A method for presenting an image on a display device (100) includes modifying the image by applying a geometric transformation to the image so that an area of the image on the display device is presented to a viewer with higher density of pixels than that in the rest of the image (S18).

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

A computing device may obtain an input image. The input image may have a white point represented by chrominance values that define white color in the input image. Possibly based on colors of the input image, the computing device may generate a two-dimensional chrominance histogram of the input image. The computing device may convolve the two-dimensional chrominance histogram with a filter to create a two-dimensional heat map. Entries in the two-dimensional heat map may represent respective estimates of how close respective tints corresponding to the respective entries are to the white point of the input image. The computing device may select an entry in the two-dimensional heat map that represents a particular value that is within a threshold of a maximum value in the heat map, and based on the selected entry, tint the input image to form an output image.

This document describes methods, systems and computer program products directed to imaging blood cells. The subject matter described in this document can be embodied in a method of classifying white blood cells (WBCs) in a biological sample on a substrate. The method includes acquiring, by an image acquisition device, a plurality of images of a first location on the substrate, and classifying, by a processor, objects in the plurality of images into WBC classification groups. The method also includes identifying, by a processor, objects from at least some classification groups, as unclassified objects, and displaying, on a user interface, the unclassified objects and at least some of the classified objects.

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes: (1) obtaining a video feed, the video feed comprising a plurality of images; and, (2) for each image, analyzing the image to determine whether the image includes a person, the analyzing including: (a) determining that the image includes a potential instance of a person by analyzing the image at a first resolution; (b) in accordance with the determination that the image includes the potential instance, denoting a region around the potential instance; (c) determining whether the region includes an instance of the person by analyzing the region at a second resolution, greater than the first resolution; and (d) in accordance with a determination that the region includes the instance of the person, determining that the image includes the person.

An approach is provided for using a machine learning model for identifying planar region(s) in an image. The approach involves, for example, determining the model for performing image segmentation. The model comprises at least: a trainable filter that convolves the image to generate an input volume comprising a projection of the image at different resolution scales; and feature(s) to identify image region(s) having a texture within a similarity threshold. The approach also involves processing the image using the model by generating the input volume from the image using the trainable filter and extracting the feature(s) from the input volume to determine the region(s) having the texture. The approach further involves determining the planar region(s) by clustering the image regions. The approach further involves generating a planar mask based on the planar region(s). The approach further involves providing the planar mask as an output of the image segmentation.

Embodiments of the invention provide a camera array imaging architecture that computes depth maps for objects within a scene captured by the cameras, and use a near-field sub-array of cameras to compute depth to near-field objects and a far-field sub-array of cameras to compute depth to far-field objects. In particular, a baseline distance between cameras in the near-field subarray is less than a baseline distance between cameras in the far-field sub-array in order to increase the accuracy of the depth map. Some embodiments provide an illumination near-IR light source for use in computing depth maps.

A remote sensing image geometric normalization method and apparatus. The method comprises: constructing a pyramid tile structure for a reference image, and releasing reference tile data, wherein the reference tile data is data in the pyramid tile structure (S11); according to the resolution and geographic coordinates of an image to be subjected to geometric normalization, calculating the level of a tile to be downloaded and the name of the tile to be downloaded, and according to the level of the tile to be downloaded and the name of the tile to be downloaded, downloading corresponding data from the reference tile data to obtain a standard tile set (S12); performing first geometric correction on the image to be subjected to geometric normalization and tiles in the standard tile set to obtain a first image processing result (S13); matching the first image processing result with the tiles in the standard tile set to obtain a plurality of control points, and using the plurality of control points to calculate a result evaluation precision (S14); and according to the result evaluation precision, determining whether to perform second geometric correction on the first image processing result (S15). The method improves the efficiency of processing a remote sensing image.

Systems and methods for multi-level vascular imaging for construction and display of vasculature from large to small vessels and micro-vessels using a combination of varying resolution contrast enhanced ultrasound flow imaging modalities are disclosed. While one or more resolution flow imaging modes may be employed for imaging large to small vessels of a vascular tree within a large region of interest, a high resolution mode, such as super resolution imaging, constructed for delineation of the microvascular morphology and directional microcirculation is provided within one or more small ROIs placed in selected locations within the larger ROI.

A method includes receiving a first image, receiving a motion dataset, determining a motion level, determining an initialization state, and determining a tracking level. In a first condition, the method includes generating a first image pyramid, detecting a plurality of features in the first image pyramid using a first detector threshold, and generating a first set of detected keypoints from the plurality of features. In a second condition, the method includes generating a second image pyramid, detecting the plurality of features in the second image pyramid using a second detector threshold, the second detector threshold being less restrictive than the first detector threshold, and generating a second set of detected keypoints. In a third condition, the method includes detecting the plurality of features in the first image according to the first detector threshold and generating a third set of detected keypoint.