195 results about "Initial Seed" patented technology

In a method and system for correcting redeye in a digital image of a human, a defect pair separation is measured. The members of the defect pair are each redeye defects in the image. Each defect has one or more pixels. The defects are adjusted, that is, retained unchanged or reduced in size to provide adjusted defects. The adjustment can follow growth of initial seed defects. The adjustment is responsive to the defect pair separation and can include trimming of pixels beyond a size limit calculated using the defect pair separation. Following the adjustment, the color of the adjusted defects is changed to reduce redeye.

A dynamic computer communication security encryption method or system using an initial seed key and multiple random number generators of a specific design, whereby a sequence of independent random entropy values is produced by one set of random number generators and encrypted along with the message stream using the initial seed key, or the output of a second set of random number generators initialized with the initial seed key, and following the subsequent transmission of the variable encrypted entropy/message block, the entropy values are used to symmetrically or identically augment or increase the current uncertainty or entropy of the cryptosystem at both the sender and the receiver, prior to the next encryption block operation. The encryption process effectively entailing the use of multiple encryption ciphers, and the entropy augmentation process entailing the encryption or application of various logical mathematical operations on the already dynamic but deterministic internal state values of the second set of random number generators, effectively altering their deterministic outputs in a random probabilistic manner.

Random length message value sequences from one or more data sources is combined with one or more random length entropy value sequences from an independent source, following which the entropy “updates” may also be used to alter, or change any cryptosystem variable, value or component in a randomly determined manner. In addition, whilst ensuring synchronization, the random entropy sequences also serve to “pollute” the cipher-stream and thereby hinder most current forms of cryptanalysis, whilst simultaneously injecting additional entropy into the cryptographic system and allowing for its propagation to affect any connected system nodes, and thereby introducing unpredictable entropy into the system pseudorandom number generator outputs, and thereby ensuring the perpetual generation of unpredictable random numbers.

Super-encryption mechanics are independent of the user data, simple, fast and efficient, and can incorporate compression, error correction and asymmetric encryption authentication routines. But most importantly, super-encryption ensures resistance to brute force attacks (not possible to verify if a message was even sent), an ability to exceed “perfect secrecy” requirements, and an improvement on previous super-encipherment design, since overhead can be dramatically reduced from 100% overhead.

Communication links previously established by system nodes with central authorities may be used for secure node authentication and registration, whilst allowing the central authority to broker and synchronize communication channels and providing mutual authentication and other security functions between the system nodes.

The invention discloses an intelligent segmentation method of medical image organization based on three-dimensional reconstruction, which comprises the following steps: 1) inputting DICOM medical image sequence, preprocessing the image and adjusting the window; 2) performing wavelet transformation on that image to reduce the image data to 1/4 of the original image; 3) performing statistic gray histogram to extract initial seed point and growth threshold; 4) carrying out three-dimensional edge detection on that image to obtain an edge contour map; 5) carrying out three-dimensional space regiongrowth by combining gray level and edge information; 6) Calculating the region attributes after segmentation, optimize the seed point and growth threshold. Through statistical histogram, the inventioncan automatically extract initial seed points required for region growth and threshold required for growth criterion, and then carry out three-dimensional region growth combined with edge contour information of an image, and continuously optimize initial seed points and growth threshold through iterative growth, thereby improving segmentation results. At that same time, the invention reduces theimage data to 1/4 of the original image through the wavelet change, thereby shortening the operation time.

The invention provides a novel tea tender shoot segmentation and identification method based on color and region growth, relating to the technical field of image processing. The method comprises the following steps of: firstly, converting the original tea RGB (Red, Green, Blue) color image into an HIS color space, and carrying out initial seed selection according to the parameters of hue H and saturation S therein; then carrying out region growth on a seed region according to the color similarity and region contiguity; carrying out region growth and combination by combining with color distance and edge distance to segment tea tender shoots; then extracting the shape feature parameters of binarized color and tender shoot images; and finally finishing identification through an improved hereditary neural network. Through a segmentation and identification experiment on the tea tender shoots in the tea image, the result shows that the algorithm can well separate the tea tender shoots from the tea image, well store the outline information of the tea tender shoots and obtain very good identification results.

The invention discloses an automatic rib marking method, which comprises: extracting thoracic bones from scan data, wherein the thoracic bones comprise ribs; according to the feature representation of a first rib on a sagittal plane, determining the position of the first rib and a basic sagittal plane; according to the position of the first rib, coding the first rib and other rib regions on the basic sagittal plane to mark the sequence number of the ribs corresponding to the rib regions. The invention also discloses an automatic rib marking device. The method can accurately mark the rib regions according to the feature presentation of the ribs on the sagittal plane; in addition, the method can obtain ideal rib outlines easily, facilitate the initial seed points of the ribs and consequently complete the positioning of the whole ribs accurately.

