A method of identifying and recognizing characters using a dual-stage neural network pipeline, the method including: receiving, by a computing device, image data; providing the image data to a first convolutional layer of a convolutional neural network (CNN); applying, using the CNN, pattern recognition to the image data to identify a region of the image data containing text; providing sub-image data comprising the identified region of the image data to a convolutional recurrent neural network (CRNN); and recognizing, using the CRNN, the characters within the sub-image data.

This invention applies dynamic weighting between a point-to-plane and point-to-edge metric on a per-edge basis in an acquired image using a vision system. This allows an applied ICP technique to be significantly more robust to a variety of object geometries and/or occlusions. A system and method herein provides an energy function that is minimized to generate candidate 3D poses for use in alignment of runtime 3D image data of an object with model 3D image data. Since normals are much more accurate than edges, the use of normal is desirable when possible. However, in some use cases, such as a plane, edges provide information in relative directions the normals do not. Hence the system and method defines a “normal information matrix”, which represents the directions in which sufficient information is present. Performing (e.g.) a principal component analysis (PCA) on this matrix provides a basis for the available information.

In a method for performing adaptive content classification of a video content item, frames of a video content item are analyzed at a sampling rate for a type of content, wherein the sampling rate dictates a frequency at which frames of the video content item are analyzed. Responsive to identifying content within at least one frame indicative of the type of content, the sampling rate of the frames is increased. Responsive to not identifying content within at least one frame indicative of the type of content, the sampling rate of the frames is decreased. It is determined whether the video content item includes the type of content based on the analyzing the frames.

A method for detecting comparison persons 7 to a search person 4, wherein a plurality of classification persons 3 is classified by extracting values W1,W2,W3 for classification features K1,K2,K3 from classification images 2 of the classification persons 3, the classification being ambiguous in such a way that the classification does not enable a unique identification of any of the classification persons 3, wherein during a search for a search person 4 using a search image 5 by a comparison of values of search features from the search image 5 with values W1,W2,W3 of classification features K1,K2,K3, at least two classification persons 3 are output as comparison persons 7.

Embodiments are disclosed for generating tables from charts. The techniques include determining a chart type of a selected chart. The techniques include determining a plurality of chart elements of the selected chart. Further, the techniques include determining a plurality of measurements of a plurality of data representations of the selected chart. Additionally, the techniques include determining a plurality of corresponding numeric values for the measurements based on the chart elements. Also, the techniques include generating a data table comprising the chart elements and the corresponding numeric values.

An object detection device detects a predetermined object from an image. The object detection device includes a first detection unit configured to detect a plurality of candidate regions where the predetermined object exists from the image, a region integrating unit configured to determine one or a plurality of integrated regions according to the plurality of candidate regions detected by the first detection unit, and a second detection unit configured to detect, in the one or the plurality of integrated regions, the predetermined object by using a detection algorithm different from an algorithm of the first detection unit. As a result, it is possible to detect the predetermined object faster and more accurately than before.

A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.

The purpose of the present invention is to obtain reliable recognition performance even in a lens which is capable of long-distance and close proximity photography. This object recognition device 1 comprises an image acquisition unit which acquires an image 1000 which includes a plurality of image regions 1001, 1002, 1003 which have differing resolutions, and a recognition processing unit which carries out a recognition process upon an object within the screen 1000. One image region 1003, among the plurality of image regions 1001, 1002, 1003 of the screen 1000, has a lower resolution than the other image regions 1001, 1002. The recognition processing unit carries out the recognition process of the object on the basis of assessment references which differ among the one image region 1003 and the other image regions 1001, 1002.

A data generation apparatus includes: an acquisition unit configured to acquire an image of an object to be inspected including a defect, and a region of the image that includes the defect; a correction unit configured to correct the region acquired by the acquisition unit by expanding an outer edge of the region so that the number of pixels included in the region is increased by a predetermined amount; and a generation unit configured to generate learning data by associating the region corrected by the correction unit with the image.

In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.