761results about How to "Improve registration accuracy" patented technology

A tracking and navigation system is provided. The system includes an imaging or treatment device, a tracker device, and a fiducial marker. At least part of the imaging or treatment device is movable relative to a patient. The tracker device is mounted on the imaging or treatment device and is movable therewith relative to the patient. The fiducial marker may be fixed relative to the patient to define a patient coordinate system. The fiducial marker is detectable by the tracker device to substantially maintain registration between the tracker device and the patient coordinate system. A tracking and navigation kit including the tracker device and at least one fiducial marker may also be provided, for example, for retrofitting to existing imaging or treatment devices

The invention provides an SAR image registration method based on SIFT and normalized mutual information. The method includes the steps that firstly, a standard image I1 and an image to be registered I2 are input and are respectively pre-processed; secondly, features of the pre-processed image I1 and features of the pre-processed image I2 are extracted according to the MM-SIFT method to acquire initial feature point pairs Fc and SIFT feature vectors Fv1 and Fv2; thirdly, initial matching is carried out through the Fv1 and the Fv2; fourthly, the Fc is screened for the second time according to the RANSAC strategy of a homography matrix model, final correct matching point pairs Fm are acquired, and a registration parameter pr is worked out according to the least square method; fifthly, I2 is subjected to space conversion through affine transformation, and a roughly-registered image I3 is acquired through interpolation and resampling; sixthly, pr serves as the initial value of normalization information registration, I1 and I2 are subjected to fine registration through the normalized mutual information method, a final registration parameter pr1 is worked out, and a registered image I4 is output. The method can be quickly, effectively and stably carried out, and SAR image registration precision and robustness are improved.

The invention provides a precise registration method of multilook point cloud, comprising the following steps: respectively selecting one piece of point cloud overlapping approximately from two piecesof global point cloud to be registered to serve as the target point cloud and the reference point cloud; utilizing a principle direction bonding method to realize the preregistration of the target point cloud and the reference point cloud; utilizing the principle direction test method to judge and realize the consistency of the preregistration principle directions of the target point cloud and the reference point cloud; respectively calculating the curvature of each point in the target point cloud and the reference point cloud; respectively obtaining characteristic matching point symetries P0and Q0 according to curvature similarity; using the iterative closest point matching algorithm to realize the precise registration of the target point cloud and the reference point cloud by utilizingthe characteristic matching point symetries P0 and Q0; and completing the registration of the two pieces of global point cloud. The method is characterized by high computation speed and high registration precision, thus being capable of realizing good registration effect.

A unit (110) acquires an image, sensed by an image sensing device (100), of a physical object having an outer appearance that can be expressed by line segments. A unit (120) stores defining data used to classify line segments configuring a 3D virtual object that simulates the outer appearance of the physical object into a plurality of groups and to define respective line segments belonging to the groups. A unit (130) selects one group from the groups one by one without any repetition. The unit (130) projects line segments which belong to the selected group onto a physical space using the defining data used to define that line segments, and the image of the physical space. The unit (130) then updates a position and orientation of the image sensing unit based on the line segments projected onto the physical space and a feature in the image of the physical object.

The invention provides a method and apparatus for the reproduction of color images by multi-color dithering. Multi-color dithering is an extension of bi-level dithering for halftoning an input image using as primary colors an arbitrary number of inks. The inks may comprise standard inks or non-standard inks such as non-process color inks, opaque inks, metallic inks, variable color inks and fluorescent inks. Multi-color dithering provides a solution for creating artistic multi-color dithered images, whose screen elements are made of artistic color screen shapes such as micro-letters, symbols and ornaments. When printed at high resolution and at high registration accuracy, multi-color dithering using large dither arrays incorporating artistic dither shapes provides an effective solution for preventing counterfeiting. The use of non-standard inks offers additional protection. Multi-color dithering also offers a solution for printers requiring that all inks are printed side by side, without overlaps.

