A LED controller includes a power distribution module and an interface to an external data bus. An image frame buffer is connected to the interface to receive image data. A separate logic module is connected to the interface and configured to modify image frame buffer output signals sent to an LED pixel array connected to the image frame buffer. The LED pixel array can project light according to a pattern and intensity defined at least in part by the image held in the image frame buffer.

A software development infrastructure can enable user developers to select remote hardware devices to develop and test their software programs. For some tests and development activity, physical access to the remote device can be provided by the infrastructure administrator by replacing the internal calls of an application with mimic code, which can respond to application requests for sensor data, as if the user was physically handling the remote device at the same location.

The present disclosure relates to a system and a method for extracting a computer readable code from a captured image of a mailpiece or parcel using downsampling and edge detection. The system can include a reader configured to capture an image of an item having a computer readable code positioned thereon and a processor in data communication with the reader. The processor can generate captured image data of the item including the computer readable code, downconvert the captured image data to generate a downconverted image data and detect an edge of the computer readable code. The processor can also identify a position of the computer readable code in the downconverted image data and store or process only the identified computer readable code.

In the described examples, a compiled image scaler includes a set of machine executable instructions that generate a scaled image that is a scaled version of a source image with integer and bitwise operations. The compiled image scaler employs filtering to blend colors of adjacent pixels in the source image to generate the scaled image, and each filtering operation concurrently scales three color channels of a pixel in the source image.

An image data frame is received from an external source. An error concealment operation is performed on the received image data frame in response to determining that a first frame size of the received image data frame is erroneous. The first frame size of the image data frame is determined to be erroneous based on at least one frame synchronization signal associated with the image data frame. An image processing operation is performed on the received image data frame on which the error concealment operation has been performed, thereby enabling an image processing module to perform the image processing operation without entering into a deadlock state and thereby prevent a host processor from having to execute hardware resets of deadlocked modules.

A blind spot visualization system and method for eliminating blind spots for an operator of a vehicle. The blind spots are caused by obstructions in the vehicle. A first image based on a first frequency range, and a second image based on a second frequency range, is generated. The images of the first frequency range and the images of the second frequency range are combined to create a composite image. Displays on the obstruction, facing the operator, receive the composite image. The displays display the images to the operator so that the blind spots, caused by the obstructions in the vehicle, are eliminated.

Systems for image evaluation and dynamic cropping are provided. In some examples, a system, may receive an instrument or image of an instrument. Identifying information may be extracted from the instrument or image of the instrument. Based on the extracted identifying information, a check/check image profile may be retrieved. In some examples, expected size and/or shape data may be extracted from the check/check image profile. The extracted expected size and/or shape data may be compared to size and/or shape data from the received instrument or image of the instrument to identify any anomalies (e.g., to determine whether the expected size and/or shape data matches the size and/or shape data of the received instrument or image of the instrument. If the expected size and/or shape data does not match size and/or shape data from the received instrument or image of the instrument, the instrument or image of the instrument may be programmatically modified and a modified image of the instrument may be generated.

Embodiments are disclosed for generating 360-degree panoramas from input narrow field of view images. A method of generating 360-degree panoramas may include obtaining an input image and guide, generating a panoramic projection of the input image, and generating, by a panorama generator, a 360-degree panorama based on the panoramic projection and the guide, wherein the panorama generator is a guided co-modulation generator network trained to generate a 360-degree panorama from the input image based on the guide.

The present disclosure relates to a system and a method for extracting a computer readable code from a captured image of a mailpiece or parcel using downsampling and edge detection. The system can include a reader configured to capture an image of an item having a computer readable code positioned thereon and a processor in data communication with the reader. The processor can generate captured image data of the item including the computer readable code, downconvert the captured image data to generate a downconverted image data and detect an edge of the computer readable code. The processor can also identify a position of the computer readable code in the downconverted image data and store or process only the identified computer readable code.

A method includes receiving an input video stream and providing, to a convolutional neural network (CNN), multiple image frames of the video stream including a target pair of consecutive frames, a frame immediately preceding the target pair, and a frame immediately following the target pair. The method includes generating, by the CNN, multiple interpolated image frames by performing 3D space-time convolution on the multiple image frames and outputting a video stream in which the interpolated image frames are inserted between the frames of the target pair. The convolution may include passing a 3D filter over the multiple image frames in common width and height dimensions, and in a depth dimension representing the number of frames. Generating the interpolated image frames may include generating image data for multiple color channels in respective convolutional layers. The CNN may be trained to predict non-linear movements that occur over multiple image frames.