Synchronization of a frame rate to a detected cadence includes receiving a sequence of image frames. Motion data recorded contemporaneously with a capture of the sequence of image frames are also received. At least some of the motion data are converted from time domain data to frequency domain data. A dominant frequency in the frequency domain data is determined. Frames from the sequence of image frames are sampled at a sampling frequency related to the dominant frequency. A new image sequence is created using the sampled frames.

Devices, systems and methods are described. A method includes driving a first light source to generate a visible light pulse having a first intensity for a first duration to illuminate a scene. An image sensor is used to record a visible-spectrum image of the scene illuminated by the first light pulse. A second light source is driven to generate a second light pulse having a second intensity that exceeds the first intensity by at least 100% for a second duration that is at most 10% of the first duration to illuminate the scene during the first duration. An image sensor is used to record a monochrome image of the scene illuminated by the second light pulse. Image processing is performed on a visible-spectrum image and the monochrome image to obtain the digital image of the scene.

Searching for documents includes retrieving objects from a physical media image using a camera from a smartphone, a user selecting a subset of the objects, forming a search query based on the subset of objects, and applying the search query to a search engine to search for the documents. Retrieving objects from a media image may include waiting for a view of the camera to stabilize. Waiting for the view of the camera to stabilize may include detecting changing content of a video flow provided to the camera and/or using motion sensors of the camera to detect movement. Retrieving objects may include the smartphone identifying possible subsets of objects in the media image. The user selecting a subset of the objects may include the smartphone presenting at least some of the possible subsets to the user and the user selecting one of the possible subsets.

A method and an apparatus for image processing are provided. An original image is captured. At least one reference image is generated by adjusting brightness of the original image. Multiple denoised images are generated by performing artificial intelligence based denoising on the original image and the at least one reference image respectively. A target image is generated by performing HDR synthesis on the multiple denoised images.

The present disclosure relates to an image processing device and method, and an image processing system that can suppress an increase in load due to image stabilization. A readout area, which is an area from which an image is read out, in an imaging area formed by a plurality of pixels is set on the basis of motion prediction information for a predetermined frame after a frame to be processed, and a cutout area to cut out a partial image from a readout image read out from the set readout area of the frame to be processed is set. The present disclosure can be applied to, for example, an image processing device, an imaging device, or an image processing system, or the like.

An untethered apparatus for performing inside-out device tracking based on visual-inertial simultaneous location and mapping (SLAM) includes a dynamic vision sensor (DVS) configured to output an asynchronous stream of sensor event data, an inertial measurement unit (IMU) sensor configured to collect IMU data associated with motion of the apparatus at a predetermined interval, a processor and a memory. The memory contains instructions, which when executed by the processor, cause the apparatus to accumulate DVS sensor output over a sliding time window, the sliding time window including the predetermined interval, apply a motion correction to the accumulated DVS sensor output, the motion correction based on the IMU data collected over the predetermined interval, generate an event-frame histogram of DVS sensor events based on the motion correction, and provide the event-frame histogram of the DVS sensor events and the IMU data to a visual inertial SLAM pipeline.

The disclosure discloses a method and a system for generating image adversarial examples based on an acoustic wave. The method includes: acquiring an image containing a target object or a target scene; generating simulated image examples for the acquired image, wherein the simulated image examples have adversarial effects on a deep learning algorithm in a target machine vision system; optimizing the generated simulated image examples to obtain an optimal adversarial example and corresponding adversarial parameters; and injecting the adversarial parameters into an inertial sensor of the target machine vision system in a manner of an acoustic wave, such that the adversarial parameters are used as sensor readings that will cause an image stabilization module in the target machine vision system to operate to generate particular blurry patterns in a generated real-world image so as to generate an image adversarial example in a physical world.

There is provided an information processing apparatus. A detecting unit detects a first motion amount of an object from an image obtained through first shooting carried out repeatedly at predetermined intervals of time. A converting unit converts the first motion amount into a first motion blur amount that will arise in second shooting, on the basis of the predetermined intervals of time and a shutter speed used in the second shooting. A determining unit determines a first shutter speed adjustment step size on the basis of a difference between the first motion blur amount and a first target motion blur amount that is lower than the first motion blur amount. A changing unit changes the shutter speed of the second shooting at the first shutter speed adjustment step size in response to a first shutter speed changing unit being operated.

Systems and methods of correcting for signal drift in optical image stabilization of a portable device. An accelerometer is used to obtain an acceleration signal corresponding to acceleration of the portable device. A gravity component is removed from the accelerometer signal to obtain a proper acceleration signal. A double integration is performed on the proper acceleration signal to obtain a translation signal. A drift component of the translation signal is estimated. The estimated drift component is used to generate a drift correction signal. A phase compensation signal is determined based on a plurality of phase responses of a plurality of components of the optical image stabilization system. The drift correction signal and the phase compensation signal are applied to an actuator controller which controls movement of a lens within the portable device as part of the process for optical image stabilization.

Systems and methods of optically stabilizing an image. A lens obtains optical image information corresponding to an image. An image sensor converts the optical image information into electrical image information. A rotation signal related to rotational movement of a camera unit is generated using a first motion sensor that provides a signal related to rotation when the camera unit is rotated. A translation signal related to translational movement of the camera unit is generated using a second motion sensor that provides a signal related to translation when the camera unit undergoes translational movement. The translation signal is modified using the rotation signal to generate a corrected translation signal that compensates for lens movement which is different from second motion sensor movement. The corrected translation signal controls an actuator that adjusts a lens position with respect to the image sensor, based on rotation and translation experienced by the camera unit.