12 results about "Top-hat transform" patented technology

The invention discloses a star sensor rapid tracking method based on dynamic window blind restoration, and belongs to the field of star sensor algorithms. The method comprises the following steps: estimating a coordinate estimation value of each star in an overlapped view field on a star map at the next moment through a historical star map, and extracting a local window image of a star point corresponding to each star in the overlapped view field; gaussian filtering, top-hat transformation and connected domain processing are carried out on the local window image, star point trailing information is extracted based on a Zernike moment sub-pixel edge extraction method, and a star point area is restored by using a Richardson-Lucy algorithm; then, star point centroids are extracted from the restored image, star points which are tracked successfully are screened out in combination with motion consistency, and initial attitude parameters are calculated through a QUEST method; and finally fusing the star point information newly entering the field of view to obtain attitude parameters of the star sensor under the reference coordinate system. According to the method, the image traversal range is greatly reduced, and the method has important engineering significance.

Methods and systems are provided herein for generating a morphology-based recomposition based on 3-dimensional input image data including an image stack of 2-dimensional (2D) images, the morphology-based recomposition being a final image generated by performing one or more white top hat (WTH) transforms on each 2D image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated PSF and image data.

The present application belongs to the technical field of image analysis, and particularly relates to a self-adaptive cleaning control method for a cleaning robot based on image texture analysis, which comprises the following steps: performing top-hat transformation on an original image by using a multi-angle linear structural element to obtain an anisotropic residual texture image and calculate a gray run matrix; constructing a gray run inverse coupling inertia based on a negative correlation mapping relationship between gray and run; obtaining a morphological erosion residual ratio representing stain stability through a morphological erosion operation, and then fusing to generate a sediment consolidation modulus; and adjusting the command marching speed and the command shear torque of the robot accordingly. The present application solves the problems of grid line interference and misjudgment caused by reflection by deeply coupling visual features and physical stability, and realizes accurate matching between cleaning intensity and stain state.

The invention discloses a rock fracture extraction method fusing morphological top-hat transformation, belongs to the field of digital core analysis, and discloses a rock micro-fracture extraction method based on an unsharpened mask and morphological top-hat transformation, which comprises the following steps: extracting rock micro-fractures based on top-hat transformation, and carrying out dynamic weighted fusion on the rock micro-fractures and extracted rock main fractures to obtain a rock micro-fracture extraction result; complete reconstruction of the rock core fracture network is realized; according to the scheme, an interactive processing method and a morphological operation mechanism are integrated, high-precision separation of main and micro cracks of rock and a rock matrix is realized through an unsharpened mask and a morphological-based image processing method, and accurate extraction of microstructure information in a rock core is realized; and support is provided for better serving establishment and application of various rock physical models.

The present application belongs to the technical field of image processing, and particularly relates to a surface oxidation detection method and system for overhead strand, which comprises the following steps: obtaining gradient field data of the overhead strand image; determining the local main direction entropy of the pixel point based on the angle difference between the gradient direction of all neighborhood pixel points of the pixel point and the local main direction, and determining the entropy threshold through clustering to screen anchor points; constructing a gradient profile line along the local main direction of the anchor point, determining the local rust width of the anchor point according to the gradient amplitude change of the pixel points on the gradient profile line; obtaining the adaptive structure element parameters based on the local rust width and the local main direction of the anchor point, and performing morphological top-hat transformation to calculate the response value of the anchor point, and marking the anchor points with a response value greater than the response threshold as oxidation defects. The present application significantly improves the accuracy of overhead strand surface oxidation rust recognition by adaptively matching the local geometric morphology of the rust.

The invention belongs to the technical field of radiation detection, and particularly relates to a synthetic aperture radiometer target detection method and device under a high dynamic condition based on continuous frame image processing. Utilizing a synthetic aperture radiometer to continuously obtain millimeter wave signals of a target scene, and obtaining visibility functions of a plurality of continuous frames according to preset integral time; performing background subtraction, Gaussian low-pass filtering and Fourier transform on the visibility function sequence to obtain a plurality of frames of brightness temperature maps; solving a rotation matrix of the current frame image according to the attitude angle at the current moment, and performing registration fusion on the brightness temperature image according to the rotation matrix to obtain a preliminary result image; a convolution kernel is set according to antenna array arrangement and platform motion imaging properties, the preliminary result image is enhanced, then the image is further enhanced by adopting multi-scale white top-hat transform, and a final result image is obtained; and setting a target detection window, screening candidate target points according to pixels in the window, and finally setting a threshold value according to a full-image pixel mean value and variance to determine the positions of the target points.

