A visual inspection device and apparatus is disclosed for inspecting fiber ends of a connector by capturing an image of the connector end face, and implementing an image analysis tool for detecting contamination from the captured image. The visual inspection tool includes components for providing a larger field of view to capture the entire connector end face in a single image, and the image analysis tool is able to accurately and efficiently detect contamination from the captured image.

In variants, the method can include: detecting a property within a set of measurements; determining a set of property parameters based on the detection; determining a set of higher-resolution measurements based on the set of property parameters; and determining a set of property attributes based on the set of higher-resolution measurements.

An image detection obtains original image. The original image is corrected to obtain a corrected image. Median filtering is performed on the corrected image to obtain a filtered image. A contrast of the filtered image is adjusted to obtain an adjusted image. Bilateral filtering is performed on the adjusted image to obtain an enhanced image. Defects in the enhanced image is detected. The method can detect defects in images accurately and efficiently.

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information.

In one embodiment, a computing system may determine a target grayscale value associated with a target image to be represented by a plurality of subframes. The system may determine grayscale ranges based on the target grayscale value. Each grayscale range may correspond to a combination of zero or more subframes of the plurality of subframes. The system may select dot subsets from a dithering mask based on the grayscale ranges. Each of the dot subsets may correspond to a grayscale range. The system may generate the subframes based on (1) the selected dot subsets and (2) respective combinations of zero or more subframes. The subframes may have a smaller number of bits per color than the target frame. The system may display the subframes sequentially in time domain on a display to represent the target image.

A method of populating a digital environment with digital content is disclosed. Environment data describing the digital environment is accessed. Populator data describing a populator digital object is accessed. The populator data includes semantic data describing the populator digital object. The populator digital object is placed within the digital environment. A semantic map representation of the populator digital object is generated. The semantic map representation is divided into a plurality of cells. A target cell of the plurality of cells is selected as a placeholder in the digital environment for a digital object that is optionally subsequently instantiated. The selecting of the target cell is based on an analysis of the environment data, the populator data, and the semantic map representation. Placeholder data is recorded in the semantic map representation. The placeholder data includes properties corresponding to the digital object that is optionally subsequently instantiated.

Aspects described herein provide a computer-implemented method and system for generating topographic map data at different scales. More specifically, the topographic map data is reduced from large scale to small scale, wherein the scale of the vector features is reduced according to their geometry, feature type and attributes. In order to quickly and easily output digital maps at different scales so that a user may quickly zoom in and out of a map, different zoom levels are produced for different scales, each zoom level containing a variable amount of detail according to its scale, with larger scale zoom levels generally containing more detail than small scale zoom levels. Each zoom level is made up of one or more vector tiles representative of a geographic area, wherein the vector tiles may contain one or more vector features that are representative of objects within that geographic area, and other information related to that geographical area.

Systems, methods, and non-transitory computer-readable media can perform operations comprising determining visual geometry detections (e.g., lane line detections) associated with geometry corresponding with a map; aligning the visual geometry detections with the geometry based on transformations associated with selected degrees of freedom; and determining a pose of a vehicle based on alignment of the visual geometry detections with the geometry.

A computer-implemented method, computer program product and computer system (100) for determining the impact of herbicides on crop plants (11) in an agricultural field (10). The system includes an interface (110) to receive an image (20) with at least one crop plant representing a real world situation in the agricultural field (10) after herbicide application. An image pre-processing module (120) rescales the received image (20) to a rescaled image (20a) matching the size of an input layer of a first fully convolutional neural network (CNN1) referred to as the first CNN. The first CNN is trained to segment the rescaled image (20a) into crop (11) and non-crop (12, 13) portions, and provides a first segmented output (20s1) indicating the crop portions (20c) of the rescaled image with pixels belonging to representations of crop. A second fully convolutional neural network (CNN2), referred to as the second CNN, is trained to segment said crop portions into a second segmented output (20s2) with one or more sub-portions (20n, 20l) with each sub-portion including pixels associated with damaged parts of the crop plant showing a respective damage type (11-1, 11-2). A damage measurement module (130) determines a damage measure (131) for the at least one crop plant for each damage type (11-1, 11-2) based on the respective sub-portions of the second segmented output (20s2) in relation to the crop portion of the first segmented output (20s1).

A method for segmenting images is provided including tessellating an image obtained from one of an image database and an imaging system into a plurality of sectors; classifying each of the plurality of sectors by applying one or more pre-defined labels to each of the plurality of sectors, wherein the pre-defined labels indicate at least one of an image quality metric (IQM) and a metric of structure; assigning each of the plurality of classified sectors an Image Quality Classification (IQC); identifying anchor sectors among the plurality of classified sectors, applying filtering and edge detection to identify target boundaries; applying contouring across contiguous sectors and using the assigned IQC as a guide to complete segmentation of an edge between any two identified anchor sectors; and smoothing across segmented regions to increase parametric second-order continuity.