The invention discloses an image registration and multi-resolution fusion-based panoramic image splicing method. An adaptive threshold of sequence images is subjected to binary processing and image contour information is extracted by utilizing a pixel neighborhood relationship; a similar region of an image contour structure is quickly estimated through a similarity measurement method for Hu momentinvariant features; and SIFT feature point extraction is performed in a similar region part of two original images. Moreover, feature point initial matching is performed by using a bidirectional K-nearest classification algorithm; mismatched points are screened by an RANSAC algorithm; and an affine transformation matrix among the images is estimated. In addition, in an image fusion aspect, brightness equalization and local color mapping processing is adopted. Then, through an improved Laplacian pyramid multi-resolution fusion algorithm, spliced images are fused to realize seamless splicing. According to the method, the image splicing time is shortened, the image splicing efficiency is improved, the similar region can be quickly located, the mismatch in splicing is effectively improved, and the image registration precision is improved.

A method for controlling the automatic chromaticity of printing machine by use of camera includes such steps as light printing a marker for detecting the chromaticity error, using a camera to take the digital image of the marker area, transmitting the digital image, detecting the chromaticity error, outputting chromaticity control signal, and fast regulating the position of printing drum.

The invention provides an automatic registration method of an airborne laser point cloud and an aerial image. The automatic registration method comprises the following steps of: extracting a building outline from the point cloud without interpolation for the laser point cloud; obtaining building angular characteristics as a registration element through outline regularization; automatically matching the point cloud with the image according to the homonymic angular characteristics with the aid of an approximate exterior orientation element of the aerial image; and utilizing bundle block adjustment and a cyclic iterative policy so as to realize overall optimal registration of aerial image and point cloud data. The registration method provided by the invention has the following advantages that: the building outline is directly extracted from the laser point cloud without interpolation for the laser point cloud so as to obtain the building angular characteristics as the registration element, which prevents interpolation errors and improves the registration accuracy; and the exterior orientation element of the image is solved by virtue of bundle block adjustment, and meanwhile overall optimal registration between the aerial image and the airborne laser point cloud is realized by adopting the cyclic iterative registration policy.

The present invention discloses a method for registering synthetic aperture radar image with a change area based on a point pair constraint and Delaunay, and the problem of SAR image registration with a change area without using ground control point data is mainly solved. The method comprises a step of filtering inputted floating image and reference image respectively and then using a SURF algorithm to extract feature points, a step of carrying out similarity measure matching of a normalized descriptor matrix on obtained reference image feature point and floating image feature point and then carrying out distance and wiring direction angle constraint to obtain an initial candidate matching point pair set, a step of using matching feature points in the floating image and the reference image to construct Delaunay, searching a homonymous triangle pairs in the Delaunay of the two images to update the candidate matching point pair set, and taking non-repeated vertices in all homonymous triangle pairs corresponding to a minimum affine error as a final matching point pair set, and calculating an affine change matrix, carrying out affine conversion and bicubic interpolation on the floating image, and obtaining a final registration result image.

The invention relates to a multi-scale normal feature point cloud registering method. The multi-scale normal feature point cloud registering method is characterized by including the steps that two-visual-angle point clouds, including the target point clouds and the source point clouds, collected by a point cloud obtaining device are read in; the curvature of radius neighborhoods of three scales of points is calculated, and key points are extracted from the target point clouds and the source point clouds according to a target function; the normal vector angular deviation and the curvature of the key points in the radius neighborhoods of the different scales are calculated and serve as feature components, feature descriptors of the key points are formed, and a target point cloud key point feature vector set and a source point cloud key point feature vector set are accordingly obtained; according to the similarity level of the feature descriptors of the key points, the corresponding relations between the target point cloud key points and the source point cloud key points are preliminarily determined; the wrong corresponding relations are eliminated, and the accurate corresponding relations are obtained; the obtained accurate corresponding relations are simplified with the clustering method, and the evenly-distributed corresponding relations are obtained; singular value decomposition is carried out on the final corresponding relations to obtain a rigid body transformation matrix.

