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 determination system includes: a projection control unit that controls a projection unit to project a random marker within an angle of view of an imaging unit; an acquisition unit that obtains an image of a target person including the marker from the imaging unit; and a determination unit that determines whether or not the target person imaged by the imaging unit is a living body on the basis of a state of the marker included in the image. According to such a determination system, it is possible to accurately determine whether or not the target person is a living body. Therefore, for example, it is possible to avoid a breakthrough of biometric authentication by an illegal method, or the like.

Various embodiments illustrated herein disclose a method that includes receiving a plurality of images from an image capturing unit. Thereafter, an image evaluation process is executed on each of plurality of sections in each of the plurality of images. The image evaluation process includes performing optical character recognition (OCR) on each of the plurality of sections in each of the plurality of images to generate text corresponding to the plurality of respective sections. Further, the image evaluation process includes querying a linguistic database to identify one or more errors in the generated text. Further, the method includes modifying one or more image characteristics of each of the plurality of images and repeating the execution of the image evaluation process on the modified plurality of images until at least the calculated statistical score is less than a pre-defined statistical score threshold.

Methods, devices, and systems for identifying charging devices are provided. In one aspect, a method of identifying a charging device include: capturing an infrared image and a depth image of a current field of view with a depth camera; determining, according to the infrared image, that there are one or more suspected charging device areas that satisfy first specified conditions; determining, according to the depth image, that there is a target charging device area whose height relative to a depth camera is within a specified range in the one or more suspected charging device areas; and identifying the charging device according to the target charging device area. The first specified conditions indicate that a gray-scale value of each of pixels in an area is greater than a second specified value, and a number of the pixels in the area is greater than a third specified value.

A method for semantic annotation of sensor data may include obtaining sensor data representing an image of a geographic area. The boundary points defining a first polygon in the image of the geographic area may be determined based on the sensor data. An overlap between the first polygon and a second polygon in the image of the geographic area may be detected based at least on the boundary points defining the first polygon. At least one of the first polygon or the second polygon may be modified to remove the overlap between the first polygon and the second polygon. An annotation corresponding to the first polygon may be generated based on the modifying of at least one of the first polygon or the second polygon. The annotation may identify a physical feature within the geographic area. Related systems and computer program products are also provided.

Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.

This invention provides a system and method for displaying color match information on an acquired image of an object. A model/pattern having a plurality of color test points at locations of stable color is provided. A display process generates visible geometric shapes with respect to the color test points in a predetermined color. An alignment process aligns features of the object with respect to features on the model so that the geometric shapes appear in locations on the object that correspond to locations on the model. The geometric shapes can comprise closed shapes that surround a region expected to be stable color on the object. Such shapes can define circles, squares, diamonds or any other acceptable closed or open shape that is visible to the user on the display.

A method for using an object key to deprioritize processing of relative regions of images of an object includes capturing an image of an object to be tracked and selecting an object key of the object. The object key includes a portion of the object, the object key is attached to the object and is recognizable by an image capture device. The method includes calculating a relative size of the object key with respect to a size of the object and a location of the object key with respect to the object, and tracking one or more locations of the object from the relative size and location of the object with respect to the object key and by capturing a plurality of images of the object key at a resolution sufficient for tracking movement of the object key.

Technologies pertaining to electronic document understanding are described herein. A document is received, wherein the document includes a section of a type. An image of the document is generated, and a candidate region is identified in the image of the document, wherein the candidate region encompasses the section. A label is assigned to the candidate region based upon text of the section, wherein the label identifies the type of the section. An electronic document understanding task is performed based upon the label assigned to the candidate region.