Methods and apparatus are disclosed for implementing data augmentation within a storage controller of a data storage device based on machine learning data read from a non-volatile memory (NVM) array of a memory die. Some particular aspects relate to configuring the storage controller to generate augmented versions of training images for use in training a Deep Learning Accelerator of an image recognition system by rotating, translating, skewing, cropping, etc., a set of initial training images obtained from a host device and stored in the NVM array. Other aspects relate to controlling components of the memory die to generate noise-augmented images by, for example, storing and then reading training images from worn regions of the NVM array to inject noise into the images. Data augmentation based on data read from multiple memory dies is also described, such as image data spread across multiple NVM arrays or multiple memory dies.

A material data collection system allows capturing of material data. For example, the material data collection system may include digital image data for materials. The material data collection system may ensure that captured digital image data is properly aligned, so that material data may be easily recalled for later use, while maintaining the proper alignment for the captured digital image. The material data collection system may include using a capture guide, to provide cues on how to orient a mobile device used with the material data collection system.

Methods, apparatus, systems, and articles of manufacture for real-time interactive anamorphosis projection via face detection and tracking are disclosed. An example system includes a sensor to capture an image of a face of a user. An augmented reality controller is to access the image from the sensor, determine a position of the face of the user relative to a display surface, and apply a perspective correction to an anamorphic camera representing a vantage point of the active user. A user application is to generate a scene based on the position of the anamorphic camera. A display is to present, at the display surface, the scene based on the vantage point of the active user.

An image processing device acquires an image to be processed that includes an image of clothing, the image being composed of a hue, saturation and brightness; calculates a difference between the image to be processed and a plurality of shift images obtained by shifting the image to be processed in units of one pixel by at least one pixel or more in a prescribed direction, and generates a plurality of difference images that correspond to the number of shifted pixels; generates a histogram of differential pixel values for each of the hue, saturation and brightness of the difference images; determines the fabric of the clothing on the basis of the features of the histogram.

An apparatus includes a resolving unit configured to resolve input image data into image data for each frequency band, an acquisition unit configured to acquire skewness corresponding to the resolved image data resolved by the resolving unit, wherein the skewness is acquired from a histogram corresponding to each of the resolved image data, and an adjustment unit configured to determine image data to be processed, out of the resolved image data based on the acquired skewness acquired and perform gain adjustment on the determined image data.

Implementations relate to dynamic adaptation of images for projection by a projector, based on one or more properties of user(s) that are in an environment with the projector. The projector can be associated with an automated assistant client of a client device. In some versions of those implementations, a pose of a user in the environment is determined and, based on the pose, a base image for projecting onto a surface is warped to generate a transformed image. The transformed image, when projected onto a surface and viewed from the pose of the user, mitigates perceived differences relative to the base image. The base image (on which the transformed image is based) can optionally be generated in dependence on a distance of the user. Some implementations additionally or alternatively relate to dynamic adaptation of projection parameters (e.g., a location for projection, a size of projection) based on one or more properties of user(s) that are in an environment with the projector.

Methods and systems for skull-based brain imaging. In some examples, a method includes receiving a first brain image of a brain taken at a first time and a second brain image of the brain taken at a second time; characterizing a structural change of brain tissue of the brain between the first time and the second time by: determining a global linear change of brain tissue using the first brain image and the second brain image; and determining a local deformation of brain tissue using the first brain image, the second brain image, and the global linear change of brain tissue; and outputting one or more parameters characterizing the structural change based on the global linear change of brain tissue and the local deformation of brain tissue.

Implementations relate to dynamic adaptation of images for projection by a projector, based on one or more properties of user(s) that are in an environment with the projector. The projector can be associated with an automated assistant client of a client device. In some versions of those implementations, a pose of a user in the environment is determined and, based on the pose, a base image for projecting onto a surface is warped to generate a transformed image. The transformed image, when projected onto a surface and viewed from the pose of the user, mitigates perceived differences relative to the base image.

A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment by following a scan pattern comprising component scan lines. The method includes obtaining, based on the sensor data, a point cloud frame. Additionally, the method includes identifying a first pixel and a second pixel that are co-located within a field of regard and overlap a point cloud object within the point cloud frame and calculating a difference between a depth associated with the first pixel and a depth associated with the second pixel. The method includes determining a relative velocity of the point cloud object by dividing the difference in depth data by a time difference between when the depth associated with the first pixel was sensed and the depth associated with the second pixel was sensed.

Methods, apparatuses and computer programs for image processing are provided. A sequence of images, in particular, is processed in this case. The images are subdivided into tiles and the tiles are transformed into the frequency domain. By evaluating the argument of the spectral density in the frequency domain, it is possible to identify and rectify disturbances which, for example, are caused by air disturbances (flickering).