The invention discloses an improved ICP object point cloud splicing method for fusing fast point characteristic histogram. The method comprises the steps of projecting a standard sinusoidal digital grating onto the surface of the object to be measured, photographing stripe images of the surface of the object projected with the standard sinusoidal digital grating from different angles of view by aCCD camera, and obtaining photographing point clouds from multiple angles of view; for two image point clouds that need to be stitched together, building a k-D tree and interpolate to obtain that interpolated point cloud; for the two interpolated point clouds to be spliced, computing the fast point feature histogram, and obtaining the point cloud by random sampling consistent transformation; usingthe improved iterative nearest point method to obtain the first interpolated point cloud which is precisely registered; overlaying point cloud and mesh to realize the mosaic of two different angles of view of the shooting point cloud. The invention has low requirement for the initial position of the splice point cloud, the robustness is remarkably improved, the local optimization is not easy to fall into, the splice accuracy is improved, and the precise splicing of the point cloud under multi-view angles is realized, so that the practical industrial application requirements can be met.

The invention involves a multi-mode medical body data three-dimensional video-method. Most the existing technologies are based on two-two-dimensional lay or two-three dimensional lay, which do not realize the integrated display course of medical body data aligning and amalgamation. The said method includes standardization mutual information-way aligning based on multi resolution ratio and single drop multi-mode straight body protracting-way amalgamation display. The previous one includes three approaches, such as coordinate transform, orientation criterion and multi resolution ratio optimize; the latter includes five approaches, such as impress function definition, the calculation of illumination model, image composition, single drop multi-mode show of the multi-mode data and amalgamation display. The invention can realize the integrated display course of medical body data aligning and amalgamation, and is a real two-three dimensional multi-mode video-method.

The invention discloses a variable-viewing angle obstacle detection method for a robot based on outline recognition. The method comprises the following steps: (1) calibrating a binocular visual system by using a calibration plate; (2) simultaneously shooting the same scene by virtue of two video cameras, carrying out epipolar rectification on left and right images, then extracting edges and connecting the edges; (3) searching an outline of a target object, and matching the outline; (4) rebuilding three-dimensional point clouds of the outline, and optimizing three-dimensional outline point clouds; (5) rotating a turntable at a certain angle, ensuring an overlapping region in two times of shooting, carrying out three-dimensional rebuilding in another direction; (6) searching the overlapping region of two groups of three-dimensional outline point clouds; (7) carrying out initial registration on the two groups of three-dimensional outline point clouds by adopting a seven-parameter method; (8) providing corresponding point weights of the overlapping region, carrying out iterative closest point operation on the three-dimensional outline point clouds of the overlapping region, and finishing accurate registration of the three-dimensional outline point clouds. The method disclosed by the invention is high in registration accuracy, small in operand and relatively high in instantaneity.

The invention provides an overall registering method for carrying out automatic global optimization on registering initial values of existing multi-view laser point clouds. The overall registering method is characterized in that firstly, a multi-view laser point cloud registering global optimization overall adjustment model is established and detailedly deduced, and by estimating the density of the point cloud, all two-view clouds with a certain overlapping degree are automatically detected; then a K-D tree is used for searching for approximate homonymy points in the two-view clouds with the certain overlapping degree, the approximate homonymy points are used as observed values to be substituted into the global optimization overall adjustment model, respective optimal rotation and translation transformation parameters of the multi-view laser point clouds are obtained at the same time through iterative adjustment calculation, and therefore overall precise registering of the multi-view three-dimensional laser point clouds is achieved. The method lays emphasis on improvement of the overall registering precision of the multi-view three-dimensional laser point clouds, disordered and messy multi-view three-dimensional laser scanning point clouds can be treated at the same time, registering experiments are carried out through actual multi-view three-dimensional laser point cloud data, and results prove that on the basis of guaranteeing registering efficiency, the overall registering precision is improved effectively.

