13 results about "Epipolar line" patented technology

In various examples, epipolar constraint-based cross-camera calibration validation is disclosed. For a pair of cameras that have partially overlapping fields of view, a shared region of their overlapping fields of view may be extracted and used as the basis to perform an epipolar constraint-guided feature descriptor matching process. A camera calibration metric may be computed based on the degree to which a feature descriptor appearing at a pixel of the first image aligns as expected in the second image with an epipolar line associated with the pixel of the first image, where the epipolar line is computed using extrinsic camera calibration parameters associated with the pair of cameras. Epipolar matching may be performed for a plurality of feature points and an aggregate validation score computed based on measuring the computed deviations for each feature. A sensitivity analysis may be applied to better assess the usefulness of the validation score.

The invention discloses a correlated region feature assisted binocular vision depth estimation method, which comprises the following steps of: 1, matching geometric shapes of object images in left and right views through barycentric coordinates and rotational inertia of the geometric shapes of the object images; 2, through intersection of a key point connecting line on the geometric shape contour of the image and an epipolar line, obtaining a search range of a minimum scale matched with imaging points on the epipolar line of the left and right views; 3, constructing geometric figures of imaging points and key points on the geometric shape contour of the image, calculating the rotational inertia of constructing the geometric figures, matching the imaging points in the left and right views, and calculating the parallax and depth of the matched binocular imaging points; according to the invention, through subdividing the geometric shape area of the image, the imaging point matching range of parallax calculation is reduced, and the binocular matching efficiency is improved; according to the method, the associated region features are used for assisting parallax calculation, and missing of matching feature information of poor regions such as shielding, weak texture and exposure is made up, so that the parallax calculation accuracy is enhanced, and the binocular stereo matching precision is improved.

An image-stitching method compensates electronic rolling shutter (ERS) distortion and parallax to produce a composite image. A first and second image from respective sensors are received. An ERS correction mapping is computed at a lower resolution than a parallax correction mapping. A far point and a near point defining an initial epipolar line are identified. A compensated near point is determined from ERS data, and a compensated epipolar line is formed by linear interpolation between the far point and the compensated near point. A one-dimensional search along the compensated epipolar line yields a parallax translation between the images. A warp mapping is determined from the parallax translation and the ERS correction mapping, and applied to blend the images into a composite. The technique decouples ERS estimation from parallax and is suitable for multi-sensor capture devices and post-capture stitching.

A system for producing a depth map can include a processor and a memory. The memory can store a candidate depth production module and a depth map production module. The candidate depth production module can include instructions that cause the processor to: (1) identify, in a first image, an epipolar line associated with a pixel in a second image and (2) sample, from a first image feature set, a set of candidate depths for pixels along the epipolar line. The depth map production module can include instructions that cause the processor to: (1) determine a similarity measure between a feature, from a second image feature set, and a member of the set and (2) produce, from the second image, the depth map with a depth for the pixel being a depth associated with a member, of the set, having a greatest similarity measure.

The invention discloses a multi-source data high-precision fusion method based on deep learning. According to the invention, the step of epipolar line calculation can ensure the geometric consistency of the point cloud data. An epipolar line is a straight line connecting one point in a three-dimensional space and a corresponding projection point of the epipolar line on an image, so that accurate geometric constraint is provided for subsequent point cloud processing by correct calculation of the epipolar line. The calculation of epipolar line parameters provides directional information for denoising and smoothing of the point cloud. In the de-noising process, filtering can be carried out along the epipolar line direction, so that abnormal points can be identified and removed more effectively, and meanwhile, useful structure information is reserved. By calculating the epipolar line in advance, the calculation amount in the subsequent processing step can be reduced. In the point cloud registration and fusion process, epipolar line information can be utilized to quickly position corresponding points, so that the convergence speed of the algorithm is accelerated. Therefore, more accurate basic data is provided for subsequent three-dimensional reconstruction.

This invention relates to a stereo matching method based on smoothing filtering and neighborhood pixel assistance. It incorporates neighboring points of the left and right points to be matched into the matching process, using the distance of the neighboring points from the point to be matched and whether they lie on epipolar lines for differential judgment. The judgment conditions differ for points in different neighborhood locations. On the left and right absolute phase maps, if the absolute phase values ​​of the actual pair of points to be matched differ, the phase matching of neighboring points will perform certain judgment compensation, ensuring that the two points with the highest similarity in absolute phase in a local region are successfully matched, thereby improving matching accuracy.

