39 results about "Entropy rate" patented technology

An image is segmented into superpixels by constructing a graph with vertices connected by edges, wherein each vertex corresponds to a pixel in the image, and each edge is associated with a weight indicating a similarity of the corresponding pixels, A subset of edges in the graph are selected to segment the graph into subgraphs, wherein the selecting maximizes an objective function based on an entropy rate and a balancing term. The edges with maximum gains are added to the graph until a number of subgraphs is equal to some threshold.

The invention discloses a rice spike segmentation method of a big paddy field based on deep learning and super-pixel segmentation. According to the invention, by use of simple linear iteration clustering method in the super-pixel segmentation technology, adjacent pixels with similar characteristics form image blocks, i.e., super-pixels; based on automatic annotation and selection of large-scale training samples, the type of the super-pixels is discriminated through a convolution neural network in the deep learning technology so that initial segmentation of rice spikes is achieved; and based on the entropy rate-based super-pixel segmentation method, initial segmentation results are optimized. Thus, effects caused by great difference in colors, shapes, sizes, poses and textures of different varieties of rice spikes in different growth periods, serious irregular edges of the rice spikes, color aliasing of spike leaves, and uneven and changeable light, shielding and wind blowing in the field can be overcome; precise segmentation of different varieties of rice spikes in different growth periods is achieved; and the method is applicable to segmentation of rice spikes in the pot plant environment. Compared with the prior art, the method is advantaged by high precision and applicability.

The invention discloses an automatic random-walk CT lung parenchyma image segmentation method based on prior information. The method comprises the following steps that an original CT image is input, and binary discrete wavelet transformation is carried out on the input CT image to obtain an image M; a chest area is extracted from the image M in an entropy based superpixel method, and then processed to obtain an image T; the amount and position of seed points of the image T are obtained by utilizing the prior information guided by anatomy knowledge, and random-walk segmentation is carried out on the obtained seed points to obtain an initial parenchyma contour of the lung; and local mis-segmented areas of the initial parenchyma contour of the lung are repaired in a curvature rectification algorithm. Thus, the method of the invention can be used to solve the technical problem that in the present random-walk technology, the segmented lung parenchyma image is greatly influenced by the amount and positions of the seed points when the seed points are selected by manual interaction.

The invention discloses a color image tamper detection method based on local quaternion index moment through which algorithm time complexity can be greatly reduced and a replication tamper area can be accurately detected. Self-adaptive super-pixel partitioning is performed on an image to be detected after Gaussian smoothing filtering preprocessing by utilizing a super-pixel segmentation algorithm based on entropy rate with combination of non-subsample Shearlet transform; the quaternion index moment is utilized to express Sifer feature point local area features and similar block matching is performed on all the Sifer feature point local area features of each super-pixel block; a suspected tamper area is determined by utilizing the matching feature points in the similar blocks obtained in the step 2; and the replication tamper area is marked by utilizing morphological filtering operation.

The invention provides a gas-liquid two-phase flow pattern dynamics representation and identification method based on multi-scale arrangement entropy. The method includes the steps of firstly carrying out a gas-liquid two-phase flow pattern experiment with air and water as media to collect three kinds of gas-liquid two-stage different-flow-pattern electrical conductance fluctuation signals, then carrying out coarse graining processing on flow-pattern signal sequences according to a multi-scale concept to obtain coarse graining time sequences, calculating arrangement entropy of the time sequences in different scales, drawing a distribution map of the multi-scale arrangement entropy, analyzing dynamics evolution characteristics of the multi-scale arrangement entropy according to the gas-liquid two-phase different-flow-pattern characteristics, ultimately calculating the multi-scale arrangement entropy rate according to distribution map of the multi-scale arrangement entropy of different flow patterns to obtain distribution of the multi-scale arrangement entropy of all the flow-pattern signals, and accordingly achieving identification and classification of the flow patterns. According to the method, complexity of gas-liquid two-phase flow-pattern signals is disclosed in terms of time sequence themselves. The method has the advantages of being simple and quick in calculation, good in robustness and the like and is especially suitable for real-time processing of the two-phase flow-pattern signals.

