Methods and devices for generating a slow motion video segment are described. A first set of video frames captures a video view at a first resolution and at a first frame rate. A second set of video frames captures the video view at a second lower resolution, and at a second frame rate that is greater for at least a portion of the second set. At least two high resolution frames are identified in the first set for generating the slow motion video segment. One or more low resolution frames are identified in the second set corresponding to an inter-frame time period between the identified high resolution frames. The slow motion video segment is generated by generating at least one high resolution frame corresponding to the inter-frame time period using interpolation based on the identified high resolution frames and the identified low resolution frames.

Methods and devices for generating a slow motion video segment are described. A first set of video frames captures a video view at a first resolution and at a first frame rate. A second set of video frames captures the video view at a second lower resolution, and at a second frame rate that is greater for at least a portion of the second set. At least two high resolution frames are identified in the first set for generating the slow motion video segment. One or more low resolution frames are identified in the second set corresponding to an inter-frame time period between the identified high resolution frames. The slow motion video segment is generated by generating at least one high resolution frame corresponding to the inter-frame time period using interpolation based on the identified high resolution frames and the identified low resolution frames.

An image sensor, electronic device and method thereof that performs on-sensor multiresolution deep neural network (DNN) processing, such as for gesture recognition. The image data is transformed into first resolution type image data and second resolution type image data. Based on detecting the first resolution type image data includes a predetermined object, processing the second resolution type image data using the second resolution type image data as input into the second DNN.

An image sensor, electronic device and method thereof that performs on-sensor multiresolution deep neural network (DNN) processing, such as for gesture recognition. The image data is transformed into first resolution type image data and second resolution type image data. Based on detecting the first resolution type image data includes a predetermined object, processing the second resolution type image data using the second resolution type image data as input into the second DNN.

An arena test system for characterizing fragments from a warhead. An entry panel and an exit panel are arranged in a wedge configuration with a wedge-shaped air space between them. Fragments are imaged as they pass through this wedge. A soft catch box is located behind the exit panel such that fragments that pass through the exit panel enter the soft catch box and are decelerated within the soft catch box.

An arena test system for characterizing fragments from a warhead. An entry panel and an exit panel are arranged in a wedge configuration with a wedge-shaped air space between them. Fragments are imaged as they pass through this wedge. A soft catch box is located behind the exit panel such that fragments that pass through the exit panel enter the soft catch box and are decelerated within the soft catch box.

In one embodiment, a method includes reconstructing a three-dimensional shape of a target object, creating a two-dimensional normal map for the three-dimensional shape of the target object, accessing image data and depth data associated with the target object, generating a first normal data associated with the target object using the image data and the depth data, updating the normal map using the first normal data, and re-rendering the three-dimensional shape of the target object based on the updated normal map.

In one embodiment, a method includes reconstructing a three-dimensional shape of a target object, creating a two-dimensional normal map for the three-dimensional shape of the target object, accessing image data and depth data associated with the target object, generating a first normal data associated with the target object using the image data and the depth data, updating the normal map using the first normal data, and re-rendering the three-dimensional shape of the target object based on the updated normal map.

An electronic endoscope system includes a plotting unit which plots pixel correspondence points, which correspond to pixels that constitute a color image that has multiple color components, on a first color plane according to color components of the pixel correspondence points, the first color plane intersecting the origin of a predetermined color space; an axis setting unit which sets a predetermined reference axis in the first color plane; a transform unit which defines a second color plane that includes the reference axis, and subjecting the pixel correspondence points on the first color plane to projective transformation onto the second color plane; and an evaluation value calculating unit which calculates a prescribed evaluation value with respect to the color image based on the pixel correspondence points subjected to projective transformation onto the second color plane.

A machine vision method includes obtaining a first representation of an image captured by an image sensor array, analyzing the first representation for an assessment of whether the first representation is sufficient to support execution of a machine vision task by the processor, if the first representation is not sufficient, determining, based on the first representation, a region of the image of interest for the execution of the machine vision task, reusing the image captured by the image sensor array to obtain a further representation of the image by directing the image sensor array to sample the image captured by the image sensor array in a manner guided by the determined region of the image of interest and by the assessment, and analyzing the further representation to assess whether the further representation is sufficient to support the execution of the machine vision task by implementing a procedure for the execution of the machine vision task in accordance with the further representation.