A method for performing CRC calculations on packets with dynamic headers is disclosed. The header may be changed during transmission across a network. When the header is changed, a CRC associated with the header is recalculated such that a residue of the initial seed value is always obtained. A final CRC covers the entire packet including the header and its header CRC, or just the data portion of the packet. The final CRC remains valid and unchanged during transmission of the packet, allowing an endpoint along the network to confirm the validity of the entire packet. By only changing the CRC associated with the changed portion of the packet (the header CRC), the introduction of errors during transmission of the packet is minimized.

A system and method for treating a least a portion of an initial seed sample to create one or more treated seed samples is provided. In various embodiments, one or more seed treatment apparatus components are in communication with one or more controllers such that seed samples treated by a seed treater may be treated at least in part based on one or more instruction sets that may be associated with one or more research plans. As a result, the method and system are configured to accurately, consistently, and predictably treat seed samples, and to adapt and change the treatment of seed samples based on changing research plans.

The invention discloses a social network influence maximization method based on a community structure. The method comprises the following specific processes that: (1) dividing a community, forming a candidate node set, dividing a network to identify a kernel node and a boundary node in the network, and forming the candidate node set; (2) heuristically selecting nodes, and for each node in the candidate node set, verifying potential influence through a node degree, a community scale, a connected community number and influence weight so as to heuristically select and add the node with the highest influence into a seed set; and (3) executing a greedy algorithm, and utilizing the greedy algorithm to select and add the node with the highest marginal income into the seed set. By use of the method, the function of the community structure in influence spreading is analyzed to further improve accuracy and operation efficiency for mining initial seed nodes, and the problem of social network influence maximization is effectively solved.

The invention provides an automatic division method for pulmonary parenchyma of a CT image. According to the automatic division method, the CT is divided through carrying out a random migration algorithm for two times to obtain the accurate pulmonary parenchyma; in the first time, the random migration algorithm is used for dividing to obtain a similar pulmonary parenchyma mask; and in the second time, the random migration algorithm is used for repairing defects of the periphery of a lung and dividing to obtain an accurate pulmonary parenchyma result. Seed points, which are set by adopting the random migration algorithm to divide, are rapidly and automatically obtained through methods including an Otsu threshold value, mathematical morphology and the like; and manual calibration is not needed so that the working amount and operation time of doctors are greatly reduced. According to the automatic division method provided by the invention, a process of 'selecting the seed points for two times and dividing for two times' is provided and is an automatic dividing process from a coarse size to a fine size; and finally, the dependence on the selection of the initial seed points by a dividing result is reduced, so that the accuracy, integrity, instantaneity and robustness of the dividing result are ensured. The automatic division method provided by the invention is funded by Natural Science Foundation of China (NO: 61375075).

The invention provides an unmanned aerial vehicle airborne LiDAR point cloud filtering method based on adaptive gradient. The method comprises the following steps: obtaining point cloud data in a test region and carrying out filtering on the point cloud data; 2) establishing index for the point cloud data; 3) carrying out calculation and keeping an optimal plane in a grid; 4) selecting an elevation lowest point in a preset window as an initial seed point of a ground point; 5) calculating the distance between the optimal plane and the ground point and gradient from the ground point to the center of the optimal plane, and obtaining a ground point set according to gradient threshold; 6) serving a point in the initial ground point set as a base point, and generating a digital terrain model through an inverse distance weighted interpolation method; and 7) through the newly-generated digital terrain model, obtaining update value of the gradient threshold, repeating the steps 4)-6) and obtaining a final ground point set. The filtering method based on adaptive gradient improves algorithm operation efficiency.

The invention relates to a dark channel experience and minimal image entropy based traffic smog visibility detection method. In an image feature extraction module, dark channel experience processing is carried out on an image I to be detected to obtain a rough estimated value of the air transmittance, a guiding and filtering edge smoothing operator is used to smooth and refine the rough estimated value of the transmittance, and depth information of each pixel point is obtained; in a road area extraction module, a road area is extracted from the image I in an area growth method, and area growth comprises the steps including setting an initial seed point, setting a target growth area, calculating the minimum of adjacent gray scale difference, determining whether a target pixel belongs to the road area and updating the seed point; and in a visibility estimation module, the minimal image entropy of the area is calculated, an optimal value of the extinction coefficient is obtained, and the smog visibility is estimated effectively. The detection method has the advantages that a target interest area extraction step is added to an image entropy solving process, the computation amount is reduced, and the operation speed and robustness are improved.

The invention provides a weak supervised image semantic segmentation method based on iterative mining common features of objects, belonging to the technical field of pattern recognition. In the training phase, the method acquires the training data set, constructs and trains the multi-label classification network, and acquires the initial seed area corresponding to each training image. Then, the superpixel region and the region label of each training image are obtained for training the region classification network, and the updated region label of the superpixel region is obtained for trainingthe semantic segmentation network. After iteration, when the performance of the semantic segmentation network converges, the trained semantic segmentation network is obtained. In the use stage, the color image is input into the trained semantic segmentation network, and the network outputs the semantic segmentation results of the image. The invention can realize reliable pixel-level semantic segmentation under the condition of only image class label, reduces the time and labor cost of data labeling, and has wide application prospect.

