Image segmentation into overlapping tiles

Abstract

Systems and techniques for image segmentation into overlapping tiles are described herein. In an example, an anomaly detection system for super-high-resolution images is adapted to divide the image into tiles with a size and overlap between each tile, wherein the size and overlap is determined using a machine-learning model. The anomaly detection system may be further adapted to use a classifier model for each tile to identify anomaly presence in the tile, wherein the classifier model is trained using a data set of tile size images that are labeled according to anomaly presence in each tile size image. The anomaly detection system may be further adapted to determine a classification for the image based on results from the classifier model for the tiles. The anomaly detection system may be further adapted to output the classification.

What is claimed is: 
     
         1 . A system for image analysis, the system comprising:
 at least one processor; and   memory storing instructions that, when executed by the at least one processor, cause the at least one processor to:
 access an image; 
 divide the image into a plurality of tiles, each tile having a tile size and overlap with at least one other tile, wherein the tile size and the overlap are determined using a machine-learning model trained using a range of tile sizes and overlaps and are based on accuracy of anomaly detection for each tile size and overlap; 
 use a classifier model for each tile in the plurality of tiles to identify anomaly presence in the tile; 
 determine a classification for the image based on results from the classifier model for the tiles in the plurality of tiles of the image; and 
 output the classification for the image. 
   
     
     
         2 . The system of  claim 1 , wherein the classifier model is trained using a data set of tile size images that are labeled according to anomaly presence in each tile size image. 
     
     
         3 . The system of  claim 2 , wherein the data set of tile size images is generated by dividing a sample set of images into tiles and labeling each tile based on anomaly presence within the tile, and wherein size and overlap of the data set of tile size images is based on results of the machine-learning model. 
     
     
         4 . The system of  claim 1 , wherein the classification for the image is output to a graphical user interface. 
     
     
         5 . The system of  claim 1 , wherein the image is classified as passing when the anomaly presence is not identified for each of the tiles of the image and wherein the image is classified as failing when the anomaly presence is identified for at least one of the tiles of the image. 
     
     
         6 . The system of  claim 1 , wherein the image is a super-high-resolution image with a resolution of at least 5000 by 5000 pixels. 
     
     
         7 . The system of  claim 1 , wherein the range of tile sizes and overlaps for the machine-learning model is based on one of random search, grid search, or Bayesian optimization. 
     
     
         8 . A non-transitory machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to:
 access an image;   divide the image into a plurality of tiles, each tile having a tile size and overlap with at least one other tile, wherein the tile size and the overlap are determined using a machine-learning model trained using a range of tile sizes and overlaps and are based on accuracy of anomaly detection for each tile size and overlap;   use a classifier model for each tile in the plurality of tiles to identify anomaly presence in the tile;   determine a classification for the image based on results from the classifier model for the tiles in the plurality of tiles of the image; and   output the classification for the image.   
     
     
         9 . The machine-readable medium of  claim 8 , wherein the classifier model is trained using a data set of tile size images that are labeled according to anomaly presence in each tile size image. 
     
     
         10 . The machine-readable medium of  claim 9 , wherein the data set of tile size images is generated by dividing a sample set of images into tiles and labeling each tile based on anomaly presence within the tile, and wherein size and overlap of the data set of tile size images is based on results of the machine-learning model. 
     
     
         11 . The machine-readable medium of  claim 8 , wherein the classification for the image is output to a graphical user interface. 
     
     
         12 . The machine-readable medium of  claim 8 , wherein the image is classified as passing when the anomaly presence is not identified for each of the tiles of the image and wherein the image is classified as failing when the anomaly presence is identified for at least one of the tiles of the image. 
     
     
         13 . The machine-readable medium of  claim 8 , wherein the image is a super-high-resolution image with a resolution of at least 5000 by 5000 pixels. 
     
     
         14 . The machine-readable medium of  claim 8 , wherein the range of tile sizes and overlaps for the machine-learning model is based on one of random search, grid search, or Bayesian optimization. 
     
     
         15 . A method comprising:
 accessing an image;   dividing the image into a plurality of tiles, each tile having a tile size and overlap with at least one other tile, wherein the tile size and the overlap are determined using a machine-learning model trained using a range of tile sizes and overlaps and are based on accuracy of anomaly detection for each tile size and overlap;   using a classifier model for each tile in at least a subset of the plurality of tiles to identify anomaly presence in the tile;   determining a classification for the image based on results from the classifier model for the tiles in the at least the subset of plurality of tiles of the image; and   outputting the classification for the image.   
     
     
         16 . The method of  claim 15 , wherein the classifier model is trained using a data set of tile size images that are labeled according to anomaly presence in each tile size image. 
     
     
         17 . The method of  claim 16 , wherein the data set of tile size images is generated by dividing a sample set of images into tiles and labeling each tile based on anomaly presence within the tile, and wherein size and overlap of the data set of tile size images is based on results of the machine-learning model. 
     
     
         18 . The method of  claim 15 , wherein the classification for the image is output to a graphical user interface. 
     
     
         19 . The method of  claim 15 , wherein the at least the subset of the plurality of tiles comprises each and every one of the plurality of tiles of the image. 
     
     
         20 . The method of  claim 19 , wherein the image is classified as passing when the anomaly presence is not identified for each of the tiles of the image and wherein the image is classified as failing when the anomaly presence is identified for at least one of the tiles of the image.