Disclosed herein is a computer-implemented method for assessing and optionally monitoring or controlling a texture of a surface. Additionally disclosed herein are a system including an input channel connected to a processing device, a processing device configured to derive and optionally monitor or control the at least one texture parameter of the surface, an output device and optionally at least on surface preparation device. Further disclosed herein is a computer program product for assessing and optionally monitoring or controlling a texture of a surface, including a data carrier storing a program code to be executed by a processor.

Disclosed herein is a computer-implemented method for assessing and optionally monitoring or controlling a texture of a surface. Additionally disclosed herein are a system including an input channel connected to a processing device, a processing device configured to derive and optionally monitor or control the at least one texture parameter of the surface, an output device and optionally at least on surface preparation device. Further disclosed herein is a computer program product for assessing and optionally monitoring or controlling a texture of a surface, including a data carrier storing a program code to be executed by a processor.

A detection apparatus includes an image acquisition interface that acquires a captured image captured by an imaging unit and a controller that generates or acquires a smoothed image yielded by smoothing the captured image. The controller compares the captured image and the smoothed image and detects a low-frequency region having a predetermined spatial frequency spectrum from the captured image.

Various arrangements for identifying and grading cancer in tissue samples are presented. A digital image of a stained tissue sample may be acquired. A Shearlet transform may be performed on the digital image of the stained tissue sample. Shearlet coefficients may be calculated based on the performed Shearlet transform of the normalized digital RGB image of the stained tissue sample. A trained neural network may be applied to create a plurality of feature maps using the digital image and Shearlet coefficients, wherein the trained neural network was trained using a plurality of images and Shearlet coefficients of a plurality of digital images. A classifier may be applied to an output of the trained neural network to identify whether cancer is present in the stained tissue sample. A notification may be output that is indicative of a grade of detected cancer in the sample.

Various arrangements for identifying and grading cancer in tissue samples are presented. A digital image of a stained tissue sample may be acquired. A Shearlet transform may be performed on the digital image of the stained tissue sample. Shearlet coefficients may be calculated based on the performed Shearlet transform of the normalized digital RGB image of the stained tissue sample. A trained neural network may be applied to create a plurality of feature maps using the digital image and Shearlet coefficients, wherein the trained neural network was trained using a plurality of images and Shearlet coefficients of a plurality of digital images. A classifier may be applied to an output of the trained neural network to identify whether cancer is present in the stained tissue sample. A notification may be output that is indicative of a grade of detected cancer in the sample.

A navigation method based on ground texture images, an electronic device and storage medium. The method includes: performing transform domain based image registration on an acquired image of a current frame and an image of a previous frame, and determining a first pose of the image of the current frame; determining whether the image of the current frame meets a preset condition, and if so, inserting the image of the current frame as the key-frame image into a map, and performing loop closure detection and determining a loop key-frame image; performing transform domain based image registration on the image of the current frame and the loop key-frame image, and determining a second pose of the image of the current frame; and determining an accumulated error according to the first pose and the second pose, and correcting the map according to the accumulated error.

Disclosed are systems, devices and methods for quantifying unique features of an object such as an artistic work to identify and authenticate the object and specific characteristics thereof using multi-spectral diagnostic characterization techniques and analytical algorithms. In some aspects, a method for creating an identification for an object includes acquiring image data of an object in two or more electromagnetic spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the two or more electromagnetic spectrums for each sample region; generating a digital identification associated with a unique data fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.

Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a standard white light image, a parallel-polarized image, and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment.

Apparatuses and methods are disclosed for assessing the texture of skin using images thereof. In exemplary embodiments, a texture map of an area of skin is generated from a combination of a standard white light image, a parallel-polarized image, and a cross-polarized image of the area of skin. The texture map is then flattened to remove the underlying curvature of the skin. A texture roughness metric is then generated based on the flattened texture map. An image of the texture map and the metric can be displayed to provide visual and alphanumeric representations of the texture of skin, thereby facilitating the comparison of baseline and follow-up images of the skin, such as those taken before and after treatment.

A method of providing a sharpness measure for an image comprises detecting an object region within an image; obtaining meta-data for the image; and scaling the chosen object region to a fixed size. A gradient map is calculated for the scaled object region and compared against a threshold determined for the image to provide a filtered gradient map of values exceeding the threshold. The threshold for the image is a function of at least: a contrast level for the detected object region, a distance to the subject and an ISO/gain used for image acquisition. A sharpness measure for the object region is determined as a function of the filtered gradient map values, the sharpness measure being proportional to the filtered gradient map values.