31results about How to "Simplify the feature extraction process" patented technology

The invention provides an on-line identification method of a compound pattern, which comprises the following steps of: firstly defining the characteristic of the compound pattern based on the formation of the element and the formation of the space relationship, wherein the characteristic is a set of combined characteristic vectors, the length of the characteristic vectors is the sum of the type of the component and the type of the space relationship, and each vector represents the number of the type of the element or the type of the space relationship in the formation of the compound pattern;designing a method for extracting the increment of the combined characteristic based on the definition of the combined characteristic vectors, passively updating the combined characteristic vectors along with the continuous pen input of the user, and performing the characteristic match only based on the updated vectors, so that the method simplifies the process for extracting the characteristic and guarantees the instantaneity of the characteristic match; and designing a space relationship erroneous judgment treating method in the process for the characteristic match, so that the method further guarantees the adaptability of the optional input of the user. The method has certain universality and expandability, is applied to the different professional fields, and realizes the intelligent human-machine interaction based on the hand painted draft on-line identification.

The invention discloses a face recognition method based on reference features. The method comprises the following steps that: scale invariant features and local binary pattern features of a face image to be recognized are extracted; a principal component analysis method is utilized for dimensionality reduction to obtain the image features of the face image to be recognized; the similarity of the image features to a cluster center is calculated by utilizing the obtained image features to obtain the reference features of the face image to be recognized; and the similarity of the reference features of the face image to be recognized and the reference features of training data concentration is calculated to obtain an analysis result. The reference features of the face image provided by the invention comprise texture information and structure information of the face image, so that the method provided by the invention can more comprehensively represent the face compared with the method in the prior art, which only represents the texture information or the structure information of the face. The process of feature extraction is simple and easy to realize; the recognition result is highly precise; high recognition rate of different facial gestures of the same person is realized.

A method and apparatus for estimating the blurriness of an image are disclosed. In this method, an image is obtained, and then the image is transformed to obtain a multi-scale representation of the image. An error vector between the gradient normalized histogram at each scale and the original gradient normalized histogram of the image is calculated, and the error vector at each scale is weighted and summed by the inverse of their sum of squares. The ambiguity estimation value is obtained based on the weighted and summed error vectors at each scale. Using the solutions of the above embodiments, the evaluation effect on blurred images of various contents is good, and the evaluation results have a high correlation with the evaluation results of human eyes.

The invention discloses a traffic identification method based on a bag of word (BOW) model and statistic features. The method adopts the BOW model matched with a feature extraction method, trains collected network traffic features, and thus obtains a feature vector corresponding to each network category. For new network traffic, similarly, by extracting traffic features, utilizing the BOW module to obtain a corresponding feature vector, and then sequentially comparing with a feature vector of each network category which is previously established, the category corresponding to the feature vector with highest matching degree serves as a category tag of the new network traffic. The BOW method combines with an unsupervised k-means clustering method and a supervised k-nearest neighbor method, thereby being more suitable for multi-category classification. Due to the fact that the BOW model is not sensitive to space position, during feature extraction, arrangement according to time series of features is not required, and processing is convenient.

The present invention relates to a surface defect detection method, terminal equipment and storage medium. The method includes: S1: collecting defect images of detection objects to form a training set, and marking the defect images in the training set; S2: constructing defect images The detection model is trained by inputting the training set into the defect detection model to obtain the trained defect detection model; the defect detection model is constructed based on the Faster R-CNN network, and its feature extraction network is a VGG-16 network, and the VGG-16 network , superimpose the output of the third layer and the output of the fifth layer; S3: Input the defect image to be detected into the trained defect detection model, and obtain the defect location frame and defect type in the defect image; S4: According to the defect image to be detected The defect localization frame in the defect image is used to segment the defect. The invention is based on the Faster R-CNN network and the threshold segmentation method, so that the defect type, position and outline can be output only by inputting an image during the detection process, that is, the end-to-end detection of defects is realized.

The invention provides a segmentation method for crop canopy images based on mean shift. The steps comprise firstly, re-sampling the crop canopy images in RGB color space, then, exchanging the crop canopy images in HSI space, next, indicating each pixel in the crop canopy images as a tetrad combined by four feature values, namely G-R, G-B, H, S, wherein R, G and B respectively indicate red weight, green weight and blue weight of pixel in the RGB space and H and S respectively indicate chroma and saturation of pixel in the HSI color space, then, employing mean shift algorithm to segment the crop canopy images into different types, finally, calculating the feature typical values of each type, if the first and the second components of the typical value are larger than zero, then, it is crops, if not, then, it is not crops. The invention has the advantages that parameters needed to be set is relatively less, the process of features extraction is simple, the algorithm is easy to be realized, and segmentation accuracy rate is high.

The present invention provides a method for extracting local reflection symmetry axes in images based on multi-instance subspace learning. The method includes the following steps: step 1, obtaining local image multi-instance feature packages of natural images; step 2, constructing partial random projection tree; Step 3, decision analysis: analyze the multi-instance feature package of the natural image whose symmetry axis is to be extracted and the multi-instance feature package in the training data set with a subspace classifier to obtain the analysis result. The feature extraction process of the invention is simple and easy, and the recall rate and accuracy rate of the extraction result are high.

The invention provides a print-form Arabic alphabet identification method based on boundary characteristic, wherein the upside, downside, left and right boundaries is regarded as a wave, each boundary is regarded as an assembly of a series of wave elements, and the following boundary characteristics are extracted therefrom: the number of the component wave, the number of the zero line, the length of the first zero line in the right boundary, the length of the first zero line in the downside boundary, the length of the longest zero line in the upside boundary, the length of the longest zero line in the right boundary, the length of the longest zero line in the upside boundary, the length of the longest zero line in the downside boundary and the number of the positive line in the upside boundary, moreover, the height width ratio of the alphabets and the alphabet auxiliary part are involved as the identification characteristics, finally, each print-form Arabic alphabet is identified by using four decision trees according to the four kinds of formats of the alphabets-the independent, the beginning, the middle and the end.

The invention discloses a live pig walking stride frequency extraction method based on depth image skeleton end analysis. The extraction method includes the steps of removing backgrounds in depth camera images, extracting skeletons, pruning the skeletons and matching skeleton drawings, the ends of the pectoral appendage skeleton and the pelvic appendage skeleton of a live pig in all frames of images are extracted, the far side attribute and the near side attribute of the ends of the two skeletons are judged, a sine curve is fitted through change point sets of coordinate relations of the far sides and the near sides of the fore limbs and the posterior limbs of the live pig in the image frames of a live pig motion sequence, and then the live pig walking stride frequency characteristics are extracted. The extraction method has the advantages of long distance, non-invasion and automatic extraction based on computer vision; the feature extraction process is simple and easy to implement; the extraction method not only is suitable for collecting the live pig walking stride frequency features, but also can be used for detecting walking stride frequency of a horse, a bull, a sheep and other animals walking with the four limbs.

A method and device for human target recognition provided by the present invention, the method comprising: obtaining a depth image; extracting image pixel features in the depth image; inputting the image pixel features into a human body deep learning model for identification and classification; Judging whether the classification of the image pixel features matches the existing human body part labels in the human body deep learning model; if the classification of the image pixel features matches the existing labels in the human body deep learning model, then the output and The label corresponding to the pixel feature. The invention adopts a deep learning model to identify image pixel features, and at the same time completes the detection and identification of human body objects, simplifies the detection and identification process, and improves the detection and identification efficiency.