229 results about "Depth imaging" patented technology

The embodiments of this invention use a multi-point scanning geometry. This design maintains the high frame rate of the slit-scan system and still allows both grayscale and multi-spectral imaging. In a confocal configuration, the multi-point scanning system's confocal performance is close to that of a single point scan system and is expected to yield improved depth imaging when compared to a slit-scan system, faster imaging than a point scan system, and the capability for multi-spectral imaging not readily achievable in a Nipkow disk based confocal system.

The invention discloses a method for inverting a near-surface velocity model by utilizing preliminary waveforms. The method comprises acoustic wave equation-based wave field forward modeling and steepest descent-based waveform inversion technologies, and comprises the following steps of 1, extracting time-domain preliminary waveform records and an initial model; 2, calculating a simulated wave field and a wave field residual by utilizing acoustic wave equation staggered grid finite-difference forward modeling simulation; 3, reversely propagating the wave field residual to obtain a retransmission wave field; 4, calculating a gradient of a target function by utilizing the retransmission wave field and a forward propagation wave field, and calculating an updating step length; 5, updating a speed model; 6, inspecting whether the speed model is consistent with an iteration stopping condition, outputting the speed model if the speed model is consistent with the iteration stopping condition, otherwise returning to the step 2, and continuing iterative updating. According to the method, a wave equation theory-based full-waveform inversion technology is used as reference, and preliminary waves with higher energy and more stable waveforms are used for inversion, so that the multiplicity of solutions of full-waveform inversion is reduced, and the inversion stability and the calculation efficiency are improved; the accuracy of static correction and shallow depth imaging is improved.

A T-O-F depth imaging device rendering a depth image of an object and a method thereof are provided. In embodiments, a T-O-F depth imaging device renders a depth image of an object that has corrected depth values. The device includes a pixel array that uses an Electronic Rolling Shutter scheme. The device also includes a light source that transmits towards the object light that is modulated at an operating frequency, and an optical shutter that opens and closes at the operating frequency. The optical shutter further modulates the light that is reflected from the object, before it reaches the pixel array. The transmission of the light and the operation of the shutter change phase relative to each other while at least one of the pixels is sensing the light it receives, which permits a faster frame rate. Depth is determined by the amount of light sensed by the pixels, and a correction is computed to compensate for the changing phase.

A 3D depth imaging method and system are disclosed. The 3D depth imaging method involves radiating light at a measurement target object using a projection means and imaging the light using an image receiving means, and includes the steps of assigning a unique transmitting side address to a signal corresponding to each pixel of the projection means to encode the signal; projecting multiple light patterns at the projection means to transmit the signal; receiving the encoded signal at the image receiving means; separating the received signal to restore the address; and determining a pixel position of the object using the transmitting side address and the restored address. With the 3D depth imaging method and system, it is possible to exactly separate signals received by the image receiving means even when the signals are overlap and the geometrical structure of the object varies, and it is also possible to obtain a depth image that is robust against ambient environmental noise.

Methods, systems, and devices involving patterned radiation are provided in accordance with various embodiments. Some embodiments include a device for projecting pattern radiation. Some embodiments include a method for estimating coordinates of a location on an object in a 3D scene. Some embodiments include a system for estimating the coordinates of a location on an object in a 3D scene. A variety of radiation patterns are provided in accordance with various embodiments. Some embodiments may relate to the use of patterned illumination to identify the angular information that may be utilized to measure depth by triangulation.

The present invention relates to near-field scanning optical microscopy (NSOM) and near-field / far-field scanning microscopy methods, systems and devices that permit the imaging of biological samples, including biological samples or structures that are smaller than the wavelength of light. In one embodiment, the present invention permits the production of multi-spectral, polarimetric, near-field microscopy systems that can achieve a spatial resolution of less than 100 nanometers. In another embodiment, the present invention permits the production of a multifunctional, multi-spectral, polarimetric, near-field / far-field microscopy that can achieve enhanced sub-surface and in-depth imaging of biological samples. In still another embodiment, the present invention relates to the use of polar molecules as new optical contrast agents for imaging applications (e.g., cancer detection).

The embodiment of the invention provides a monocular structured light depth imaging method based on a convolutional neural network. The method comprises the following steps: S1, carrying out visual calibration on a monocular structured light imaging hardware system; s2, generating a synthetic training data set; s3, carrying out data augmentation and preprocessing; s4, constructing a convolutionalneural network; s5, determining a loss function; s6, setting an optimizer; s7, training a convolutional neural network; and S8, testing the convolutional neural network. The method provided by the embodiment of the invention is used for training a synthetic training data set of a convolutional neural network, specific optimization and improvement are carried out on face recognition, the imaging effect and the recognition accuracy when monocular structure light depth imaging is adopted for face recognition are improved, the network calculation amount is reduced, and the real-time performance under the imaging quality requirements for high precision and high resolution is improved.