A vision based light detection and ranging (LIDAR) system captures images including a targeted object and identifies the targeted object using an object recognition model. To identify the targeted object, the vision based LIDAR system determines a type of object and pixel locations or a boundary box associated with the targeted object. Based on the identification, the vision based LIDAR system directs a tracking beam onto one or more spots on the targeted object and detects distances to the one or more spots. The vision based LIDAR system updates the identification of the targeted object based on the one or more determined distances.

Disclosed is a method for recognizing a bitting code of a key. The method comprises: using a back light source and/or a lateral light source to collect an image of a key to be recognized; and recognizing, on the basis of the image, a bitting code of the key to be recognized. Further disclosed are an apparatus for recognizing a bitting code of a key, and a storage medium and an image collection device. The present disclosure can improve the efficiency of recognizing a bitting code of a key and reduce the wear of the key during a recognition process.

Disclosed is a method for recognizing a bitting code of a key. The method comprises: using a back light source and/or a lateral light source to collect an image of a key to be recognized; and recognizing, on the basis of the image, a bitting code of the key to be recognized. Further disclosed are an apparatus for recognizing a bitting code of a key, and a storage medium and an image collection device. The present disclosure can improve the efficiency of recognizing a bitting code of a key and reduce the wear of the key during a recognition process.

A topographic measurement method includes provision of a sample including first surface provided with plurality of salient patterns. The first surface of the sample is illuminated by means of structured light that defines several repetitive patterns. The structured light is emitted at first angle with respect to first surface. A first image of first surface of sample illuminated by structured light is acquired. The first image is acquired at second angle with respect to first surface. A second image of illuminated sample is acquired. The second image differs from first image by value of exposure time. The first image is compared with second image to determine presence of at least one artefact on the first image. A reference image is formed from the first image and the second image. The reference image is devoid of any artefact. A quantity representative of the first surface is calculated from the reference image.

A topographic measurement method includes provision of a sample including first surface provided with plurality of salient patterns. The first surface of the sample is illuminated by means of structured light that defines several repetitive patterns. The structured light is emitted at first angle with respect to first surface. A first image of first surface of sample illuminated by structured light is acquired. The first image is acquired at second angle with respect to first surface. A second image of illuminated sample is acquired. The second image differs from first image by value of exposure time. The first image is compared with second image to determine presence of at least one artefact on the first image. A reference image is formed from the first image and the second image. The reference image is devoid of any artefact. A quantity representative of the first surface is calculated from the reference image.

A portable electronic device may include a housing, a display at least partially within the housing, a front cover coupled to the housing and positioned over the display, and a biometric sensor module configured to illuminate an object and capture an image of the object through the front cover. The biometric sensor module may include a first lens positioned below the front cover, a first light source positioned below the first lens and configured to project, through the first lens, a dot pattern on the object, a second light source positioned below the first lens and configured to illuminate, through the first lens, the object with a flood of light, a second lens positioned below the front cover, and a light sensor positioned below the second lens and configured to capture an image of the object.

A portable electronic device may include a housing, a display at least partially within the housing, a front cover coupled to the housing and positioned over the display, and a biometric sensor module configured to illuminate an object and capture an image of the object through the front cover. The biometric sensor module may include a first lens positioned below the front cover, a first light source positioned below the first lens and configured to project, through the first lens, a dot pattern on the object, a second light source positioned below the first lens and configured to illuminate, through the first lens, the object with a flood of light, a second lens positioned below the front cover, and a light sensor positioned below the second lens and configured to capture an image of the object.

Systems and methods for identifying material components on graft products include an image capture device for obtaining image data of a graft product and a processor for processing image data with an artificial neural network, the artificial neural network localizing and classifying materials of the graft product from the image data. The image capture device may include an optical filter and an ultraviolet light source for ultraviolet fluoresce imaging of the graft product. Using the captured image data, the artificial neural network may identify unwanted materials on the graft product for subsequent removal, such as, for example, fascia or flesh on a piscine skin.

Systems and methods for identifying material components on graft products include an image capture device for obtaining image data of a graft product and a processor for processing image data with an artificial neural network, the artificial neural network localizing and classifying materials of the graft product from the image data. The image capture device may include an optical filter and an ultraviolet light source for ultraviolet fluoresce imaging of the graft product. Using the captured image data, the artificial neural network may identify unwanted materials on the graft product for subsequent removal, such as, for example, fascia or flesh on a piscine skin.

An image processing apparatus comprises: an acquisition unit that acquires a first image obtained through shooting and distance information of the first image; a detection unit that detects a main subject from the first image; an extraction unit that extracts another subject from the first image based on the distance information of the main subject; a setting unit that sets parameters of one or more virtual light sources that emit virtual light to the main subject and the extracted other subject; and a processing unit that generates from the first image a second image in which the main subject and the other subject are illuminated with the virtual light using the parameters set by the setting unit.