9 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.

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.

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.

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.

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.