Abstract: Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.

Abstract: Approaches to segmentation or detection of objects and their boundaries in images (or other data sets) do not rely on machine learning approaches that aim to minimize pixel-level agreement between a computer and a human. Optimizing such pixel-level agreement does not, in general, provide the best possible result if boundary detection is a means to the ultimate goal of image segmentation, rather than an end in itself. In some examples, end-to-end learning of image segmentation specifically targets boundary errors with topological consequences, but otherwise does not require the computer to “slavishly” imitate human placement of boundaries. In some examples, this is accomplished by modifying a standard learning procedure such that human boundary tracings are allowed to change during learning, except at locations critical to preserving topology.

Abstract: Approaches to segmentation or detection of objects and their boundaries in images (or other data sets) do not rely on machine learning approaches that aim to minimize pixel-level agreement between a computer and a human. Optimizing such pixel-level agreement does not, in general, provide the best possible result if boundary detection is a means to the ultimate goal of image segmentation, rather than an end in itself. In some examples, end-to-end learning of image segmentation specifically targets boundary errors with topological consequences, but otherwise does not require the computer to “slavishly” imitate human placement of boundaries. In some examples, this is accomplished by modifying a standard learning procedure such that human boundary tracings are allowed to change during learning, except at locations critical to preserving topology.

Abstract: Identifying objects in images is a difficult problem, particularly in cases an original image is noisy or has areas narrow in color or grayscale gradient. A technique employing a convolutional network has been identified to identify objects in such images in an automated and rapid manner. One example embodiment trains a convolutional network including multiple layers of filters. The filters are trained by learning and are arranged in successive layers and produce images having at least a same resolution as an original image. The filters are trained as a function of the original image or a desired image labeling; the image labels of objects identified in the original image are reported and may be used for segmentation. The technique can be applied to images of neural circuitry or electron microscopy, for example. The same technique can also be applied to correction of photographs or videos.