The application discloses a kind of multi-source data high-precision fusion methods based on deep learning.The step of epipolar line calculation in the application can ensure the consistency of point cloud data in geometry.Epipolar line is the straight line connecting a point in three-dimensional space and its corresponding projection point on the image, therefore, the correct calculation of epipolar line provides accurate geometric constraints for subsequent point cloud processing.The calculation of epipolar line parameters provides directional information for point cloud denoising and smoothing processing.In the denoising process, filtering can be performed along the epipolar line direction, which can more effectively identify and remove outliers while retaining useful structural information.By pre-computing epipolar lines, the computational load in subsequent processing steps can be reduced.In the point cloud registration and fusion process, epipolar line information can be used to quickly locate corresponding points, thereby speeding up the convergence speed of the algorithm.Provide more accurate basic data for subsequent three-dimensional reconstruction.

The invention provides a three-dimensional shape measurement noise reduction method and device, computer equipment and a storage medium. According to the scheme, firstly, each measurement pair is controlled to obtain a calibration image, and then multiple groups of first sets are obtained according to the calibration image; and respectively calculating an epipolar constraint error and a monotonicity constraint error of each first coordinate pair, synthesizing the two errors to determine a comprehensive error, and removing the first coordinate pair of which the comprehensive error is lower than a first threshold value. And determining a transformation relation between the coordinate system of the projection equipment and the unified coordinate system, updating the coordinate pairs into a second set, carrying out two-time fusion on the second set to obtain a third set, and finally generating a three-dimensional point cloud according to the coordinate pairs in the third set. According to the scheme, the coordinate pairs are screened through the combination of the epipolar constraint and the monotonicity constraint, noise data generated by factors such as optical pollution are effectively removed, compared with a traditional epipolar constraint method, errors caused in the epipolar line direction can be further considered, and the quality of measured data is remarkably improved.

An apparatus configured to encode video data is configured to receive a first picture having a first resolution, receive a second picture having a second resolution, wherein the second resolution is different than the first resolution, and wherein the first picture and the second picture have at least partially overlapping fields of view, and encode a current block of the first picture, relative to the second picture, using affine prediction with a single parameter search, wherein the single parameter search is performed along an epipolar curve, and wherein the single parameter search is used to determine parameters of an affine prediction model.

There is provided a method of matching features depicted in images, comprising: detecting a first object depicted in a first image, projecting a first epipolar line, from the first object of the first image, to a second image, selecting second objects along the first epipolar line of the second image, projecting second epipolar lines, from the second objects of the second image, to a third image, projecting a third epipolar line from the first object of the first image to the third image, identifying on the third image, a third object along an intersection of the third epipolar line and a certain second epipolar line of the second epipolar lines, and generating an indication of the first object depicted in first image and the third object depicted in the third image as matches of a same physical object.

The present invention provides a method and system for detecting moving targets in multi-phase images based on epipolar constraints. First, the ASIFT algorithm is used to detect image feature points, and the RANSAC algorithm is used to eliminate mismatched points. Epipolar constraints are introduced to calculate the basic matrix. Secondly, points are calibrated on the region of interest in the original image, and the epipolar lines corresponding to the calibrated points in the region of interest are obtained in the target image using the basic matrix. Finally, a robust and efficient template matching method based on deep convolution features is used to calibrate the template image area to be matched in the original image, and template matching is performed in the target image. By comparing the positional relationship between the epipolar lines and the rectangular box of the template matching result in the target image, it is determined whether the target has moved. The present invention can effectively detect moving targets with high accuracy, has the characteristics of simple method, obvious effect, and easy engineering implementation, and is of great significance in actual production and life.

The invention relates to the technical field of SLAM, provides an SLAM loopback detection method and device and positioning equipment, and is used for improving the success rate of SLAM loopback. By determining the epipolar line of the first feature point in the historical frame image in the current frame image and the corresponding projection block of the image block of the first feature point on the epipolar line, the feature tracking of the feature point with accurate depth information in the historical frame image in the current frame image is realized. Therefore, the number of feature point matching pairs in the current frame image and the historical frame image is increased, and the success rate and robustness of SLAM loopback are ensured. On the other hand, by calculating descriptors of the feature points in the historical frame image and the feature points in the current frame image, whether a feature point matching pair composed of the feature points in the two frames of images is reserved or not is determined, so that the feature tracking result is verified by using the feature matching result, the precision of the feature point matching pair used by the SLAM loopback is ensured, and the accuracy of the SLAM loopback is improved. Therefore, the accuracy of SLAM loopback is improved.

A computer-implemented method for providing an item of detection information includes applying a function to a received first X-ray projection image, at least one received second X-ray projection image, and a received and / or ascertained item of relative arrangement information. The function is configured to identify one projection respectively from at least one object in a predefined epipolar region in the first X-ray projection image and in the at least one second X-ray projection image. The predefined epipolar region is arranged around an epipolar line on which the projection of the object is situated in the first X-ray projection image and / or in the at least one second X-ray projection image. A corresponding item of detection information is provided when the projection of the object is identified in the predefined epipolar region in the first X-ray projection image and in the at least one second X-ray projection image.