Abstract: In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

Abstract: An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

Abstract: In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

Abstract: In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

Abstract: In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

Abstract: An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

Abstract: Apparatus and methods to unwarp at least portions of distorted, electronically-captured images are described. Keypoints, instead of an entire image, may be unwarped and used in various machine-vision algorithms, such as object recognition, image matching, and 3D reconstruction algorithms. When using unwarped keypoints, the machine-vision algorithms may perform reliably irrespective of distortions that may be introduced by one or more image capture systems.

Abstract: Method for generating a lane departure warning in a vehicle, comprising acquiring a plurality of frames of a digital image of a road on which the vehicle is running, the digital image of a road including the image of a lane within which the vehicle is running and of marking lines of the lane, for each of the acquired frames, extracting edge points of the frame, analyzing the edge points to evaluate a lane departure status, the evaluation including performing a lane departure verification procedure including identifying in the frame points representative of the position of the lane marking lines, generating a lane departure alert if a lane departure status is detected by the lane departure verification procedure.

Abstract: A sequence of images is processed to generate optical flow data including a list of motion vectors. The motion vectors are grouped based on orientation into a first set of moving away motion vectors and a second set of moving towards motion vectors. A vanishing point is determined as a function of the first set of motion vectors and a center position of the images is determined. Pan and tilt information is computed from the distance difference between the vanishing point and the center position. Approaching objects are identified from the second set as a function of position, length and orientation, thereby identifying overtaking vehicles. Distances to the approaching objects are determined from object position, camera focal length, and pan and tilt information. A warning signal is issued as a function of the distances.

Abstract: A sequence of images obtained by a camera mounted on a vehicle is processed in order to generate Optical Flow data including a list of Motion Vectors being associated with respective features in the sequence of images. The Optical Flow data is analyzed to calculate a Vanishing Point by calculating the mean point of all intersections of straight lines passing through motion vectors lying in a road. An Horizontal Filter subset is determined taking into account the Vanishing Point and a Bound Box list from a previous frame in order to filter from the Optical Flow the horizontal motion vectors. The subset of Optical Flow is clustered to generate the Bound Box list retrieving the moving objects in a scene. The Bound Box list is sent to an Alert Generation device and an output video shows the input scene where the detected moving objects are surrounded by a Bounding Box.

Abstract: A panning device for processing relative motion vectors and absolute motion vectors obtained from a video sequence, includes: a panning filter module, such as a high-pass IIR filter, for subjecting relative motion vectors to panning processing, an adder module for adding the relative motion vectors subjected to panning in the panning filter module to absolute motion vectors to obtain respective summed values of motion vectors, a clipping module for subjecting the summed values of motion vectors obtained in the adder module to clipping according to a selected cropping window for obtaining final output absolute motion vectors, a first leak integrator arranged after the panning filter module, and a second leak integrator arranged after the clipping module.

Abstract: Image processing circuitry processes image frames in a sequence of image frames, for example, to identify objects of interest. The processing includes filtering motion vectors associated with a current image frame, grouping the filtered motion vectors associated with the current image frame into a set of clusters associated with the current image frame, and selectively merging clusters in the set of clusters associated with the current image frame. At least one of the filtering, the grouping and the merging may be based on one or more clusters associated with one or more previous image frames in the sequence of image frames. Motion vectors included in merged clusters associated with a previous frame may be added to filtered motion vectors before grouping the motion vectors in the current frame.

Abstract: Method for generating a lane departure warning in a vehicle, comprising acquiring a plurality of frames of a digital image of a road on which the vehicle is running, the digital image of a road including the image of a lane within which the vehicle is running and of marking lines of the lane, for each of the acquired frames, extracting edge points of the frame, analyzing the edge points to evaluate a lane departure status, the evaluation including performing a lane departure verification procedure including identifying in the frame points representative of the position of the lane marking lines, generating a lane departure alert if a lane departure status is detected by the lane departure verification procedure.

Abstract: In an embodiment, focusing an image-capture device such as, e.g., a camera including an optical system displaceable in opposite directions (A, B) via a focusing actuator, is controlled by evaluating a scale factor for the images acquired by the device. An accumulated value of the variations of the scale factor over a time interval (e.g., over a number of frames) is produced and the absolute value thereof is compared against a threshold. If the threshold is reached, which may be indicative of a zoom movement resulting in image de-focusing, a refocusing action is activated by displacing the optical system via the focusing actuator in the one or the other of the opposite focusing directions (A or B) as a function of whether the accumulated value exhibits an increase or a decrease (i.e., whether the accumulated value is positive or negative).

Abstract: An embodiment relates to a method for the detection of texture of a digital image, including providing a raw data image of the image by means of Bayer image sensors, determining noise in at least a region of the raw data image and determining the texture based on the determined noise without using a high pass or low pass filter.

Abstract: A sequence of images is processed to generate optical flow data including a list of motion vectors. The motion vectors are grouped based on orientation into a first set of moving away motion vectors and a second set of moving towards motion vectors. A vanishing point is determined as a function of the first set of motion vectors and a center position of the images is determined. Pan and tilt information is computed from the distance difference between the vanishing point and the center position. Approaching objects are identified from the second set as a function of position, length and orientation, thereby identifying overtaking vehicles. Distances to the approaching objects are determined from object position, camera focal length, and pan and tilt information. A warning signal is issued as a function of the distances.

Abstract: A sequence of images obtained by a camera mounted on a vehicle is processed in order to generate Optical Flow data including a list of Motion Vectors being associated with respective features in the sequence of images. The Optical Flow data is analyzed to calculate a Vanishing Point by calculating the mean point of all intersections of straight lines passing through motion vectors lying in a road. An Horizontal Filter subset is determined taking into account the Vanishing Point and a Bound Box list from a previous frame in order to filter from the Optical Flow the horizontal motion vectors. The subset of Optical Flow is clustered to generate the Bound Box list retrieving the moving objects in a scene. The Bound Box list is sent to an Alert Generation device and an output video shows the input scene where the detected moving objects are surrounded by a Bounding Box.

Abstract: Image processing circuitry processes image frames in a sequence of image frames, for example, to identify objects of interest. The processing includes filtering motion vectors associated with a current image frame, grouping the filtered motion vectors associated with the current image frame into a set of clusters associated with the current image frame, and selectively merging clusters in the set of clusters associated with the current image frame. At least one of the filtering, the grouping and the merging may be based on one or more clusters associated with one or more previous image frames in the sequence of image frames. Motion vectors included in merged clusters associated with a previous frame may be added to filtered motion vectors before grouping the motion vectors in the current frame.

Abstract: An embodiment relates to a method for color processing of an input image, the method including the steps of low-pass filtering of the input image to obtain a low-pass component, high-pass filtering of the input image to obtain a high-pass component, processing the input image for edge detection to obtain edginess parameters, and performing a color-space transformation of the input image based on the low-pass component, the high-pass component, and the edginess parameters.

Abstract: According to an embodiment, a sequence of video frames as produced in a video-capture apparatus such as a video camera is stabilized against hand shaking or vibration by:—subjecting a pair of frames in the sequence to feature extraction and matching to produce a set of matched features;—subjecting the set of matched features to an outlier removal step; and—generating stabilized frames via motion-model estimation based on features resulting from outlier removal. Motion-model estimation is performed based on matched features having passed a zone-of-interest test confirmative that the matched features passing the test are distributed over a plurality of zones across the frames.