6 results about "Prewitt operator" patented technology

The invention relates to an image recognition method and system for a pointer type meter. The method comprises the following steps: acquiring an original image of a meter to be recognized; fusing the continuous multiple frames of original images through a Laplacian pyramid method to obtain a fused image; performing edge detection on the fused image through a Prewitt operator to obtain an edge enhanced image; establishing double detection heads, and respectively detecting the positions of a pointer and a dial in the edge enhancement image through the double detection heads to obtain position information; and according to the position information, calculating a relative angle between the pointer and the dial through a Hough transform method, and converting the relative angle into a reading so as to complete image recognition of the pointer type meter. According to the method, the accuracy and robustness of pointer meter image recognition in the weak light environment are effectively improved.

The application discloses a kind of steel wire rope magnetic flux leakage image defect detection methods under distortion noise, first acquisition magnetic flux leakage signal, then through detrending and spline difference mode are preprocessed and the image after pre-processing is blocked;Next, each image block is classified, and the template response image block of each image block is constructed using Prewitt operator;After power law transformation is carried out to the template response image block, the envelope image is obtained by taking envelope;Finally, the defect positioning operation is realized on the envelope image by binary threshold method.

The invention provides a spatial heterodyne interference correction method based on adaptive bilateral filtering and UDWT. The method is characterized by comprising the following steps: S1, acquiring an interferogram through a spatial heterodyne spectrometer, calculating edge response by using a Prewitt operator, and extracting edge features; and S2, on the basis of a Gaussian filter, constructing a bilateral filter combining a gray domain weight and a spatial domain weight, and improving the denoising effect of bilateral filtering. And S3, performing non-subsampled wavelet transform (UDWT) on the processed interferogram, decomposing the processed interferogram into sub-bands of multiple scales and directions, and performing reconstruction to obtain a denoised interferogram. S4, spectrum recovery is achieved through Fourier transform, a denoised spectrum signal is obtained, the characteristic peak of the denoised spectrum signal is more obvious, the curve is smoother, and meanwhile the peak signal-to-noise ratio and non-uniformity are remarkably improved. The method has a remarkable noise removal effect on spectral signals, and can be widely applied to noise processing of spatial heterodyne spectrums.

The invention discloses a bone development image enhancement method and device, equipment, a medium and a product, and relates to the field of image enhancement, and the method comprises the steps: applying a self-adaptive Prewitt operator and a self-adaptive Laplacian operator to a bone development image, and generating a first bone development image and a second bone development image; performing multi-scale processing on the bone development image, and applying a Sobel operator to the multi-scale bone development image to generate a third bone development image; fusing the three bone imaging images to generate a fused bone imaging image, performing CLAHE processing, and determining an equalized bone imaging image; performing non-local mean filtering on the equalized bone imaging image, determining a bone imaging image after non-local mean filtering, performing preprocessing, and determining a preprocessed bone imaging image; and performing connected domain analysis on the preprocessed bone development image, and determining the enhanced bone development image, thereby avoiding excessive enhancement and artifact generation of an area with overlarge pixel difference, inhibiting noise interference, and improving an image enhancement effect.

The invention discloses an image retrieval method based on depth edge information, which comprises the following steps: firstly, extracting depth features of an image from a deep neural network, and constructing a target information feature map through color information and spatial information of the image; secondly, extracting a horizontal edge graph and a vertical edge graph from the target information feature graph and the depth feature graph by using a Prewitt operator to construct an edge information weight and an edge information feature graph; then, weighting the edge information feature map by using an edge information weight, and obtaining a spatial aggregation feature vector through spatial aggregation; and calculating a channel difference weight value according to the edge information feature map and the spatial aggregation feature value, and weighting the spatial aggregation feature vector by using the channel difference weight to obtain a final feature vector. And principal component analysis and feature dimension compression are carried out on the final feature vector to obtain the final representation of the image. And performing similarity calculation by utilizing the final representation of the image during retrieval to obtain a retrieval result. According to the method, the image retrieval performance of the depth features can be improved.

The invention discloses a glass fiber reinforced plastic mold processing method and system based on feedback analysis. The method comprises the following steps: acquiring video data of N processing steps, extracting key frames to form a data set, extracting contour, texture and color features by applying a Prewitt operator and a color histogram, and constructing a first feature set; learning and reconstructing data features by adopting an auto-encoding network, generating a second feature set, analyzing feature changes in combination with a ResNet model, forming an intermediate change feature set, constructing a full-machining process feature table, comparing a real-time target feature set with the feature table, identifying machining defects and positioning problems, and generating an optimization scheme. Accurate optimization and real-time feedback analysis of the machining steps are achieved, the machining quality and the machining efficiency are improved, and the situation that machining evaluation is conducted depending on manual experience is reduced.