A method and deterministic random bit generator system operating in accordance with the method, for generating cryptographic keys and similar secret cryptographic inputs which are hard to guess. A entropy is input from an entropy source; and an initial seed is generated as a function of the entropy input. When a request to generate a cryptographic key is received a temporary seed is generated by applying the function to the seed. The requested cryptographic key is generated by applying a second function to the temporary seed; and output. A new seed is then generated by applying a third function to the temporary seed. In one embodiment of the subject invention all three functions are carried out by applying the same operator to appropriate inputs. In another embodiment of the subject invention new entropy is incorporated into the seed from time to time.

The present application is directed towards systems and methods for generating and maintaining cookie consistency for security protection across a plurality of cores in a multi-core system. A packet processing engine executing on one core designated as a primary packet processing engine generates and maintains a global random seed. The global random seed may be used as an initial seed for creation of cookie signatures by each of a plurality of packet processing engines executing on a plurality of cores of the multi-core system using a deterministic pseudo-random number generation function such that each core creates an identical set of cookie signatures.

The process of System Reconstruction is used to integrate sequence data, clinical data, experimental data, and literature into functional models of disease pathways. System Reconstruction models serve as informational skeletons for integrating various types of high-throughput data. The present invention provides the first metabolic reconstruction study of a eukaryotic organism based solely on expressed sequence tag (EST) data. System Reconstruction also provides a method for the identification of novel therapeutic targets and biomarkers using network analysis. The initial seed networks are built from the lists of novel targets for diseases with the high-throughput experimental data being superimposed on the seed networks to identify specific targets.

The invention provides a network security protocol fuzz test method and system based on an LSTM. The method comprises the steps of: S1, carrying out an initial fuzz test on a target network security protocol by adopting an initial test case set; S2, carrying out vulnerability analysis on the target network security protocol according to a new execution path generated in the fuzz test process, andusing the new execution path as an initial seed file set; S3, using the initial seed file set as training data, and carrying out training by utilizing a deep neural network learner to obtain an LSTM model; and S4, generating a new test case set by utilizing the LSTM model so as to carry out the fuzz test on the target network security protocol. The system comprises a first fuzz test module, a vulnerability analysis module, an LSTM model generation module and a second fuzz test module. According to the invention, by training the LSTM neural network model to optimize a generation algorithm of test cases, so that a code coverage rate of the test cases is improved.

The invention provides an automatic batch extraction method for horizontal vector contours of buildings in satellite images. The method comprises the steps of firstly using a K-means algorithm to class the images to obtain the backbone parts of the buildings, and solving the problem of selection of initial seed points by taking mass centers of all building areas; after the areas of all the seed points are grown, separating the building areas from surrounding areas by virtue of image edge information and removing non-building areas according to characteristic parameters such as rectangularity and strip index to realize the automatic extraction of the horizontal pixel contours of the buildings; then using techniques such has Hough transformation and block processing to perform linear vector processing to the horizontal pixel contours, and finally obtaining the horizontal vector contours of all buildings in a batch. The automatic batch extraction method for the horizontal vector contours of the buildings in the satellite images is applicable to the batch and quick extraction of the horizontal vector contours of common polygonal buildings with top views which are of straight-line segment structures in the satellite images.

An apparatus and method for treating a least a portion of one or more initial seed samples to create one or more treated seed samples for use in research applications is provided. In various embodiments, the apparatus and method utilize two or more seed treaters operating in parallel to increase throughput for treating a portion of one or more initial seed samples. The apparatus and method may also include a metering station configured to distribute at least a portion of the one or more initial seed samples to the two or more seed treaters. As a result, the apparatus and method are configured to efficiently and accurately treat seed samples.

A multichannel pulse coupling neural network based color image segmentation technology comprises the steps of step (1), inputting images to be segmented; step (2), using color vectors of all pixels of the images as input vectors of one input neuron and eight adjacent pixel color vectors as radial basis function (RBF) characteristic vectors, and determining initial seed points through seed selection conditions; step (3), growing the seed region through growing rules, classifying the pixel points in accordance with the growing rules in the seed region, and connecting the neurons and grouping and numbering the neurons; step (4), calculating the average characteristic vector of all connection regions, and replacing the characteristic vectors included in all neurons of the region with the obtained characteristic vector; step (5), connecting the qualified unconnected neurons with the proximate groups through a rapid connection rule; step (6),updating the preset threshold to be theta i = theta i1 +delta theta i, and repeating the step (5); step (7), performing rule merging on accordant regions in the images and merging proximate region blocks on space; repeating the step (7) till the region merging stopping conditions are met to complete color image segmentation.