Abstract: Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Abstract: Systems (100) and methods (300) for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Abstract: Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Abstract: Systems (100) and methods (300, 400) for efficient comparative non-spatial image data analysis. The methods involve ranking a plurality of non-spatial images (1011, 1050, 1231, 1539, 0001, 0102, 0900, 1678, 0500, 0020, 0992, 1033, 1775, 1829) based on at least one first attribute thereof; generating a screen page (1102-1106) comprising an array (1206) defined by a plurality of cells (1208) in which at least a portion of the non-spatial images are simultaneously presented; and displaying the screen page in a first GUI window (802) of a display screen. Each cell comprises only one non-spatial image. The non-spatial images are presented in an order defined by the ranking thereof.

Abstract: Systems (100) and methods (300) for efficiently and accurately detecting changes in feature data. The methods generally involve: determining first vectors for first features extracted from a first image using pixel information associated therewith; comparing the first vectors with second vectors defined by spatial feature data; classifying the first features into a plurality of classes based on the results of the vector comparisons; and analyzing the first image to determine if any one of the first features of at least one of the plurality of classes indicates that a relevant change has occurred in relation to an object represented thereby.

Abstract: Systems (100) and methods (300) for efficient feature data analysis. The methods involve: determining a first number of screen pages needed to verify that each of a plurality of clusters of detected features comprises only detected features which were correctly identified during feature extraction/detection operations as being of the same feature class as a selected feature of an image; determining a second number of screen pages needed to verify that each of a plurality of singular detected features was correctly identified during the feature extraction/detection operations as being of the same feature class as the selected feature of the image; selecting one of a plurality of different validation processes based on values of the first number of screen pages and the second number of screen pages; and performing the selected validation process to verify that each of the detected features does not constitute a false positive.

Abstract: Systems (100) and methods (300) for automatically generating a quality metric for a specified surface area of a terrain (104). The methods involve acquiring mobile LIDAR data defining a geometry of the specified surface area of the terrain. The mobile LIDAR data is acquired by LIDAR equipment (106) disposed on a vehicle (102) traveling along the terrain. The methods also involve automatically determining a quality metric defining a quality of the specified surface area of the terrain using the mobile LIDAR data.

Abstract: A method may be for operating a fingerprint matcher receiving reference fingerprint data. The fingerprint matcher may include a memory and a controller cooperating therewith. The method may include determining ridge flow direction magnitude values for each block of input fingerprint data using the memory and controller, and iteratively identifying blocks of the input fingerprint data in which the respective ridge flow direction magnitude values exceed an iteratively decremented threshold until reaching a stopping point thereby defining a final set of identified blocks of the input fingerprint data using the memory and controller. The method may also include determining a match between the reference fingerprint data and the final set of identified blocks of the input fingerprint data using the memory and controller.

Abstract: A method and system for synthesizing multiple fingerprint images into a single synthesized fingerprint template. Sets of features are extracted from each of three or more fingerprint images. Pair-wise comparisons identifying correspondences between sets of features are performed between each set of features and every other set of features. Transformations (translation and rotation) for each set of features are simultaneously calculated based on the pair-wise correspondences, and each set of features is transformed accordingly. A synthesized fingerprint template is generated by simultaneously registering the transformed sets of features.

Abstract: A self-cleaning oven includes an oven cavity, a gas sensor in flow communication with the oven cavity, and a controller configured to select one of a plurality of self-clean cycle times based upon a peak value of sampled signals of the gas sensor.

Abstract: A self-cleaning oven includes an oven cavity, a gas sensor in flow communication with the oven cavity, and a controller configured to select one of a plurality of self-clean cycle times based upon a peak value of sampled signals of the gas sensor.