32results about How to "Feature extraction is fast" patented technology

The invention brings forward an aerial photography image and geographical data splicing method based on ORB feature matching. The method comprises the following steps: step 1, reading images to be spliced and geographical information; step 2, distributing tasks to processors; step 3, extracting ORB features of the images to be spliced; step 4, performing initialization estimation on parameters of a camera shooting the images to be spliced and solving a rotation matrix; step 5, improving estimation precision by use of bundle adjustment; step 6, performing initial splicing on the images to be spliced; step 7, performing brightness increment compensation on the images after the splicing, and based on multiband fusion of an image pyramid, performing conditional interpolation fusion on the geographical information after the splicing to obtain splicing result images; and step 8, and performing primary integrated splicing fusion on the splicing result images processed through all threads of each processor, and performing merging to obtain a final result image.

The invention provides a human face recognition method and device based on a residual-quantized convolutional neural network, in order to provide a human face recognition method and device capable ofperforming large-scale human face recognition and reducing the amount of calculation, thereby reducing hardware requirements and reducing training time. The human face recognition method comprises thefollowing steps: step S1, constructing a convolutional neural network model and performing training; step S2, preprocessing a target image and performing preprocessing on a to-be-determined image; step S3, sequentially inputting the preprocessed to-be-determined image and the preprocessed target image to a feature extraction model to obtain a to-be-determined feature vector and a target feature vector; step S4, determining the consistent human face image according to the target feature vector and the to-be-determined vector, wherein the step S1 comprises a step of setting a predetermined layer to be a quantized layer, performing integer bit quantization on a quantized layer parameter so as to approximate a parameter matrix of the quantized layer. The present invention also provides a human face recognition device based on the residual-quantized convolutional neural network.

The invention discloses a hand-written character input method, which comprises: A, ,performing feature selection on prestored character classes and calculating a template in coarse classification; B, performing feature transformation on the prestored character classes and calculating a template in fine classification; C, acquiring a discrete coordinate sequence of a track point of an input character and adopting a smooth continuous function to adjust the size and shape of the hand-written input character and a centrobaric coordinate value; D, extracting feature to obtain a multidimensional feature vector of the hand-written character; E, selecting partial feature value of the hand-written input character, matching the hand-written input character with the template in coarse classification respectively, and selecting a plurality of candidate character classes with the maximum similarity; and F, performing feature transformation on the hand-written input character, matching the hand-written input character with the sample center of the candidate character classes selected from the template in fine classification, and determining the most similar character classes. The invention also discloses a hand-written character input system. The invention has higher speed of identifying the hand-written input character, and higher identification accuracy.

The invention discloses a polarization multi-feature based method for detecting an ocean floating radar target. The method comprises the following steps: (1) transmitting a pulse signal by a radar transmitter, receiving anchored data by a radar receiver, and constructing a receiving vector of a detection unit and a receiving vector of a reference unit in the anchored data under H0 hypothesis and H1 hypothesis; (2) respectively calculating two polarization features of the detection unit and two polarization features of the reference unit; (3) respectively calculating the relative Doppler peak height of the detection unit and the relative Doppler peak height of the reference unit; (4) respectively calculating the combined feature of the detection unit and the combined feature of the reference unit; (5) calculating the detection statistics of the detection unit; and (6) determining that a target exists in the detection unit and the H1 hypothesis is established if the detection statistics of the detection are more than zero, and determining a target does not exist in the detection unit and the H0 hypothesis is established if the detection statistics of the detection are not more than zero.

The embodiment of the invention provides an image processing method and device and electronic equipment, and the method comprises the steps: obtaining at least two to-be-processed images, wherein theat least two images are images corresponding to at least two sub-bands in a scanning synthetic aperture radar ScanSAR image; correcting the proportion of the at least two images by utilizing the geographic position information of the images to obtain at least two corrected images; performing feature extraction on the at least two corrected images based on an ORB method to obtain a feature extraction result; splicing the at least two corrected images according to the feature extraction result. Thus, the proportion of the images is corrected according to the geographic information of the images,the corrected images are spliced based on the ORB method, and rapid and high-precision splicing of the images is achieved.

A gesture recognition method based on a deep neural network and an attention mechanism belongs to the field of electronic information. The method comprises the following steps: firstly, introducing an ECA effective channel attention in a double-flow algorithm to enhance the attention of the double-flow algorithm on a gesture key frame, and respectively extracting space and time sequence characteristics in a dynamic gesture by utilizing a space convolutional network and a time convolutional network in the double-flow algorithm; secondly, a gesture frame with the highest attention degree is selected from the spatial stream through ECA, and corresponding hand posture features are extracted through the single-shot multi-frame detector technology; and finally, fusing the hand posture features with human body posture features and gesture time sequence features extracted from double flows, and then classifying and recognizing gestures. According to the method, verification is carried out on a Challarn2013 multi-mode gesture data set, the accuracy rate is 66.23%, and compared with a previous method that double-flow recognition is carried out on the data set only through RGB information, a better gesture recognition effect is obtained.

