Systems, computer-implemented methods, and computer program products to focus a microscope. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analyzer component that can analyze sub-images of respective sample images to identify one or more sub-images having a maximized variance of a gradient derivative corresponding to the one or more sub-images. The respective sample images can be acquired at one or more focal positions along an optical axis of a microscope. The computer executable components can further comprise a selection component that can select an image, from the respective sample images, that comprises the one or more sub-images identified. The computer executable components can also comprise a focus component that, based on a focal position corresponding to the image selected, can focus the microscope to the focal position.

Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.

Apparatus, system and method for tracking an image target in a system, wherein a system receives an image comprising a plurality of pixels. The received image is processed via a plurality of different recursive motion model kernels in parallel to provide a plurality of kernel outputs, wherein each of the motion model kernels may include a respective pixel mask. Per-pixel energy is estimated of at least some of the plurality of kernel outputs. Velocity of at least one of the image pixels may also be estimated by generating a directional energy vector for each motion model kernel. The per-pixel energy and velocity estimates are fused to produce a fused estimate representing at least some of the motion model kernels for the image.

A method includes obtaining, at one or more computing devices, an input image, applying an image sharpening process to at least one of one or more chroma components of the input image, subsampling the one or more chroma components of the input image to reduce a spatial resolution of the one or more chroma components of the input image, encoding the input image subsequent to applying the image sharpening process and subsampling, and performing at least one of storing or transmitting the input image subsequent to encoding.

In accordance with at least some embodiments of the present disclosure, a process to improve computed tomography (CT) to cone beam computed tomography (CBCT) registration is disclosed. The process may include receiving a CT image generated by CT-scanning of an object, and receiving a CBCT image generated by CBCT-scanning of the object. The process may include generating an image mask based on Digital Imaging and Communications in Medicine (DICOM) information extracted from the CBCT image. For a specific pixel in the CBCT image, the image mask contains a corresponding data-field indicating whether the specific pixel contains image data generated based on the CBCT-scanning of the object. The process may further include generating a registered image by utilizing the image mask to perform a DIR between the CT image and the CBCT image.

Systems and methods for providing convolutional neural network based image synthesis using localized loss functions is disclosed. A first image including desired content and a second image including a desired style are received. The images are analyzed to determine a local loss function. The first and second images are merged using the local loss function to generate an image that includes the desired content presented in the desired style. Similar processes can also be utilized to generate image hybrids and to perform on-model texture synthesis. In a number of embodiments, Condensed Feature Extraction Networks are also generated using a convolutional neural network previously trained to perform image classification, where the Condensed Feature Extraction Networks approximates intermediate neural activations of the convolutional neural network utilized during training.

Systems and methods are described for securing credentials with optical security features formed by quasi-random optical characteristics (QROCs) of credential substrates. A QROC can be a pattern of substrate element locations (SELs) on the substrate that includes some SELs that differ in optical response from surrounding SELs. During manufacturing, a QROC of a substrate can be characterized, hidden by a masking layer, and associated with a substrate identifier. During personalization, personalization data can be converted into an authentication graphic formed on the substrate by de-masking portions of the masking layer according to a de-masking pattern. The graphic formation can result in a representation that manifests a predetermined optical response only when the de-masking pattern is computed with knowledge of the hidden QROC. The authentication graphic and optical response can facilitate simple human authentication of the credential without complex or expensive detection equipment.

One embodiment provides a method, including: receiving a plurality of communication events associated with a pixel of an imaging device; identifying a frequency associated with the communication events, wherein the identifying a frequency comprises determining a number of communication events occurring within a predetermined time interval or determining a mean time interval between the communication events; determining, from a plurality of pixels neighboring the pixel, a frequency range comprising an upper frequency limit and a lower frequency limit; resolving, from the identified frequency and the determined frequency range, whether the pixel comprises a non-conforming pixel; and masking, if the pixel comprises a non-conforming pixel, subsequent communication events from the non-conforming pixel. Other aspects are described and claimed.

Various examples are directed to apparatus and methods for enhancing an X-ray medical image. In one example, a method for X-ray dental images enhancement is provided. The method includes receiving an input image and applying the noise reduction trough Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on the sliding window, which is calculated following a recursive two-dimensional scheme; contrast enhancement to the filtered image, including using recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (SW-CLAHE) utilizing a Truncation Threshold Surface (TTS) for calculation of the truncation threshold for the local histogram in each sliding window. The method also includes applying sharpness enhancement to the input image, including using Fast Recursive Adaptive Unsharp Masking (FRA-UM) utilizing a sliding window with two or one-dimensional recursion, to obtain a sharpened image. The contrast enhanced image is linearly mixed with the sharpened image using a control coefficient to obtain an output image, and the output image is provided to a display of a user.

Various examples are directed to apparatus and methods for enhancing an X-ray medical image. In one example, a method for X-ray dental images enhancement is provided. The method includes receiving an input image and applying the noise reduction trough Recursive Locally-Adaptive Wiener Filtration (RLA-WF) based on the sliding window, which is calculated following a recursive two-dimensional scheme; contrast enhancement to the filtered image, including using recursive Sliding Window Contrast Limited Adaptive Histogram Equalization (SW-CLAHE) utilizing a Truncation Threshold Surface (TTS) for calculation of the truncation threshold for the local histogram in each sliding window. The method also includes applying sharpness enhancement to the input image, including using Fast Recursive Adaptive Unsharp Masking (FRA-UM) utilizing a sliding window with two or one-dimensional recursion, to obtain a sharpened image. The contrast enhanced image is linearly mixed with the sharpened image using a control coefficient to obtain an output image, and the output image is provided to a display of a user.