a corresponding nitrogen content label, to obtain a nitrogen content prediction model; (5) transplanting the trained nitrogen content prediction model into an AI control system; (6) collecting information about a to-be-predicted leaf sample, predicting nitrogen content by using an AI sensor equipped with the AI control system, and outputting the predicted nitrogen content.

Abstract: Provided is a system for detection and analysis of a fibrous root system architecture of a plant, including a plant pot having at least one slit area extending through a cross section in an axial direction, at least one laser measuring unit, and a data analyzing unit, where each laser measuring unit has a laser transmitter and a receiver disposed corresponding to the slit area in such a manner that a laser beam emitted from the laser transmitter to the receiver goes across the cross section of the plant pot; measurement on all roots in the slit area is realized by a rotating stage, and the laser measuring unit swinging horizontally around the laser transmitter within a predetermined angle range; and the data analyzing unit is configured to perform statistical analysis on the roots of a plant to be measured according to laser measuring results.

Abstract: A vegetation index calculation apparatus (10) is provided with a learning model generation unit (11) that generates a learning model, by using an image of a crop targeted for calculation of a vegetation index and an image of plants other than the crop to learn a feature amount of the image of the crop, an image acquisition unit (12) that acquires an aerial image of a target region where the crop is being grown, a specification unit (13) that applies the aerial image acquired by the image acquisition unit (12) to the learning model generated by the learning model generation unit (11), and specifies the image of the crop in the aerial image acquired by the image acquisition unit (12), and a vegetation index calculation unit (14) that calculates the vegetation index of the crop, using the image of the crop specified by the specification unit (13).

Abstract: A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

Abstract: A system and method of grouping images captured using an image capture device. The method comprises receiving a plurality of images, each of the plurality of images having associated camera settings; and determining an inertial profile for the plurality of images based on acceleration data of the image capture device and an imaging entity at pre-determined length of time before and after capture of the each of the plurality of images. The method further comprises forming image groups from the received plurality of images based on the determined inertial profile, and the associated camera settings.

Abstract: A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.

Abstract: A plant treatment system automatically adjusts camera operation parameters for a camera used by the plant treatment system to identify and treat plants in a field. The plant treatment system can generate image segments of images received from the camera and classify the image segments based on whether the image segments represent plants. The plant treatment system determines whether each of the image segments is over- or under-exposed and adjusts the camera operation parameters for the camera based on the exposure classification of the image segments. Alternatively, the plant treatment system may use a plant detection model to identify plant pixels within an image that represent plants. The plant treatment system can then determine whether the identified plant pixels are over- or under-exposed and adjust the camera operation parameters accordingly.

Abstract: In accordance with an embodiment, a weighing apparatus includes a plurality of weighing scales and a processor. Each of the plurality of weighing scales measures weight of the object to be weighed which is placed on the plurality of weighing scales. The processor calculates net weight of the object to be weighed on the basis of the weight measured by each of the plurality of weighing scales.

Abstract: A display device may include a memory configured to store correction data during a first period, a timing controller configured to generate second data by correcting the first data using the correction data, during a second period, and a terminal unit configured to transmit, to the memory, the correction data and a memory voltage received from an external device, during the first period. During the first period, the memory may be supplied with the memory voltage through a memory voltage line and driven by the memory voltage.

Abstract: A three-dimensional object inspecting device for inspecting a three-dimensional object includes a light source, a detector, an orientation information acquisition component, a three-dimensional shading corrector, and an inspection component. The light source emits light energy toward an inspection region set for the three-dimensional object. The detector detects radiant energy radiated from the inspection region. The orientation information acquisition component acquires orientation information about the light source and the detector with respect to the inspection region. The three-dimensional shading corrector performs three-dimensional shading correction on information corresponding to the radiant energy detected by the detector, based on shape information about the three-dimensional object in the inspection region, the orientation information, and shading correction information for a planar image detected by the detector for each of a plurality of working distances.