The invention relates to the field of central air conditioner energy consumption prediction, discloses a feature extraction method and system for energy consumption prediction, and aims to quickly screen input features of energy consumption prediction and improve the generalization performance of an energy consumption prediction algorithm. The method comprises the steps of collecting historical operation data of a to-be-analyzed central air conditioning system, and preprocessing the historical operation data to obtain an initial feature set; performing training according to the initial featureset to obtain a gradient boosting tree energy consumption prediction model, and calculating the contribution degree of each input feature; performing feature screening according to the contribution degree to obtain an optimized feature set; optimizing the gradient boosting tree energy consumption prediction model according to the optimization feature set, and obtaining a prediction value according to the optimized gradient boosting tree energy consumption prediction model; calculating a mean square error between the contribution degree and the predicted value; and screening the mean square error by adopting a preset feature screening termination condition to obtain an optimal feature set.

The invention discloses a finger-vein feature extraction method, a comparison method, a storage medium and a processor. The finger-vein feature extraction method includes such steps as convoluting a finger vein image with g (x, sigma 1) cos (k1x) in horizontal direction, convoluting finger vein image with g (y, sigma 1) cos (k2y) in vertical direction to obtain first intermediate result, convoluting of finger vein image with g (x, sigma 1) cos (k2y) in horizontal direction to obtain first intermediate result, convoluting the finger vein image with g (x, sigma 1) cos (k2y) in vertical directionto obtain first intermediate result, convoluting the the finger vein image with g (x, sigma 1) cos (k2y). The vein image is convoluted with g (x, sigma 1) sin (k 1 x) in horizontal direction and thenwith g (y, sigma 1) sin (k 2 y) in vertical direction to obtain the second intermediate result; Gaussian convolution of that vein image in the horizontal and vertical directions is perform and multiplied (shown in the description) to obtain a third intermediate result; The first intermediate result is subtracted from the second intermediate result, and the third intermediate sum is obtained to obtain the final result, that is, the filtering result of Gabor, that is, the Gabor feature of the image is extracted. The invention effectively reduces the calculation amount, improves the feature extraction speed, effectively reduces the calculation overhead and consumes the memory, meets the real-time requirement, and has low calculation complexity.

The invention provides an image feature extraction method and device and electronic equipment, and relates to the technical field of image processing, and the method comprises the steps of obtaining a to-be-processed image and a preset convolution kernel, wherein the to-be-processed image comprises an original image or a feature map, and the data types of the to-be-processed image and the convolution kernel are both fixed-point types; performing convolution processing on the to-be-processed image based on the convolution kernel to obtain a convolution processing result used for representing image features of the to-be-processed image, wherein the data type of the convolution processing result is a fixed point type; involving floating point calculation and conversion between fixed points and floating points in the convolution processing process. The invention can effectively improve the feature extraction precision and speed.

The invention relates to a tunnel cross section feature extraction method and device, computer equipment, a storage medium and a computer program product, is applied to the technical field of tunnel three-dimensional point cloud data feature extraction, and is used for improving the accuracy of tunnel cross section feature extraction. The method comprises the following steps: sampling cross section data of a target tunnel cross section to obtain sampling data; inputting the sampling data into a parameter estimation model corresponding to the cross section of the target tunnel, and estimating parameters in a contour line fitting model corresponding to the cross section of the target tunnel through the parameter estimation model based on the sampling data to obtain estimated values of the parameters in the contour line fitting model; and inputting the estimated value into the contour line fitting model to obtain a contour line of the target tunnel cross section as a first target feature of the target tunnel cross section.

The invention discloses a VI waveform feature extraction method for user power consumption behavior perception analysis, and relates to the field of power grid operation and maintenance. At present, aiming at a power consumption behavior feature extraction technology at home and abroad, starting from the perspective of load features, universality is poor, harmonic waves are extracted to serve as feature data, and variable speed drive type loads cannot be further distinguished. A current and voltage trajectory curve is collected; the closed area, the average curve distortion degree and the curve self-intersection point number of a trajectory curve are considered; five characteristics of a current and voltage trajectory curve, the slope of a near zero point of the middle section of an average curve and the clockwise or anticlockwise average curvature of a VI trajectory are considered, and mathematical mode feature extraction is carried out, thus obtaining a feature data set of user behavior perception analysis. The invention is high in feature extraction speed and easy to implement, multiple features jointly participate in user behavior perception, and the accuracy is high. The method can provide data support for user load identification, and has good economic benefit and practical value.