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: 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 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 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: 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 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 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: 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 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: 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: Derivatives of 6,7-dihydro-5H-imidazo[1,2-a]imidazole-3-carboxylic acid amide exhibit good inhibitory effect upon the interaction of CAMs and Leukointegrins and are thus useful in the treatment of inflammatory disease.

Abstract: Derivatives of 6,7-dihydro-5H-imidazo[1,2-?]imidazole-3-carboxylic acid amide exhibit good inhibitory effect upon the interaction of CAMs and Leukointegrins and are thus useful in the treatment of inflammatory disease.

Abstract: Derivatives of 6,7-dihydro-5H-imidazo[1,2-a]imidazole-3-carboxylic acid amide exhibit good inhibitory effect upon the interaction of CAMs and Leukointegrins and are thus useful in the treatment of inflammatory disease.

Abstract: Derivatives of 6,7-dihydro-5H-imidazo[1,2-?]imidazole-3-carboxylic acid amide exhibit good inhibitory effect upon the interaction of CAMs and Leukointegrins and are thus useful in the treatment of inflammatory disease.