Abstract: An approach is provided for estimating a vanishing point or horizon in an image depicting one or more lanes of a roadway. The approach involves processing the image to construct one or more lane models of the one or more road lanes depicted in the image. The approach also involves extending the one or more road lanes through the image using the one or more lane models. The approach further involves determining a horizontal line in the image at which a maximum number of the one or more extended road lanes crosses over a minimum horizontal extent of the horizontal line. The approach further involves designating the horizontal line as the horizon of the image.

Abstract: An approach is provided for using one or more skip areas to label, train, and/or evaluate a machine learning model. The approach, for example, involves specifying the one or more skip areas with respect to an image. By way of example, a non-skip area of the image is a portion of the image that is not in the one or more skip areas. The approach also involves initiating a labeling of one or more features in the non-skip area of the image while excluding the one or more skip areas from the labeling to create a partially labeled image. The partially labeled image is then included in a training dataset for training a machine learning model.

Abstract: An approach is provided for a location-aware evaluation of a machine learning model. The approach, for example, involves designating a geographic area for creating an evaluation dataset for the machine learning model. The approach also involves separating a plurality of observation data records into the evaluation dataset and a training dataset based on a comparison of a respective data collection location of each of the plurality of observation data records to the geographic area. The training dataset is then used to train the machine learning model, and the evaluation dataset is used to evaluate the trained machine learning model.

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 constructing polygons for object detection. The approach involves processing, by a computer vision system, an image to generate a cell-based parametric representation of object edges. The representation, for instance, segments the image into cells with each cell including a predicted line segment representing a portion of the object edges, and a predicted centroid of the object. The approach also involves grouping the cells into cell groups based on the predicted line segment for each cell. The approach further involves generating a line to represent each cell group based on the predicted line segment for each cell of each cell group. The approach further involves constructing the polygon to represent the corresponding object based on a half planes coincident with the predicted centroid for at least one cell. Each half plane is created by bisecting a plane with the line generated for each cell group.

Abstract: An approach is provided for estimating a quality of lane features of a roadway. The approach involves processing, by a computer vision system, an input image to detect the lane features of the roadway. The approach also involves determining respective confidence values associated with a plurality of regions of the input image used to detect the lane features. The respective confidence values represent a probability of predicting the lane features from each of the plurality of regions. The approach further involves performing a classification of the plurality of regions into a plurality of confidence levels based on the respective confidence values. The approach further involves determining the estimated quality of the lane features based on the classification of the plurality of regions.

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: 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: An approach is provided for quantifying a diversity of a machine learning training data set. The approach involves creating a matrix data structure storing a plurality of feature data records describing the observations in the training data set. The approach also involves computing a covariance of the matrix data structure. For example, in one embodiment, the covariance is based on a stable rank of a covariance matrix. The approach further involves determining the diversity value of the observations based on the computed covariance.

Abstract: An approach is provided for selecting training observations for machine learning models. The approach involves determining a first distribution of a plurality of features observed in the training data set, and a second distribution of the plurality of features observed in the candidate pool of observations. The approach further involves selecting one or more observations in the candidate pool of observations for annotation based on the first distribution and the second distribution. The approach further involves adding the one or more observations to the training data set after annotation. The training data set is used for training the machine learning model.

Abstract: An approach is provided for providing quality assurance for training a feature prediction model. The approach involves training the feature prediction model to label one or more features by using a training data set comprising a plurality of data items with manually marked feature labels. The approach also involves processing the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of data items. The approach further involves computing precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of data items in the training data set. The approach further involves initiating a quality assurance procedure on said each of the plurality of data items based on a determination that the precision data does not satisfy a quality assurance criterion.