This invention discloses an infrared small target detection method based on adaptive multi-scale filtering, belonging to the field of infrared target detection technology. The steps include: image preprocessing, multi-scale top-hat transformation, adaptive threshold binarization, multi-dimensional screening of candidate targets, and IoU-based cross-frame matching and target validity determination. This invention employs a three-scale elliptical structuring element to enhance target robustness, achieves adaptive binarization based on global statistics, and suppresses false alarms through noise-sensing parameter switching and temporal verification. The entire process has low computational complexity and high real-time performance, enabling stable and accurate infrared small target detection under complex backgrounds, strong noise, and low signal-to-noise ratio conditions. It is suitable for scenarios such as military reconnaissance, security monitoring, infrared guidance, and low-altitude target detection.

The application discloses an infrared small target detection method based on an elliptical ring Top-Hat, and comprises the following steps: constructing a structure element of four angles of a detection target, wherein the structure element comprises an external structure element, an internal structure element and an elliptical structure element; defining an open operation and a closed operation according to the structure element; obtaining a top-hat transformation operation and a bottom-hat transformation operation through morphological transformation based on the open operation and the closed operation; obtaining a first detection result through fusion based on the top-hat transformation operation and the bottom-hat operation; and segmenting the first detection result based on an adaptive threshold to obtain an infrared small target detection result. The elliptical structure element obtained by constructing the structure element of four angles of the detection target can fully acquire contrast information, and the problem of serious false alarm in the case of a complex background is solved, so that the application can be widely applied to the field of infrared detection technology.

The invention belongs to the technical field of image analysis, and particularly relates to a cleaning robot self-adaptive cleaning control method based on image texture analysis, which comprises the following steps of: performing top-hat transformation on an original image by using a multi-angle linear structure element to obtain an anisotropic residual error texture image and calculate a gray scale run length matrix; constructing a gray scale run length reverse coupling inertia based on a negative correlation mapping relation between the gray scale and the run length; obtaining a morphological erosion residual ratio representing the stain stability through morphological corrosion operation, and fusing to generate a sediment consolidation degree modulus; therefore, the instruction advancing speed and the instruction shearing moment of the robot are adjusted. Through deep coupling of visual features and physical stability, the problems of grid line interference and reflection misjudgment are solved, and accurate matching of the cleaning intensity and the stain state is achieved.

The invention belongs to the technical field of image processing, and particularly relates to a surface oxidation detection method and system for an overhead stranded conductor, and the method comprises the steps: obtaining the gradient field data of an overhead stranded conductor image; based on the included angle difference between the gradient direction and the local main direction of all neighborhood pixel points of the pixel points, determining the local main direction entropy of the pixel points, and determining an entropy threshold through clustering to screen anchor points; a gradient section line is constructed in the local main direction of the anchor point, and the local rust width of the anchor point is determined according to gradient amplitude changes of pixel points on the gradient section line; and on the basis of the local rust widths and the local main directions of the anchor points, self-adaptive structure element parameters are obtained, morphological top-hat transformation is executed to calculate response values of the anchor points, and the anchor points with the response values larger than a response threshold value are marked as oxidation defects. According to the method, the accuracy of identifying the oxidation rust on the surface of the overhead stranded wire is remarkably improved through self-adaptive matching of the local geometric morphology of the rust.

Methods and systems are provided herein for generating a morphology-based recomposition based on 3-dimensional input image data including an image stack of 2-dimensional (2D) images, the morphology-based recomposition being a final image generated by performing one or more white top hat (WTH) transforms on each 2D image, generating two or more image layers based on two or more WTH transformed images, and scaling adjacent image layers based on one or more scaling factors, each scaling factor being based on an estimated point spread function (PSF), two structure element sizes, and standard deviations of the estimated PSF and image data.