Abstract: Systems and methods for facilitating communication among grow pods are provided. One embodiment of a system includes a remote computing device that includes a memory component that stores logic. The logic may cause the remote computing device to receive a grow recipe that includes actions for a particular grow pod to grow a plant, implement the grow recipe on the particular grow pod; determine an output of the plant in the particular grow pod using the grow recipe, and determine a random adjustment to the grow recipe. In some embodiments, the logic may cause the computing device to determine an output of the plant in the particular grow pod using the random adjustment to the grow recipe and determine whether the random adjustment to the grow recipe resulted in an improvement in output.

Abstract: A system to collect information about plants on a per plant or near per plant basis and to promulgate remedial courses of action is disclosed. A horticultural operation collects plant information, aggregates the information, and allows an administrative worker such as a master grower to review issues about plants in a grow operations and the associated potential remedial course of action. The administrative worker can then browse data about the affected plants to determine whether to select the potential remedial course of action. Browsing and review of issues may be done collaboratively by remote administrative workers. Remedial courses of action are then dispatched to line workers solely using an iconographic language, who in turn provide notifications when the courses of action are done. Administrative workers may then validate and verify that the courses of action have been properly executed and evaluate the courses of action's efficacy.

Abstract: In one embodiment, a recognition system has an imaging device, a storage device, and a first processor. The first processor extracts a feature amount of an article contained in image data outputted by the imaging device. The first processor recognizes a commodity to which the detected article corresponds, based on similarities between feature amounts for collation of a first commodity and a second commodity stored in the storage device and the extracted feature amount, and selects the recognized commodity from the storage device, as the commodity to which the detected article corresponds, provided that the recognized corresponding commodity is not the first commodity.

Abstract: A digital-to-analog converter may include a converting unit and a distributing unit. The converting unit may generate a first analog signal set based on less-than-all bits of an image data set in a first period and may generate a second analog signal set based on all bits of the image data set in a second period. The all bits may include at least 2 bits. The distributing unit may include output terminals, may distribute the first analog signal set to the output terminals in a first sequence in the first period, and may distribute the second analog signal to the output terminals in a second sequence. The second sequence may be opposite to the first sequence.

Abstract: A method for identifying the presence of fruit in image data in an image sensor of a scene includes acquiring image data in an image sensor for at least two distinct wavelengths of a scene. A normalized difference reflectivity index (NDRI) for each location in an array of locations in the image data is calculated with respect to said at least two distinct wavelengths. Regions in the array of locations are identified where the value of the calculated NDRI of the locations in these regions is within a range of values indicative of a presence of fruits in the scene. An output is generated on an output device with information related to the identified presence of fruits.

Abstract: First imaging device collects first image data, whereas second imaging device collects second image data of a storage portion. An image processing module identifies a rim of a container in either the first image data or the second image data. The image processing module then overlays an outline of the rim in an image presented on a display to an operator of a transferring vehicle. The image processing module further identifies the fill level of material in a receiving vehicle container and overlays this information in the image presented on the display.

Abstract: An apparatus for acquiring a profile of a food product for use in subsequent processing of the food product includes a scanning area and one or more product drives for driving a product through the scanning area in a longitudinal direction. First line lasers project one first transverse laser line transversely to the longitudinal direction on the first surface and two first cameras arranged to capture different, overlapping first transverse image portions of the first transverse laser line. A reference laser projects a beam on the first surface that indicates a transverse reference position, and the two first cameras also capture the reference position. A control system uses the transverse reference position to combine the different first transverse image portions captured by the two first cameras to calculate a first profile of the first surface at multiple positions along a length of the first surface as the product is driven through the scanning area.

Abstract: The present disclosure provides a system and method for determining a nutritional value of a food item. The system and method utilizes a food container as a model to adjust various types of distortions that exists in an instant image of the food container that retains the food item. The instant image may be compared to the model image of the food container to correct any distortions. The food container includes a boundary which has a predetermined color. The predetermined color of the boundary can be used to adjust the color configuration of the instant image, thereby increasing the accuracy of the food identification.