An embodiment of the invention discloses an identification method and an identification device for a cheating behavior. The identification method comprises the steps of generating a historical behavior sequence according to a historical behavior event of to-be-evaluated equipment or account; calculating entropy rate of the historical behavior sequence; determining a characteristic subsequence of the historical behavior sequence; calculating the weight of the characteristic subsequence; and when the entropy rate is lower than a preset first threshold and the weight is larger than a preset second threshold, identifying cheating of the to-be-evaluated equipment or account in operation. The identification method provided by the embodiment of the invention has advantages considering regularity of the historical behavior of the to-be-evaluated equipment or account, excavating the behavior sub-characteristic of the historical behavior sequence which is arranged according to time, realizing consideration to regularity and sequence of the historical behavior sequence, and improving probability of identifying the cheating account.

The invention discloses a quantitative determination method and a quantitative determination device for flow patterns of horizontal gas-liquid two-phase flows. The quantitative determination method comprises the steps of: acquiring two-phase flow liquid film thickness data in various flow patterns; carrying out signal processing analysis on the two-phase flow liquid film thickness data in various flow patterns according to a multiscale entropy (MSE) concept, so as to extract characteristics of different flow patterns; acquiring MSE arrangement distribution characteristic curves according to the extracted characteristics of different flow patterns, and calculating rates of MSE of different flow pattern curves of MSE arrangement distribution diagrams; and establishing a flow pattern database according to the two-phase flow liquid film thickness data in various flow patterns, the MSE arrangement distribution characteristic curves and the rates of MSE, so as to obtain a flow pattern identification quantitative determination form, and achieving the purpose of determination by means of the flow pattern identification quantitative determination form. The quantitative determination method can process two-phase flow liquid film thickness signals by adopting the MSE algorithm, achieve the purpose of flow pattern identification through extracting characteristic quantities, improve the applicability and reliability of determination, and improve the accuracy of determination.

The invention relates to the technical field of network security, and relates to a method and system for detecting an interest packet flooding attack in a content center network. The method comprisesthe following steps: counting a probability distribution model of a content name prefix of an interest packet; calculating an information entropy of a router according to a statistical value; and comparing the information entropy with a given threshold, and then calculating an entropy rate. In the interest packet flooding attack detection scheme designed by the invention, the difference between the entropy rate change of the legal burst stream and the attack stream is considered, the outburst stream of a legitimate user and an attack stream of an attacker are effectively distinguished on the premise of ensuring that the attack is detected as soon as possible, thereby effectively reducing the misjudgment of the legitimate user.

The invention discloses a color image forensic method based on quaternion polar harmonic transform; the method comprises the following steps: carrying out adaptive segmentation based on entropy rate super pixel for a detected color image, combining a probability density SURF operator so as to extract feature points, and determining a local suspected tampered area; sliding window on the suspected area, using a quaternion polar harmonic transform coefficient moment value to express each slide window block feature, and carrying out similarity matching; finally determining and tagging the tampered area. According to tests results and compared with a conventional tamper detection method based on blocking, the color image forensic method can ensure a high detection correct rate, thus greatly reducing the algorithmic time complexity.

The invention provides a method and device for identifying machine assisted cheating. The method includes the following steps that: a time interval sequence is generated according to the operation time of an assessed device or account; the time interval sequence is discretized, so that a discrete time interval sequence can be formed; the entropy rate of the discrete time interval sequence is calculated; and when the entropy rate is smaller than a preset threshold value, it is identified that the assessed device or account adopts machine assisted cheating in operation. According to the method and device of the invention, the entropy rate of the discrete time interval sequence is calculated, so that rhythmic operation and arrhythmic operation can be accurately distinguished, and therefore, the machine assisted cheating can be accurately identified.

The invention discloses a method for enhancing a low-illumination image. An entropy rate based superpixel segmentation algorithm is applied to a dark channel prior model, and an enhanced image meeting the visual effect requirement of human eyes better is obtained. The method comprises steps as follows: the low-illumination image is subjected to superpixel segmentation firstly and then is inversed, a more accurate transmission transmittance image is obtained by the dark channel prior model according to a homogenous block area obtained through superpixel segmentation, the image is enhanced according to an atmospheric scattering physical model, finally, the image is inversed again, and an enhanced effect image is obtained. The method can be used for processing the low-illumination image adaptively, so that the enhanced image has more ideal contrast and visual effect, and the overall enhancement effect is substantially improved when compared with that of an existing enhancement algorithm. The automation and self-energy of the whole process can be realized, and manual intervention is not needed.

