Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

Abstract: Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

Abstract: Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Systems and methods are disclosed for locating a retroreflective object in a digital image and/or identifying a feature of the retroreflective object in the digital image. In certain environmental conditions, e.g. on a sunny day, or when the retroreflective material is damaged or soiled, it may be more challenging to locate the retroreflective object in the digital image and/or to identify a feature of the object in the digital image. The systems and methods disclosed herein may be particularly suited for object location and/or feature identification in situations in which there is a strong source of ambient light (e.g. on a sunny day) and/or when the retroreflective material on the object is damaged or soiled.

Abstract: Provided is a visual vehicle speed estimation system based on camera output and calibration for such a vehicle speed estimation system. The calibration allows use of the system where the absolute camera position is unknown. Calibration determines an absolute-position-independent relationship between an image space placement and a physical space position relative to the camera. The calibration is done on the basis of camera output using vehicle features of known dimensions and some assumed physical constraints related thereto to provide a conversion relationship between image coordinates and physical space coordinates in a physical space defined in relation to the camera. This relationship is then used to estimate vehicle speeds based only on the visual information provided by the camera. Abstract is not to be interpreted as limiting.

Abstract: Provided is a visual vehicle speed estimation system based on camera output and calibration for such a vehicle speed estimation system. The calibration allows use of the system where the absolute camera position is unknown. Calibration determines an absolute-position-independent relationship between an image space placement and a physical space position relative to the camera. The calibration is done on the basis of camera output using vehicle features of known dimensions and some assumed physical constraints related thereto to provide a conversion relationship between image coordinates and physical space coordinates in a physical space defined in relation to the camera. This relationship is then used to estimate vehicle speeds based only on the visual information provided by the camera. Abstract is not to be interpreted as limiting.

Abstract: In accordance with one embodiment, a method of determining a transformation between a first image and a second image includes acts of, for at least one region in the first image, determining a corresponding region in the second image as a function of a transformation parameter and determining a similarity function as a function of the transformation parameter between the region in the first image and the corresponding region in the second image. In one embodiment, the method further includes acts of determining a similarity function between the first image and the second image using the similarity function for the at least one region, and determining a value of the transformation parameter, wherein the value substantially optimizes the similarity function between the first image and the second image.

Abstract: A single-site color image, such as a Bayer CCD image, is converted to a color space image using the resource of a Graphics Processing Unit (GPU). The Bayer image is loaded into the GPU along with commands to cause the texture engine in the GPU to use the Bayer image as a source texture and to compute, for each pixel in a destination image having same dimensions as the single-site color camera image, interpolated neighbor pixel values from the single-site color camera image for the remainder of said colors. A code image can be used to provide, for each pixel in the destination image, a value for each combination of color space image color and each Bayer image color. Each pixel is then computed as a sum of a product of each code image. value and a corresponding value selected from the corresponding source texture pixels and an interpolation of neighboring source texture pixels.

Abstract: A method for the detection of an irregularity in an object based on an image of the object that includes the steps of binarizing the image at a plurality of binarization thresholds to obtain a plurality of binarized images, extracting information from each of the binarized images, estimating the regular object resulting from the binarization at the respective binarization threshold of an image of a version of the object in which the irregularity is absent, combining the information extracted from each of the binarized images, and detecting the irregularity based on the combined information. A method for the detection of a defect in a solder element based on an X-ray image of the solder element.

Abstract: A geometric hashing method usable by a machine vision system for model-based recognition of an object. More specifically, in a computer having a texture engine, a method of pattern matching for recognition of objects within an image. The method comprises the following steps: deriving at least one target primitive representative of the image; forming at least one basis from at least one target primitive; in the texture engine, determining, for each one of the at least one basis, an affine invariant representation of the at least one target primitives; and identifying, using the affine invariant representation, at least one predefined model primitives that at least partially matches the at least one target primitives.