Abstract: Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.

Abstract: A vehicle navigation system includes a Kalman filter having a first filter receiving satellite positioning data and a second filter receiving vehicle sensor data. The first filter generates a first state vector estimate and a corresponding first state error covariance matrix. The second filter generates a second state vector estimate and a corresponding second state error covariance matrix. A third filter receives the first and second state vector estimates and the first and second state error covariance matrices, and generates a combined state vector estimate and a corresponding combined state error covariance matrix. A prediction processor generates a predicted state vector estimate and a predicted state error covariance matrix from the combined state vector estimate and the combined state error covariance matrix. The predicted state vector estimate and the predicted state error covariance matrix are provided to the first filter, the second filter, and the third filter.

Abstract: In order to determine extrinsic parameters of a vehicle vision system or an aspect of a vehicle vision system, road lane markings may be identified in an image captured by a camera and provided to the vehicle vision system. For one or more of the identified road lane markings, a first set of parameters defining an orientation and a position of a line along which a road lane marking extends in an image plane may be determined. Also, a second set of parameters defining an orientation and a position of a line along which a road lane marking extends in a road plane may be determined. A linear transformation that defines a mapping between the first set of parameters and the second set of parameters may be identified. The extrinsic parameters may be established based on the identified linear transformation.

Abstract: A system for calibrating a vision system of an object. For example, a system for calibrating a surround view system of a vehicle. The system may be configured to capture, via a first and second image sensor, a first and second image of a plurality of objects. Upon capturing such information, the system may be configured to determine a position and orientation of the first and the second image sensor relative to the plurality of objects based on the first and the second captured image. Further, the system may be configured to determine a relative position and orientation between the first and the second image sensor based on a result of the determining a position and orientation of the first and the second image sensor. Also, the system may be configured to determine intrinsic and extrinsic parameters of the first and the second image sensor.

Abstract: A system for calibrating a surround view system of an object, such as a vehicle. The system may be implemented via calibration logic that may include software, hardware, firmware, or a combination thereof. The system may include positioning a set of markers on a plane at least partially surrounding the object. The plane may be in a field of view of a camera of the surround view system, and the camera may include a predetermined intrinsic calibration. The system may also include determining a first plurality of extrinsic parameters of the camera with respect to the set of markers. The system may repeat the positioning of the set of markers and the determination of the first plurality of extrinsic parameters for at least one other camera of the surround view system.

Abstract: A surround view system that can provide a surround view, e.g., a 360° view, from a vehicle by way of cameras positioned at various locations on the vehicle. The cameras can generate image data corresponding to the surround view, and a processing device can process the image data and generate the surround view on a simulated predetermined shape that can be viewed from a display. The simulated predetermined shape can have a flat bottom with a rectangular shape and a rim with a parabolic shape.

Abstract: Systems and methods are provided for detecting edges in an image. In an example method, the image is smoothed using a filter. A gradient magnitude and angle are determined for each pixel of the image. A non-maximum suppression is performed on the resulting image data. A double threshold with an upper and a lower threshold are applied to the resulting image data to determine the edges in the image. The upper and the lower thresholds are calculated automatically using a distribution of intensity values of the image. In example systems, an image is acquired by a camera, or other image acquisition units. An image processing unit is configured to detect the edges in the image using an adaptive threshold edge detection method.

Abstract: A method and device are described which are configured to establish whether a traffic sign has at least one graphical feature extending linearly thereon. A portion of image data which represents at least a portion of the traffic sign is identified. Coefficients of a two-dimensional spectral representation of the portion of the image data are calculated. The coefficients of the two-dimensional spectral representation are determined for Fourier space coordinates disposed along a line in Fourier space. Based on the determined coefficients it is established whether the traffic sign has the at least one graphical feature extending linearly on the traffic sign.