The invention discloses a color image segmenting method based on pixel probability density classification; the method comprises the following steps: firstly using an entropy rate super pixel forming method to segment an original color image, thus obtaining a super pixel image; secondarily calculating a super pixel energy feature and an entropy value feature based on pixel probability density gradient; then using two dimensional entropy to primarily segment the formed super pixel image; finally using a classifier to segment the image. Test results show that the super pixel is introduced, so the color component correlation is considered when calculating the super pixel features, and the classifier with high performance is employed for segmentation, thus greatly improving image segmentation precision and speed.

The invention discloses a user online activity explosion time predictability calculation method and system. The method includes the steps: extracting an explosive shower structure from a historic online activity time record of a user; acquiring a user explosion time interval sequence by the explosive shower structure; discretizing the explosion time interval sequence of the user to obtain an explosion time interval notation sequence; calculating the entropy rate of the explosion time interval notation sequence of the user; calculating user online activity explosion time predictability according to the entropy rate of the explosion time interval notation sequence of the user. The user online activity explosion time predictability calculation method can effectively predict a next online activity behavior of the user, and can help a developer to more effectively design and improve an online service platform.

The invention belongs to the technical field of information security and true random number generators, and particularly relates to a test method for a maximum entropy rate of a true random number generator. The test method for the maximum entropy rate of the true random number generator comprises the four steps of parameter selection, data collection, block segmentation calculation and analysis fitting. By utilizing a characteristic that an average entropy rate obtained by calculation after block segmentation of a random data stream is saturated along with the reduction of the data block length and the increment of a data rate, a relational curve of a saturation entropy rate and a data block length is subjected to linear fitting; a function value corresponding to a fitting straight line when the data block length is equal to 1 is namely the maximum entropy rate; and the problem that the maximum entropy rate in a test of the true random number generator cannot be uniquely determined can be effectively solved.

The invention provides a hyperspectral anomaly detection method for component projection optimization separation. The hyperspectral anomaly detection method comprises the following steps: performing superpixel segmentation on a hyperspectral image by using an entropy rate-based superpixel segmentation algorithm; calculating an average value of pixel points contained in each superpixel to serve asa spectral vector of the superpixel; calculating a Mahalanobis distance from a pixel point contained in each super-pixel to the spectral vector, and summing discrete values representing the super-pixel; solving local abnormal factors one by one by taking the superpixel as a unit; calculating the product of the discrete value of each super-pixel and the reciprocal of the local abnormal factor of each super-pixel, and selecting a part of super-pixels with a smaller product as a background set to construct an estimated background set; setting a component projection and separation optimization filtering function, and solving an optimal filtering vector; multiplying the optimal solution by the hyperspectral image pixel by pixel to obtain a detection result. Image information is interpreted froma super-pixel level, an estimated background set is obtained by using local abnormal factors, and a hyperspectral image abnormal target detection result is obtained by combining optimized filtering.

The invention relates to an entropy rate-based electrochemical noise corrosion signal processing method, which comprises the following steps: cleansing original data; performing direct current removal on data of each small file by using a wavelet removal method to obtain time data; signifying the time data; calculating probability distribution and a transfer matrix; calculating to obtain an entropy rate value; analyzing the size relation among entropy rates of three corrosion systems; according to a sample, verifying the entropy rate value; and evaluating the entropy rate processing results. In the entropy rate-based electrochemical noise Corrosion signal processing method provided by the invention, each entropy rate is used as a new characteristic value for characterizing an electrochemical corrosion system to represent the degree of chaos of the corrosion system; the data processing results show that each entropy rate can well distinguish among different corrosion systems, and cannot be affected by the concentration of a solution; and finally, an entropy rate parameter and a noise resistance performance parameter are compared to obtain the advantages and the disadvantages of the use of the entropy rate.

The invention relates to a large-breadth remote sensing image building extraction method used for a hybrid scene. The large-breadth remote sensing image building extraction method comprises the following steps: 1: preprocessing a remote sensing image; 2: carrying out superpixel segmentation based on an entropy rate; 3: extracting three characteristics including shadows, homogeneity and strong edges; 4: generating a shade layer, a homogeneity layer and a strong edge layer; 5: extracting buildings independently on different layers; and 6: carrying out building superposition. The large-breadth remote sensing image building extraction method has the beneficial effects that more characteristics and semantic information of deeper levels are extracted, and the large-breadth remote sensing image building extraction method can adapt to building extraction in a larger geographic area, and is used for realizing relevant businesses including basic geographic database update and the like.