Abstract: A grain quality sensor comprising a lens, a filter, a photosite array, an illumination source, and an electronics module, wherein the illumination source directs light containing a known set of wavelengths onto a crop sample, wherein the lens picks up light reflected by the crop sample and directs it into the filter, which allows light to pass into different parts of the photosite array such that certain locations on the photosite array only get certain frequencies of the reflected light, wherein the electronics module is electrically connected to the photosite array and capable of determining which parts of the photosite array received light and what frequency the light received was, wherein the electronics module can analyze the optical data received by the photosite array, and wherein the analysis of the optical data is used to determine the composition of different parts of the crop sample.

Abstract: Embodiments relate generally to new and useful systems and methods for processing digital images of items to facilitate item transport and/or handling. In some embodiments, a method is provided for operating an image processing and item transport system having a computing system that includes a digital image processing component. In some embodiments, the method can involve the digital image processing component acquiring a digital image and processing the digital image to obtain a digital representation of an item represented in the digital image, to estimate a dimensional size and/or weight of the item represented in the digital image, and to convert the digital image to a format suitable for outputting.

Abstract: Robotic systems and specialized end-effectors provide for automated harvesting of produce such as fresh market apples. An underactuated design using tendons and flexure joints with passive compliance increases robustness to position error, overcoming a significant limitation of previous fruit harvesting end-effectors. Some devices use open-loop control, provide a shape-adaptive grasp, and produce contact forces similar to those used during optimal hand picking patterns. Other benefits include relatively low weight, low cost, and simplicity.

Abstract: A method of computing physiological measurements resulting from a multi-scale physiological system using a data-driven model includes generating a database of physiological measurements associated with a multi-scale physiological system. A computer uses dimensionality reduction techniques on the database to identify a reduced set of components explaining the multi-scale physiological system. The computer learns a data-driven model of the multi-scale physiological system from the database. Then, new input parameters are received by the computer and used to compute new physiological measurements using the data-driven model. New derived physiological indicators are computed by the computer based on the reduced set of components. Once computed, the new derived physiological indicators may be displayed along with the new physiological measurements.

Abstract: A system and method to inspect flaws associated with a part. The system includes a first image capturing device configured to capture a first set of images of the part and a computer operably associated with first image capturing device and configured to receive and analyze the first set of images. The method includes treating the part with a nital etchant solution, capturing a first set of images of an outer surface of the part with the first image capturing device, and identifying a part defect with an algorithm associated with the computer.

Abstract: A harvesting system includes multiple robots, one or more sensors and one or more computers. The robots are mounted on a common frame facing an area to be harvested, and are each configured to harvest crop items by reaching and gripping the crop items from a fixed angle of approach. The sensors are configured to acquire images of the area. The computers are configured to identify the crop items in the images, and to direct the robots to harvest the identified crop items.

Abstract: A method of selectively harvesting fruits includes generating a first series of images of a plant while the plant is moved to expose hidden fruits, identifying an object displayed in the first series of images as a suspect fruit from feature boundary data defined by color regions, comparing a color parameter of the suspect fruit to a criterion associated with a ripeness, advancing an automated picker toward the suspect fruit based on a determined position of the suspect fruit and while monitoring a proximity sensor of the automated picker, detecting a color of the impediment and confirming that the impediment is a fruit, operating the automated picker to pick the fruit from the plant, and while the automated picker is operated to pick the fruit, generating a second series of images of one or more additional plants while the one or more additional plants are moved to expose additional fruits.

Abstract: An image processing device includes: an identifying unit that identifies an image area where gradations are lost in a first captured image obtained by capturing a subject image; an image texture image generation unit that generates an image texture image by aligning a second captured image, obtained by capturing an image of a subject matching the subject of the first captured image, with the first captured image and extracting an image texture corresponding to the image area having been identified by the identifying unit; and an interpolation unit that executes interpolation for the image area in the first captured image by synthetically incorporating the image texture image having been generated by the image texture image generation unit with the first captured image.

