In a graphics processing unit (GPU), receiving an input image comprising an array of pixels. Each pixel having a grayscale value from a range of N grayscale values. For each particular input patch of pixels of a set of input patches partitioning the input image and in parallel for each particular grayscale value the range, counting the number of pixels in the particular input patch having the particular grayscale value. In parallel for each particular input patch of pixels of a set of input patches partitioning the input image, creating an output image patch as an ordered sequence of N pixels, with the color value of the nth pixel in each corresponding output patch representing the count of pixels in the particular input patch having the nth grayscale value. Combining the output image patches into a single composite output image of N pixels, the pixel value of the nth pixel in the single composite output image corresponding to the count of pixels in the input image having the nth grayscale value.

A radiographing system that can attach a dose index for a composition image and perform dose management in a composition image is provided. The radiographing system includes: a dose index calculation unit configured to respectively calculate dose indices from a plurality of radiographic images, an obtaining unit configured to obtain a representative dose index from among the plurality of dose indices calculated by the dose index calculation unit, and a storage unit configured to store the representative dose index together with the composition image.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

An electronic device includes a sensor module, a dual camera including a first image sensor and a second image sensor, and a controller that processes first image data and second image data. The controller allows at least one of the first image sensor and the second image sensor to maintain a power restricted state based on at least one of a first condition associated with information extracted from the first image data or the second image data, a second condition associated with sensing information collected by the sensor module, and a third condition associated with a zoom characteristic of each of a plurality of lenses, a respective one of the plurality of lenses being mounted in each of the first image sensor and the second image sensor.

The present disclosure is directed to systems and methods for strike through detection and, more particularly, to systems and methods for detecting a strike through in an address block of a mailpiece. The method is implemented in a computing device and includes: generating edges of lines within a text block identified through optical character recognition processes; locating text lines within the text block; characterizing the edges within the text lines and outside of the text lines; and grouping identified edges of the characterized edges outside of the text lines into co-linear groups.

The accuracy of an image matching process can be improved by determining relevant swatch regions of the images, where those regions contain representative patterns of the items of interest represented in those images. Various processes examine a set of visual cues to determine at least one candidate object region, and then collate these regions to determine one or more representative swatch images. For apparel items, this can include locating regions such as an upper body region, torso region, clothing region, foreground region, and the like. Processes such as regression analysis or probability mapping can be used on the collated region data (along with confidence and/or probability values) to determine the appropriate swatch regions.

Disclosed is an image quality evaluation method for a digital pathology system according to the present invention. The image quality evaluation method includes receiving a digital slide image by an image quality evaluation unit; dividing the digital slide image into a plurality of blocks by the image quality evaluation unit; analyzing the plurality of blocks to extract a foreground; calculating a blur for the extracted foreground; calculating brightness distortion for the extracted foreground; calculating contrast distortion for the extracted foreground; and evaluating the overall quality of the digital slide image using the blur, the brightness distortion, and the contrast distortion by the image quality evaluation unit.

A management system for supervising an operator within an operation area with a processing apparatus installed therein has an imaging camera for capturing an image of the operator, and a control unit. The control unit stores an area map that includes information about the processing apparatus and the positions of the processing apparatus, acquires identifying information regarding the operator on the basis of the image captured by the imaging camera, and extracts traffic lines of the operator. A management unit records the traffic lines of the operator and the identifying information of the operator, in relation to each other on the area map.

Disclosed is an image compression method of a digital pathology system. The image compression method is a method of compressing digital slide images having first to nth original plane images (n is a natural number greater than or equal to 2). The image compression method includes selecting a block having an optimal focal point as an optimal block from each set of blocks positioned at identical positions of the first to nth original plane images; forming one plane image as a virtual optimal plane image by combining only the optimal blocks; generating block descriptors for forming the first to nth original plane images based on the virtual optimal plane image; generating first to nth predictive plane images from the virtual optimal plane image such that the first to nth predictive plane images are the least out of focus by using location information for the blocks and the block descriptors; generating first to nth differential plane images, the first differential plane image corresponding to a difference between the first original plane image and the first predictive plane image and the nth differential plane image corresponding to a difference between the nth original plane image and the nth predictive plane image; and compressing the first to nth differential plane images.

Methods and apparatus distinguish invasive adenocarcinoma (IA) from in situ adenocarcinoma (AIS). One example apparatus includes a set of circuits, and a data store that stores three dimensional (3D) radiological images of tissue demonstrating IA or AIS. The set of circuits includes a classification circuit that generates an invasiveness classification for a diagnostic 3D radiological image, a training circuit that trains the classification circuit to identify a texture feature associated with IA, an image acquisition circuit that acquires a diagnostic 3D radiological image of a region of tissue demonstrating cancerous pathology and that provides the diagnostic 3D radiological image to the classification circuit, and a prediction circuit that generates an invasiveness score based on the diagnostic 3D radiological image and the invasiveness classification. The training circuit trains the classification circuit using a set of 3D histological reconstructions combined with the set of 3D radiological images.