Abstract: An approach is provided for constructing polygons for object detection. The approach involves processing, by a computer vision system, an image to generate a cell-based parametric representation of object edges. The representation, for instance, segments the image into cells with each cell including a predicted line segment representing a portion of the object edges, and a predicted centroid of the object. The approach also involves grouping the cells into cell groups based on the predicted line segment for each cell. The approach further involves generating a line to represent each cell group based on the predicted line segment for each cell of each cell group. The approach further involves constructing the polygon to represent the corresponding object based on a half planes coincident with the predicted centroid for at least one cell. Each half plane is created by bisecting a plane with the line generated for each cell group.

Abstract: An approach is provided for a vertex-based evaluation of polygon similarity. The approach, for instance, involves processing, by a computer vision system, an image to generate a first set of vertices of a first polygon representing an object depicted in the image. The approach also involves for each vertex in the first set of vertices, determining a closest vertex in a second set of vertices of a second polygon, and determining a distance between said each vertex in the first set of vertices and the closest vertex in the second set of vertices. The approach further involves calculating a polygon similarity of the first polygon with respect to the second polygon based on a total of the distance determined for said each vertex in the first set of vertices normalized to a number of vertices in the first set of vertices.

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: 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 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; determining at least one feature within each of the ground truth map and the prediction map based on pixels including the at least one feature; computing the overlap of pixels of the at least one feature from the ground truth map with the pixels of the at least one feature from the prediction map; matching the at least one feature from the ground truth map with the at least one feature from the prediction map in response to the overlap of pixels satisfying a predetermined value; and establishing a precision of the prediction map based on the overlap of pixels.

Abstract: An approach is provided for an asymmetric evaluation of polygon similarity. The approach, for instance, involves receiving a first polygon representing an object depicted in an image. The approach also involves generating a transformation of the image comprising image elements whose values are based on a respective distance that each image element is from a nearest image element located on a first boundary of the first polygon. The approach further involves determining a subset of the plurality of image elements of the transformation that intersect with a second boundary of a second polygon. The approach further involves calculating a polygon similarity of the second polygon with respect the first polygon based on the values of the subset of image elements normalized to a length of the second boundary of the second polygon.

Abstract: An approach is provided for estimating a vanishing point or horizon in an image depicting one or more lanes of a roadway. The approach involves processing the image to construct one or more lane models of the one or more road lanes depicted in the image. The approach also involves extending the one or more road lanes through the image using the one or more lane models. The approach further involves determining a horizontal line in the image at which a maximum number of the one or more extended road lanes crosses over a minimum horizontal extent of the horizontal line. The approach further involves designating the horizontal line as the horizon of the image.

Abstract: An approach is provided for object detection. The approach involves receiving a feature map encoding high level features of object contours detected in an image divided into a plurality of grid cells, and further encoding start locations of each detected object contour. The approach also involves selecting a grid cell including a start location of an object contour. The approach further involves determining a precise location of the start location within the grid cell. The approach further involves determining a set of feature values from a set of proximate grid cells. The approach further involves processing the precise location and the set of feature values using a machine learning network to output a displacement vector to indicate a next coordinate of the object contour, and updating a cursor of the machine learning network based on the displacement vector.

Abstract: An approach is provided for estimating a quality of lane features of a roadway. The approach involves processing, by a computer vision system, an input image to detect the lane features of the roadway. The approach also involves determining respective confidence values associated with a plurality of regions of the input image used to detect the lane features. The respective confidence values represent a probability of predicting the lane features from each of the plurality of regions. The approach further involves performing a classification of the plurality of regions into a plurality of confidence levels based on the respective confidence values. The approach further involves determining the estimated quality of the lane features based on the classification of the plurality of regions.

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 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; determining at least one feature within each of the ground truth map and the prediction map based on pixels including the at least one feature; computing the overlap of pixels of the at least one feature from the ground truth map with the pixels of the at least one feature from the prediction map; matching the at least one feature from the ground truth map with the at least one feature from the prediction map in response to the overlap of pixels satisfying a predetermined value; and establishing a precision of the prediction map based on the overlap of pixels.