International Business Machines Corporation

Florian Michael Scheidegger

Adelmo Cristiano Innocenza Malossi

Konstantinos Bekas

Christoph Hermann Hagleitner

Dionysios Diamantopoulos

Giovanni Mariani

Roxana Istrate

Dhruti Nilesh Shah

A computer-implemented method is disclosed for image stitching for a plurality of digital images of an infrastructure surface for defect detection. The method includes providing a plurality of partial digital images of an infrastructure surface. The method includes extracting global positioning system meta-data from data corresponding to the partial digital images. The method includes determining feature descriptions of features in the one or more partial digital images. The method includes executing a scheduled processing sequence for the partial digital images based on the extracted global positioning system meta-data including determining an affinity matrix using the feature descriptions of adjacent partial digital images to incrementally position each of the partial digital images such that an overview image of the infrastructure surface is produced by iteratively digitally stitching the plurality of partial digital images together.

Image stitching for high-resolution scans

Aspects of the present invention disclose a method, computer program product, and system for improving object detection in high-resolution images at inference time. The method includes one or more processors receiving a high-resolution image. The method further includes one or more processors decomposing the received image into hierarchically organized layers of images. Each layer comprises at least one image tile of the received image. Each of the image tiles have a corresponding resolution suitable to a baseline image recognition algorithm. The method further includes one or more processors applying the baseline algorithm to each of the image tiles of each layer. The method further includes one or more processors performing a result aggregation of results of the baseline algorithm applications to the image tiles of the layers.

Hierarchical image decomposition for defect detection

Determining cognitive models to be deployed at auxiliary devices may include maintaining relations, e.g., in a database. The relations map hardware characteristics of auxiliary devices and example datasets to cognitive models. Cognitive models are determined for auxiliary devices, based on said relations, e.g., for each of the auxiliary devices. An input dataset is accessed, which comprises data of interest, e.g., collected at a core computing system (CCS), and hardware characteristics of each of the auxiliary devices. An auxiliary cognitive model is determined based on a core cognitive model run on the input dataset accessed, wherein the core cognitive model has been trained to learn at least part of said relations. Parameters of the auxiliary model determined can be communicated to said each of the auxiliary devices for the latter to deploy the auxiliary model determined. Method may be implemented in a network having an edge computing architecture.

Automatic determination of cognitive models for deployment at computerized devices having various hardware constraints

The present disclosure relates to a hardware accelerator for executing a computation task composed of a set of operations. The hardware accelerator comprises a controller and a set of computation units. Each computation unit of the set of computation units is configured to receive input data of an operation of the set of operations and to perform the operation, wherein the input data is represented with a distinct bit length associated with each computation unit. The controller is configured to receive the input data represented with a certain bit length of the bit lengths and to select one of the set of computation units that can deliver a valid result and that is associated with a bit length smaller than or equal to the certain bit length.

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Effretikon, Switzerland

Florian Michael Scheidegger