An identification module is disclosed for use in a key making machine. The identification module may have a key receiving assembly configured to receive only a shank of an existing key. The identification module may also have a tip guide, configured to receive a tip of the shank of the existing key. The tip guide may have a slot that exposes a tip end of the shank. The identification module may also have an imaging assembly configured to capture an image of the tip end through the slot.

The disclosure provides a surface inspection apparatus for inspecting a surface of an object, a non-transitory computer readable medium thereof, and a surface inspection method thereof. According to an aspect of the disclosure, the surface inspection apparatus includes an imaging device configured to image a surface of an object to be inspected, and a processor configured to calculate a numerical value representing a quality of the surface by processing an image captured by the imaging device, and display, on a display device, the image including an index for specifying a position of a portion that has contributed to the calculation of the numerical value and the numerical value.

A system and a method for tracing components of an electronic assembly. The method may include obtaining an image of a component of the electronic assembly, generating identification information of the component based on visual features of the component, authenticating the component based on the identification information, generating correlating information by associating the component to an electronic assembly and classifying the component to a group based on the visual features of the component.

Embodiments relate to in-situ process monitoring of a part being made via additive manufacturing. The process can involve capturing computed tomography (CT) scans of a post-built part. A neural network (NN) can be used during the build of a new part to process multi-modal sensor data. Spatial and temporal registration techniques can be used to align the data to x,y,z coordinates on the build plate. During the build of the part, the multi-modal sensor data can be superimposed on the build plate. Machine learning can be used to train the NN to correlate the sensor data to a defect label or a non-defect label by looking to certain patterns in the sensor data at the x,y,z location to identify a defect in the CT scan at x,y,z. The NN can then be used to predict where defects are or will occur during an actual build of a part.

An object manipulation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: calculate, based on an image in which one or more objects to be grasped are contained, an evaluation value of a first behavior manner of grasping the one or more objects; generate information representing a second behavior manner based on the image and a plurality of evaluation values of the first behavior manner; and control actuation of grasping the object to be grasped in accordance with the information being generated.

A system (100A) to track logs in a wood processing chain, includes a database arrangement (102) that includes pre-recorded image of a given log, wherein the given log is associated with log identification information. The system further includes a plurality of imaging devices implemented at a sorting station. The plurality of imaging devices (104) is configured to capture a first set of images from at least a first prespecified oblique angle. The system further includes a data processing arrangement (106) that is configured to: identify the given log at the sorting station; compare the at least one pre-recorded image with the captured first set of images at the sorting station in order to find an optimum image from the compared images for identification of the given log; determine a plurality of physical characteristics; and append the log identification information with the determined physical characteristics of the given log.

Described herein are mobile sensing units for capturing raw data corresponding to certain characteristics of plants and their growing environment. Also described are computer devices and related methods for collecting user inputs, generating information relating to the plants and/or growing environment based on the raw data and user inputs, and displaying same.

Systems, methods, storage media, and computing platforms for scanning items at the point of manufacturing are disclosed. Exemplary implementations may: receive a first set of images of an item from a first set of camera sources; detect a code in the first set of images; combine, responsive to detecting the code, along a second axis perpendicular to the first axis, the first set of images into a first set of combined images; rotate parallel to the first axis; and combine along the first axis.

A method of analyzing a product includes performing an anomaly detection on a received image using an autoencoder, wherein the autoencoder includes at least one first neural network trained based on a first set of training images, and the first set of training images includes a plurality of training images each showing a corresponding defect-free product; determining, using a binary classifier, whether or not a defect is present based on a result of the anomaly detection; performing defect detection on the received image using a defect detector, wherein the defect detector includes a third neural network trained based on a one third set of training images, and the third set of training images includes a plurality of training images each showing a corresponding defective product; and evaluating a result based on a weighting of the results of the anomaly detection, the defect detection, and the binary classifier.

A method including taking, with a camera, an image of a physical object. The method also includes processing the image by converting the image into a vector data file. Processing the image also includes inputting the vector data file to a trained machine learning model artificial intelligence (AI). Processing the image also includes executing the AI to produce an output including a classification of an anomaly in the image. Processing the image also includes converting the output into a reconfigured output including a data format configured for use with augmented reality (AR) hardware. The method also includes transmitting the reconfigured output to the AR hardware. The method also includes displaying, using the reconfigured output, the physical object on a display device of the AR hardware. The method also includes highlighting, on the display device concurrently with displaying the physical object, the anomaly.