Abstract: A process for automated high-throughput fruit or vegetable calyx removal includes a material handling system, a vision system, and a cutting system. The material handling system is capable of transporting the fruit or vegetable through the automated process. The material handling system may also orient the fruits or vegetables along an axis of the fruit and or align the fruit or vegetables in a desired pattern, orientation, and/or configuration. The vision system identifies the calyx and determines calyx position data and optimal cutting angle for individual fruit. The cutting system uses data received from the vision system to automatically remove the calyx from the fruit or vegetables.

Abstract: The present disclosure provides a system and method for determining a nutritional value of a food item. The system and method utilizes a food container as a model to adjust various types of distortions that exists in an instant image of the food container that retains the food item. The instant image may be compared to the model image of the food container to correct any distortions. The food container includes a boundary which has a predetermined color. The predetermined color of the boundary can be used to adjust the color configuration of the instant image, thereby increasing the accuracy of the food identification.

Abstract: A camera system (10), in particular a camera-based code reading system, for detecting a stream of objects (14) moved relative to the camera system (14) is provided, wherein the camera system (10) has a plurality of detection units (18) each having an image sensor and a focusing unit (20) as well as at least one control unit (30, 32) to set the focal positions of the detection units (18) in a complementary manner on a simultaneous detection of a plurality of objects (14) such that as many objects (14) as possible are detected in focus by at least one detection unit (18). In this respect, an evaluation unit (32) is provided which is configured to compose image data of the detection units (18) to form a common image.

Abstract: In a method for milling an opencast mining surface or for milling off layers of an asphalt or concrete traffic surface with a milling machine removing the ground surface, by milling the ground surface along a predetermined milling track and by transporting the milled material via a conveying device to at least one container of a truck that travels along next to the milling machine, a relative position between the container and the conveying device is automatically controlled to regulate the loading of the container.

Abstract: A method of distinguishing individual plants within a row of plants, including directing radiation at the row of plants at an angle selected to illuminate a portion of the plant and cast a shadow at the plant center, collecting an image from the radiation reflected off of two or more contiguous plants with a detector, identifying a continuous foreground region indicative of a plant within the image, identifying points of interest within the region, classifying the points of interest as plant centers and non-plant centers, and segmenting the region into sub-regions, each sub-region encompassing a single point of interest classified as a plant center.

Abstract: The single-camera multi-mirror imaging method and apparatus is an inspection system configured to examine a whole surface of a rotating object, preferably a spheroidal object such as a fruit or vegetable. The system includes a plurality of mirrors that direct an image of the inspected object into a digital line scan camera with an associated processor. The processor produces an image of the inspected object showing any detected surface defects and selected contamination on the outer surface of the object.

Abstract: A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Abstract: A system for in-vehicle monitoring and control of commodity loading, such a grain or loose or fluid materials, from a storage bin to a transport bin using a loading system, includes a loading system control device and a user interface device capable of mutual wireless communication. The system improves safety by eliminating the need for operators to crawl up the side of vehicle in order to check levels of commodity in the transport bin, while also trying to move the transport bin as required. The loading system control device includes a logic switching unit for selectively operating the loading system that transfers the commodity from the storage bin; one or more cameras providing visual feeds of the loading of commodity into the transport bin; and a security or safety feature. The user interface device includes a display for displaying the visual feeds and control switches for operating the loading system.

Abstract: The present disclosure provides systems and methods for determining the presence of a contaminate in a food sample. Interacted photons from a food sample having a contaminate of interest are collected. The interacted photons are passed through a tunable filter to a hyperspectral detector that generates a hyperspectral image representative of the filtered interacted photons. The hyperspectral image is analyzed by comparing the hyperspectral image obtained from the food sample to known hyperspectral images to identify a contaminate in the food sample. The systems and methods disclosed herein provide an easy and non-destructive tool for identifying contaminates in a food sample.

