International Business Machines Corporation

Ophir Azulai

Udi Barzelay

Mattias Marder

Dror Porat

Yochay Tzur

Oshri Pesah Naparstek

Oded Margalit

Roy Levin

Peter Hans Westerink

Eran Raichstein

Gal Ashour

Martin G Kienzle

Joseph Shtok

Slava Shechtman

Leonid Karlinsky

Daniel Nechemia Rotman

Rami Ben-Ari

Ofer Lavi

Sivan Harary

Inbar Shapira

Alexander Khazanovitch

Gayathri Shaikh

Durga Sai Phaneendar Vemuru

Mor Shpigel Nacson

Approaches presented herein enable action recognition. More specifically, a plurality of video segments having one or more action representations is received. One or more sub-action representations in the plurality of video segments are learned. An embedding in a space of a distance metric learning (DML) network for each of the one or more sub-action representations is determined. A set of respective trajectory distances between each of the one or more sub-action representations and one or more class representatives in the space of the DML network based on the embedding is computed, and the one or more action representations based on the set of respective trajectory distances are classified.

Action recognition using limited data

An example system includes a processor to receive detected chart regions in a page of a document. The processor is to produce, via a graphical elements detector, predicted heatmaps and bounding boxes for graphical objects in the detected chart regions. The processor is also to apply chart type specific analysis algorithm to the predicted heatmaps and bounding boxes, to extract tabular chart data. The processor can then generate an output data file and a visualization based on the predicted heatmap and the extracted tabular chart data.

Detecting graphical elements in charts using predicted heatmaps

Described are techniques for font attribute detection. The techniques include receiving a document having different font attributes amongst a plurality of words respectively comprised of at least one character. The techniques further include generating a dense image document from the document by setting the plurality of words to a predefined size, removing blank spaces from the document, and altering an order of characters relative to the document. The techniques further include determining characteristics of the characters in the dense image document and aggregating the characteristics for at least one word. The techniques further include annotating the at least one word with a font attribute based on the aggregated characteristics.

Font attribute detection

A method, computer system, and a computer program product for text detection is provided. The present invention may include training a text detection model. The present invention may include performing text detection on an inputted image using the trained text detection model. The present invention may include determining whether at least one of a plurality of bounding boxes generated using the inputted image has an aspect ratio above a threshold. The present invention may include based upon determining that at least one of the plurality of bounding boxes generated using the inputted image has the aspect ratio above the threshold, upscaling any text within the at least one bounding box and performing text detection on a new image using the trained text detection model. The present invention may include outputting an output image.

Text detection algorithm for separating words detected as one text bounding box

A method for synthesizing negative training data associated with training models to detect text within documents and images. The method includes one or more computer processors receiving a set of dictates associated with generating one or more negative training datasets for training a set of models to classify a plurality of features found within a data source. The method further includes identifying a set of rules related to generating negative training data to detect text based on the received set of dictates. The method further includes compiling one or more arrays of elements of hard-negative training data into a negative training data dataset based on the identified set of rules and one or more dictates. The method further includes determining metadata corresponding an array of elements of hard-negative training data.

Synthesizing hard-negative text training data

An approach to identifying text within an image may be presented. The approach can receive an image. The approach can classify an image on a pixel-by-pixel basis whether the pixel is text. The approach can generate bounding boxes around groups of pixels that are classified as text. The approach can mask sections of an image that where pixels are not classified as text. The approach may be used as a pre-processing technique for optical character recognition in documents, scanned images, or still images.

Document segmentation for optical character recognition

In some examples, a system for detecting text in an image includes a memory device to store a text detection model trained using images of up-scaled text, and a processor configured to perform text detection on an image to generate original bounding boxes that identify potential text in the image. The processor is also configured to generate a secondary image that includes up-scaled portions of the image associated with bounding boxes below a threshold size, and perform text detection on the secondary image to generate secondary bounding boxes that identify potential text in the secondary image. The processor is also configured to compare the original bounding boxes with the secondary bounding boxes to identify original bounding boxes that are false positives, and generate an image file that includes the original bounding boxes, wherein those original bounding boxes that are identified as false positives are removed.

