A system and method are disclosed for using a local neighborhood for determining similar targets in different documents or using implicit coordinates for obtaining a coordinate location of a target. The local neighborhood method may include identifying a first target in a first document; identifying one or more first elements within a first distance range from the first target; creating a first local neighborhood based on the identifying; determining that that first local neighborhood is similar to a third local neighborhood in a second document; and determining a second target in the second document that corresponds to the first target in the first document, based on the determining the similarity. The implicit coordinates method may include performing OCR on the first document to find the first target; and obtaining a first location of the first target by using at least one of OCR or element recognition.

The present invention provides a processing apparatus (10) including: an acquisition unit (11) that acquires an image generated by a plurality of cameras for photographing a product picked up by a customer; a recognition unit (12) that recognizes the product, based on each of a plurality of images generated by the plurality of cameras; and a determination unit (13) that determines a final recognition result, based on a plurality of recognition results based on each of the plurality of images, and a size of a region where the product is present within each of the plurality of images.

Examples of methods for fiducial pattern detection are described herein. In some examples, a method may include detecting fiducial pattern subsets in image subsets of an image of an object. In some examples, the method may also include selecting a first image subset that includes a largest first fiducial pattern subset. In some examples, the method may further include extending the first fiducial pattern subset from the first image subset to a neighboring second image subset.

The present invention relates to a method for authenticating a document comprising at least one line of anti-counterfeit patterns spaced from each other, the positions of the anti-counterfeit patterns being random, and a partial anti-counterfeit pattern being provided at an edge line of said document, the method comprising the following steps of:

receiving a digital image of said document, said digital image comprising said partial anti-counterfeit pattern in a first edge area corresponding to said edge line;

selecting said first edge area;

copy-pasting said first edge area adjacent to a second edge area of the digital image to generate a combined digital image, said second edge area being opposite to said first edge area, wherein content, which is comprised in said second edge area and located on the same line as said partial anti-counterfeit pattern, and said partial anti-counterfeit pattern jointly form a combined pattern;

authenticating said document by taking into account said combined pattern.

A method for detecting a field navigation line after ridge sealing of crops includes the following steps. A field crop image is acquired. Image color space transformation, image binaryzation, longitudinal integration, neighborhood setting and region integration calculation are sequentially performed on the field crop image to obtain a crop row image. Detections of an initial middle ridge, a left ridge and a right ridge are performed on the crop row image to obtain center lines of the initial middle ridge, left ridge and right ridge. Center lines of a left (right) crop row are established by using an area 1 between the center lines of the left (right) ridge and the initial middle ridge. A center line model of a middle ridge is established by using an area 0 between the center lines of the left and right crop rows, namely a navigation line of a field operation machine.

Systems and methods for lost asset management using photo-matching are disclosed herein. An example method includes capturing a lost asset image corresponding to a lost asset, and generating, by a feature extractor model, a lost asset descriptor that represents features of the lost asset image. The example method also includes storing the lost asset descriptor and the lost asset image in an asset database that includes known asset descriptors, and performing, by a visual search engine, a nearest neighbor search within the asset database to determine a respective metric distance between the lost asset descriptor and the known asset descriptors. The example method also includes determining, by the visual search engine, a ranked list of known assets corresponding to the lost asset, and displaying, at a user interface, the ranked list of known assets for viewing by a user.

A method for detecting a double-parked vehicle includes identifying a parking region in video data received from an image capture device monitoring the parking region. The method includes defining an enforcement region at least partially surrounding the parking region. The method includes detecting a stationary vehicle in the enforcement region. The method includes determining the occurrence of an event relative to the stationary vehicle. In response to the determined occurrence of the event, the method includes classifying the stationary vehicle as being one of double parked and not double parked.

An approach is disclosed that captures, at a digital camera of a first information handling system, a digital image of a display of a second information handling system. The approach analyzes the captured digital image with the analysis resulting in an identification of a network location that corresponds to the captured digital image. Data from the identified network location is retrieved via a network connection from the first information handling system and this data is displayed on a display that is accessible by the first information handling system.

An efficient K-nearest neighbor search algorithm for three-dimensional (3D) lidar point cloud in unmanned driving and a use of the foregoing K-nearest neighbor search algorithm in a point cloud map matching process in the unmanned driving are provided. A novel data structure for fast K-nearest neighbor search is used, such that each voxel or sub-voxel includes a proper quantity of points to reduce redundant search. The novel K-nearest neighbor search algorithm is based on a double segmentation voxel structure (DSVS) and a field programmable gate array (FPGA). By means of the novel K-nearest neighbor search algorithm, nearest neighbors are searched for only in a neighboring expected area near a search point, thereby reducing search of redundant points. In addition, an optimized data transmission and access policy is used, which makes the algorithm more fit the characteristic of the FPGA.

An image processing apparatus acquires a plurality of captured images of characters captured in time series, each of the characters including a plurality of segments, recognize the characters captured for each of the plurality of captured images, and determine which one of the characters recognized from each of the plurality of captured images is to be output. The image processing apparatus determines, in accordance with a change aspect in time series of the characters recognized from each of the plurality of captured images, which one of the characters recognized from each of the plurality of captured images is to be output.