Abstract: A method, apparatus, and computer program product are disclosed to estimate the accuracy of feature prediction in an image. Methods may include: receiving a binary ground truth map of pixels and a binary prediction map of pixels, where pixels corresponding to features of an environment are assigned a 1 or a 0, and pixels not corresponding to features of the environment are assigned the other of a 1 or a 0; computing a distance transform at each pixel of the binary ground truth map representing a distance to a closest pixel representing a feature; computing a distance transform at each pixel of the prediction map representing a distance to a closest pixel corresponding to a feature; and establishing accuracy of the prediction map of pixels using the error map of the distance transform for each pixel.

Abstract: An approach is provided for parametric representation of signs. The approach involves receiving a request to detect and encode signs depicted in an input image into a parametric representation. The approach also involves assigning processing nodes of a computer vision system to independently process each grid cell of the input image to detect at least one edge of a sign. The processing nodes are assigned based on proximity to each grid cell. Each respective grid cell is created by overlaying a grid onto the input image. The approach further involves encoding, by the processing nodes, an angle and a location of a detected edge as edge parameters of a cell-based parametric representation for each grid cell. The approach further involves aggregating the cell-based parametric representation for each respective grid cell in which at least one edge is detected to output the parametric representation of the at least one sign.

Abstract: An approach is provided for parametric representation of lane lines. The approach involves segmenting an input image into grid cells. The approach also involves processing a portion of the input image in each grid cell to detect lane lines. The approach further involves, for each grid cell in which lane lines are detected, determining intercepts of the lane lines with edges of the grid cell, and slopes of the lane lines at the intercepts. The approach further involves generating a parametric representation of the lane lines for each grid cell. The parametric representation encodes the intercepts and slopes into a data structure for each grid cell. The approach further involves providing an output parametric representation for the input image that aggregates the parametric representations of each grid cell.

Abstract: The position and/or pose of a vehicle is determined in real time. An observed position and an observed pose of a vehicle are determined. A reference image is generated based on the observed position and the observed pose. The reference image comprises one or more reference static features. A captured image and the reference image are implicitly compared. Based on a result of the comparison, a correction to the observed position, the observed pose, or both is determined.

Abstract: A method is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

Abstract: An approach is provided for determining a polygon of a geographic database that overlaps a candidate polygon or candidate point. The geographic database represents stored polygons as respective polygon points with zero area. The approach involves determining proximate polygon points from among the respective polygon points with zero area that are within a distance threshold of the candidate polygon or the candidate point. The approach also involves retrieving one or more proximate polygons from the geographic database that correspond to the one or more proximate polygon points. The approach further involves determining an intersection between the one or more proximate polygons and the candidate polygon or the candidate point. The approach then involves selecting the polygon that overlaps the candidate polygon or the candidate point based on the determined intersection.

Abstract: A first image and a second image are provided to a trained neural network. The first image comprises one or more static features and the second image comprises at least one of the one or more static features. A static feature is identified in both the first and second images by a branch of the trained neural network. A three dimensional image comprising the identified static feature is generated and three dimensional geometric information/data related to the static feature is extracted and stored in association with a tile of a digital map. A set of training images may be used to train the trained neural network comprises training image subsets comprising two or more images that substantially overlap that were (a) captured at different times; (b) captured under different (i) weather conditions, (ii) lighting conditions, or (iii) weather and lighting conditions; or both a and b.

Abstract: A method is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

Abstract: An approach is provided for automated classification of an image based on the fogging attributes associated with the image. The approach involves processing and/or facilitating a processing of image data associated with at least one image to cause, at least in part, an extraction of one or more fogging attributes from the image data. The approach also involves causing, at least in part, a mapping of the one or more fogging attributes to one or more features of at least one classifier, wherein the at least one classifier is trained based, at least in part, on a co-registration of the one or more features to one or more records of previously made image judgements. The approach further involves causing, at least in part, a classification of the at least one image as either in a fogging-afflicted state or a non-fogging-afflicted state based, at least in part, on the at least one classifier.

Abstract: A method is provided for generating and revising map geometry based on a received image and probe data. A method may include: receiving probe data from a first period of time, where the probe data from a first period of time is from a plurality of probes within a predefined geographic region; generating a first image of the predefined geographic region based on the probe data from the first period of time; receiving probe data from a second period of time different from the first period of time, where the probe data from the second period of time is from a plurality of probes within the predefined geographic region; generating a second image based on the probe data from the second period of time; comparing the first image to the second image; and generating a revised route geometry based on changes detected between the first image and the second image.

Abstract: An approach is provided for automated classification of an image based on the fogging attributes associated with the image. The approach involves processing and/or facilitating a processing of image data associated with at least one image to cause, at least in part, an extraction of one or more fogging attributes from the image data. The approach also involves causing, at least in part, a mapping of the one or more fogging attributes to one or more features of at least one classifier, wherein the at least one classifier is trained based, at least in part, on a co-registration of the one or more features to one or more records of previously made image judgements. The approach further involves causing, at least in part, a classification of the at least one image as either in a fogging-afflicted state or a non-fogging-afflicted state based, at least in part, on the at least one classifier.

Abstract: An approach is provided for automated analysis and classification of quality characteristics associated with captured imagery that may be used in an application such as a map application. The approach includes determining digital data associated with a region of interest in an image. The approach also comprises processing and/or facilitating a processing of the digital data to determine one or more quality attributes associated with the region of interest. The approach further comprises causing, at least in part, a comparison of the one or more quality attributes to one or more criteria. The approach also comprises causing, at least in part, a generation of one or more classifications for the image based, at least in part, on the comparison.

Abstract: In accordance with an example embodiment of the present invention, a method is disclosed. An image is provided. The image is transformed. A similarity score is determined. The similarity score corresponds to a comparison of the image and the transformed image. A camera setting is predicted based on the determined similarity score.

Abstract: Cluster centers of an image are initialized. The image includes a plurality of pixels. Pixels of the plurality of pixels in a pattern are labeled. The cluster centers are recomputed based on the labeling of the plurality of pixels.

Abstract: In accordance with an example embodiment of the present invention, a method is disclosed. An image is provided. The image is transformed. A similarity score is determined. The similarity score corresponds to a comparison of the image and the transformed image. A camera setting is predicted based on the determined similarity score.

Abstract: In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of an image, and partitioning the image into a plurality of sub-images. The method further includes super-pixels in the plurality of sub-images, wherein determining the super-pixels includes determining a plurality of super-pixels in each sub-image of the plurality of sub-images. An image graph including a plurality of connections between the super-pixels is determined, wherein a connection of the plurality of connections is determined between a first super-pixel and a second super-pixel. The first super-pixel belongs to a sub-image and the second super-pixel belongs to the sub-image or a neighboring sub-image of the sub-image located in an immediate vicinity of the sub-image in the image.

Abstract: Disclosed herein is a method. Cluster centers of an image are initialized. The image includes a plurality of pixels. Pixels of the plurality of pixels in a pattern are labeled. The cluster centers are recomputed based on the labeling of the plurality of pixels.