The invention relates to a mobile platform-based color laser point cloud imaging system and belongs to the field of environment awareness. The mobile platform-based color laser point cloud imaging system comprises a mobile platform, a support rod, a camera, a camera calibration module, a motion control module, a man-machine interaction module, a spatial alignment module, a mechanical wrist, a depth map establishing module, a filter module, a laser scanning radar, an attitude and azimuth reference module, and a laser point cloud dyeing module, wherein the mechanical wrist comprises a connecting shaft A, a connecting shaft B and a connecting cantilever. The mobile platform-based color laser point cloud imaging system acquires the three-dimensional point cloud information and image information through the laser scanning radar and the camera, and realizes the imaging of the color laser point cloud by the spatial alignment method and the laser point cloud dyeing method of the laser scanning radar and the camera. Compared with the traditional methods, the mobile platform-based color laser point cloud imaging system improves the registration accuracy of the laser scanning radar and the camera, has more real color three-dimensional point cloud obtained, can acquire the color three-dimensional point cloud in wide environment range by means of the mobile platform simultaneously, and has wider application range.

The invention provides a three-dimensional entity model reconstruction method based on dense point cloud data. The dense point cloud data is acquired by a laser 3D scanner; 3D laser scanning can rapidly acquire three-dimensional coordinate data of the surface of a measured object in a large-area and high-resolution manner; a rapid and high-precision modeling method is matched, so that the characteristics of rapidness, non-contact, initiative, accuracy and the like are highlighted, data obtained in real time has the characteristics of high accuracy, high density and the like, reconstruction ofa three-dimensional model becomes more convenient, a digital model of an actual object can be created in the virtual world, and rapid and high-accuracy three-dimensional entity model reconstruction based on dense point cloud data is achieved; according to the semantic-based feature extraction method, a large amount of working time is saved, the semi-automatic mode is adopted for operation, the feature extraction precision and accuracy are effectively improved, the cost is greatly reduced, and the precision of the reconstructed three-dimensional solid model is obviously improved.

The invention relates to a multi-resolution medical image registration method based on the quantum-behaved particle swarm optimization, which is characterized in that the method comprises the following step of: firstly removing the backgrounds of the two images to be registered to keep the images off the interference of noise; next obtaining the images with low resolution from the two background-removed images via the wavelet transform, using the low resolution to be the object, taking the normalized mutual information as the objective function, using the quantum-behaved particle swarm optimization, then the images with the high resolution to be the object, and obtaining the rotation amount and the translation amount between the two images to be registered to finish the image registration by using the Powell method. With solving a plurality of local extrema based on the objective function of the mutual information, the invention greatly improves the registration precision and speed, reaching up to the sub-pixel level; and is widely applicable to the fields of the image discrimination of the clinical diagnosis, the framing of the radiation treatment, the image guided surgical, etc.

The invention discloses a multiple time phase remote sensing image registration method based on an edge and spectral reflectivity curve. The multiple time phase remote sensing image registration method based on the edge and spectral reflectivity curve mainly solves the problems that stability is insufficient and accuracy is low when registration is conducted on two time phases of multiple spectral image sets with variation regions and whole luminance difference in the prior art. The multiple time phase remote sensing image registration method based on the edge and spectral reflectivity curve comprises the following steps: small wave high frequency coefficient matrixes are added and divided, the small wave high frequency coefficient matrixes are added simultaneously, the input two time phases of the multiple spectral image sets are preprocessed, and thus two time phases of strong edge images are acquired; registration parameters of edge alignment metric of the two time phases of the strong edge images are calculated to register the two time phases of image sets and acquire coarse registration image sets; the coarse registration image sets are converted into opposite object spectral reflectance image sets, small wave high frequency coefficients of time phases 1 of the object spectral reflectance image sets are reconstructed to acquire coarse registration edge images, and the coarse registration edge images conduct local matching on opposite object spectral reflectance images of the time phases 1 and time phases 2 to acquire a final registration image set. The multiple time phase remote sensing image registration method based on the edge and spectral reflectivity curve can be used for change detection, integrating and embedding.