Abstract: The present invention relates to a food processing device, for example but not exclusively, a gripper, a conveying hook, or a collection cup (1), having at least one processing surface portion of matt stainless steel having the following roughness characteristics:—a roughness value Ra, 0.2?Ra?1.2 (?m), and—a peak density value Rpc, 40<Rpc<140 (1/cm). It has been found that with devices treated in this manner, the application of vision technology is appreciably improved, so that much more reliable images are obtained. An example of this concerns observing yolk in albumen upon the breaking of eggs in egg breakers. Further, a particularly advantageous combination of usefulness is obtained, in this case for vision and cleanability.

Abstract: Disclosed herein are systems for identifying a plant in a field with pixel-level precision and taking an action on the identified plant, also with pixel level precision. The disclosed systems can include multiple implements, enabling the performance of multiple tasks on an identified plant or associated area of soil. Methods of using the described systems for automated thinning, weeding and spot spraying crops are also disclosed.

Abstract: System and methods for farming of crops on a plant-by-plant basis are disclosed. Plants are imaged and data is acquired on a plant-by-plant basis, which enables the visual micro management of a crop field on a plant-by-plant basis. Past and current images of a plant may be displayed in a manner that allows plants to be diagnosed and maintained. In some embodiments, images of an individual plant and instructions for maintenance are automatically displayed to a field work in real-time as the worker is in the proximity of the plant. Techniques described herein can be used in the field or during the harvest process including sorting tables or sorting conveyor belts. This can be done via signage, bar codes or RFID tags or other unique plant identifier technique. This approach compresses the acquired data and automatically selects and displays relevant information to a specific fruit or plant.

Abstract: Methods and systems using a vision system to process shrimp. The vision system captures images of samples of shrimps. The processor produces a digital image of the shrimps in the samples. Shrimps exiting a peeler are imaged to determine the number of tail segments in each. The shrimps are classified by the number of intact segments, and quality, yield, and throughput computed from the classification results. The processor can control operational settings of the peeler based on the classification results. In a larger system including other shrimp-processing equipment besides the peeler, other points along the processing path can be imaged by camera or sensed by other sensors to determine processing quality and to make automatic operational adjustments to the equipment.

Abstract: Systems and methods that improve farmers' ability to measure quantities of dry agricultural materials and rates of change of those quantities are disclosed. Also disclosed are systems and methods to rapidly and effectively communicate materials quantities and rates between farm equipment operators.

Abstract: A system and method for calibrating a dynamic digital imaging system for the detection of defects in a moving product stream. The system has an elevated platform above a conveying unit for receiving a reference color tile. The elevated platform allows for the passage of products to be inspected on the below conveying unit surface such that calibration and re-calibration processes during image capturing may be accomplished on a continuous basis without interruption of the product stream.

Abstract: A mobile device includes a processor; and an imaging unit that records subjects as images. The processor is configured to detect an input operation of selecting an item among a group of items representing any among an object and an event possibly motivating a recording of a subject by a person engaged in farm work, output a record instruction to the imaging unit upon detecting the input operation, correlate the item for which the input operation is detected and an image that is recorded by the imaging unit consequent to the output record instruction, and output a result of correlation.

Abstract: System for imaging pieces of sawn timber travelling transversely past the system. Each piece of sawn timber includes a top surface and a bottom surface, with sides connecting them. The system is arranged to image the top surface and at least one side when the sawn piece travels past the system's measuring area. At least one matrix camera is positioned in a first position relative to the path of the sawn pieces. At least one minor is positioned in a second position relative to the path of the sawn pieces and to the said first position. The first and second positions are selected in such a way that each sawn piece travelling past transversely can be imaged using the at least one matrix camera directly from a first direction and as a mirror image through the at least one mirror from at least one second direction.