Techniques for detecting text

Automatic cloning of a PYTHON CONDA environment into a DOCKER image, such that at least one CONDA container that functions the same as the PYTHON CONDA environment can be started from the DOCKER image. The automatic cloning may include: First, creating a Dockerfile that comprises commands to: install a PYTHON ANACONDA environment or obtain a PYTHON ANACONDA environment image, copy the PYTHON CONDA environment into the DOCKER image, and run a CONDA command, in the ANACONDA environment, to create a cloned PYTHON CONDA environment from the copied PYTHON CONDA environment. Second, building the DOCKER image from the Dockerfile.

Automatic cloning of Conda environment into Docker image

A method of disarming and reconstructing an encoded video stream to nullify malicious agents potentially embedded in the encoded video stream comprising decoding a received encoded video stream to obtain a decoded video stream, extracting, from the encoded video stream, encoding information calculated by an originating encoder to create the encoded video stream, adjusting the encoding information by replacing one or more quantization parameters defined in the encoding information with respective adjusted quantization parameter(s) calculated based on a random selection of a value from a range of quantization parameter values, encoding the decoded video stream using the adjusted encoding information to produce a modified encoded video stream and transmitting the modified encoded video stream.

Low delay content disarm and reconstruction (CDR) of live streaming video

Technology for matching images (for example, video images, still images) of an identical infrastructure object (for example, a tower component of a tower supporting power lines) for purposes of comparing the infrastructure object to itself at different points in time to detect a potential anomaly and the potential need for maintenance of the infrastructure object. In some embodiments, this matching of images is done using creation of a three dimensional (#D) computer model of the infrastructure object and by tagging captured images with location on the 3D model across multiple videos taken at different points in time.

Identifying temporal changes of industrial objects by matching images

Technology for matching images (for example, video images, still images) of an identical infrastructure object (for example, a tower component of a tower supporting power lines) for purposes of comparing the infrastructure object to itself at different points in time to detect a potential anomaly and the potential need for maintenance of the infrastructure object. In some embodiments, this matching of images is done using creation of a three dimensional (# D) computer model of the infrastructure object and by tagging captured images with location on the 3D model across multiple videos taken at different points in time.

A computer implemented method of mapping multimedia analytics of multimedia objects into a resilient distributed dataset (RDD), comprising one or more processors adapted to obtain an RDD of a cluster computing framework executed by a cluster comprising a plurality of computing nodes, the RDD comprises a plurality of entries each comprising a pointer to one of a plurality of multimedia objects stored in a shared storage, instruct each of a plurality of framework tasks executed by at least some members of the cluster to apply a docker operator for retrieving and executing one of a plurality of multimedia containers each associated with a respective one of the multimedia objects and comprising a multimedia processing algorithm for processing the respective multimedia object, receive from the framework tasks multimedia analytics results generated simultaneously by the multimedia containers and map the multimedia analytics results into the RDD.

Multimedia analytics in spark using docker

Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data representing the signal, and accessing a database using the digital data to retrieve information identifying the object.

Real-time system for determining current video scale

A method, system, and computer program product for precise target positioning in geographical imaging are provided. The system includes: a sensor including a camera and a telemetry information provider; and a simulated image mechanism for simulating an image of the view from the camera generated from an orthophotograph. A display displays a geographical image of a view from the camera and simultaneously displaying the simulated image. A selection component is provided for selecting a location on the simulated image. This is done by reference to a target in the geographical image. A world coordinates calculating component accurately calculates the world coordinates for the selected location from the simulated image.

Precise target positioning in geographical imaging

A method comprising using at least one hardware processor for: receiving an aerially-captured video and metadata associated with the video; receiving deviation data indicative of an amount of inaccuracy in the metadata; and overlaying, on the video, a geographic annotation descriptive of an object of interest having known geographic coordinates, wherein the geographic annotation is of a size and a shape representative of the amount of inaccuracy.

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Tivon, Israel

Ophir Azulai