The invention provides an augmented reality flight simulator based on a plurality of fixed cameras, which comprises a simulator cabin body, a tracker, a helmet-mounted display and a plurality of fixed cameras. The fixed cameras are used for shooting real scenes, and the shot images are selected and cut through a computer with the selected and cut frames as real scenes during virtual and real fusion. The tracker is used for tracking the pose of the heads of users and calculate the accurate positions of virtual scenes. The helmet-mounted display is used for displaying the final virtual and real fused images.

The invention discloses a method and system for performing three-dimensional reconstruction of an indoor complete scene based on a consumer-level depth camera. The method comprises: a depth image is obtained and adaptive bilateral filtering is carried out on the depth image; with the filtered depth image, visual odometer estimation is carried out, segmentation is carried out on an image sequence automatically based on a visual content, and closed-loop detection is carried out between segments, and global optimization is carried out; and according to the optimized camera trajectory information, weighted volume data fusion is carried out to reconstruct a three-dimensional model of an indoor complete scene. According to the method and system disclosed by the invention, edge keeping and denoising of a depth image are carried out by using an adaptive dual-side filter algorithm; because of the automatic segmentation algorithm based on a visual content, an accumulation error in visual odometer estimation can be reduced and the registration precision can be improved; and on the basis of the weighted volume data fusion algorithm, the geometric details at the surface of the object can be kept. Therefore, a technical problem of improving three-dimensional reconstruction precision in an indoor scene can be solved and thus a complete, accurate, and refined indoor scene model can be obtained.

The invention discloses a remote sensing image registration method based on anisotropic gradient dimension space, which mainly solves the problem of relatively low correct matching rate under the condition of relatively great brightness nonlinear change of the remote sensing images. The implementing steps of the remote sensing image registration method based on anisotropic gradient dimension space are as follows: (1) inputting remote sensing image pairs; (2) constructing dimension space of anisotropic diffusion; (3) calculating a gradient amplitude image; (4) detecting feature points; (5) generating a main direction of the feature points; (6) generating a descriptor of each feature point; (7) matching the feature points; (8) deleting wrongly matched feature point pairs; and (9) registering a reference image and a to-be-registered image. As feature point detection, feature point main direction generation and feature point descriptor generation are carried out on the gradient amplitude image in the anisotropic dimension space, the situation of relatively great brightness nonlinear change of the images can be dealt efficiently, and the remote sensing image registration method based on anisotropic gradient dimension space can be applied to complex multisource and multispectral remote sensing image registration.

The invention discloses a positioning and locating device for radiotherapy and a positioning method of a dynamic target region. A therapy robot has an operating arm, wherein a compact linear electronic accelerator is arranged at one end of an operating arm of the therapy robot, and a secondary collimator is installed at one end of the compact linear electronic accelerator; a robot treatment table is arranged on a corresponding position of a double-image C-arm system, an C-arm sliding rail laser locator is arranged inside the double-image C-arm system, and a left side locator for an C-arm installation space is arranged on a corresponding position at the outer part of the double-image C-arm system. The positioning and locating device provided by the invention is provided with two groups of X-ray image systems, so as to realize binocular imaging; when the C-arm sliding rail rotates and a group of X-ray image systems is started, and CBCT (Cone Beam Computed Tomography) imaging can be realized; according to the positioning method, different imaging ways are adopted for a static target region and a dynamic target region, the rapidity and timeliness of the binocular imaging, the high quality, high definition and image registration of the CBCT imaging can be fully exerted.

The present invention belongs to the field of three-dimensional reconstruction technology and relates to a fast three-dimensional reconstruction method based on Kinect camera. Compared with the conventional technique, the method of the present invention uses the method of main optical axis constraints to remove the imaging error points in a single-view point cloud and improves the quality of the point cloud to a certain extent, making the three-dimensional reconstruction higher accuracy. In addition to that, considering that the point cloud has poor accuracy and less coincidence, the existing point cloud registration algorithm based on RGB-D data is improved. Although some computational time overhead is increased, the registration accuracy of the algorithm is greatly improved. This means the great possibility of the algorithm converging to a local minimum.