809 results about "Image retrieval" patented technology

The invention relates to the technical field of agricultural knowledge base retrieval, and discloses an intelligent retrieval system for a knowledge base, which comprises an image preprocessing unit, an image adjusting unit, an image matching unit and a retrieval judgment unit, and is characterized in that a binary mask matrix is generated through image enhancement and pixel clustering, a modular entropy value is calculated in combination with parameters, and a basis is provided for feature extraction; multi-scale feature self-adaptive extraction is achieved through dynamic scale adjustment, feature distortion is avoided, feature points are evenly distributed through density dynamic adjustment and quality evaluation, high-quality feature points are screened, double screening of matching pairs is achieved by constructing a comprehensive evaluation mode, and the defects of an SIFT algorithm are overcome by dynamically adjusting a similarity threshold value and sorting. And the accuracy of image retrieval in a complex scene is improved.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

The invention provides an intelligent retrieval matching method and system for multidimensional data of image-text features, and relates to the technical field of icon image retrieval. Comprising the following steps: extracting image features, text content and semantic features of an icon image by using a convolutional neural network, an image segmentation attention mechanism network, a converter optical character recognition model and a bidirectional semantic understanding model, constructing the extracted features into heterogeneous feature tensors, and performing singular value decomposition to obtain icon feature fingerprint vectors; and constructing a multi-level index based on locality sensitive hashing, realizing rapid retrieval, calculating visual, text and semantic similarities in combination with a deep metric learning model, weighting according to variances and discrimination coefficients of similarity features to obtain a comprehensive similarity score, and outputting a retrieval result with the highest similarity.

The invention discloses an image report generation method, system and device based on multi-modal learning optimization, and the method comprises an image retrieval module which is used for carrying out the image feature extraction of an input medical image, and obtaining similar medical images and reports; the simulation generation module is used for guiding an image report generation model to generate an initial draft report by utilizing the medical image and the report; the personalized diagnosis module is used for carrying out personalized adjustment on the report generation model according to the historical report preference of the doctor or the historical medical data of the patient; the reinforcement learning optimization module is used for dynamically adjusting a text generation mode based on a reinforcement learning report optimization strategy and optimizing an image report generation model in combination with doctor scoring, automatic evaluation and clinical feedback; and the iterative refining module is used for performing text retrieval by using the initial draft report as a query, acquiring text fragments which are semantically similar to the initial draft report, and performing neural symbol reasoning optimization to generate a final report.

Techniques are generally described for query evaluation for image retrieval and image generation. In various examples, a first encoded representation of first natural language input data may be generated. An image retrieval process may be selected from among the image retrieval process and an image generation process based at least in part on the first encoded representation of the first natural language input data. A second natural language encoder may generate a second encoded representation of the first natural language input data. The second encoded representation may be used to determine first image data stored in a first data repository. The first image data may be sent for output on a display of a first computing device.

The invention relates to an enhanced LLM-RAG multi-hop question and answer method based on logic tree reasoning, and belongs to the technical field of new-generation information, and the method comprises the following steps: inputting a multi-hop question and answer question into a computer system; the computer system calls a pre-training large language model LLM, the multi-hop question-answer question is decomposed into a hierarchical logic tree in a recursive mode, and each node of the logic tree comprises a sub-question and a corresponding hypothesis answer; performing image retrieval from a structured knowledge source Wikidata and performing text retrieval from an unstructured knowledge source Wikipedia on the basis of each node sub-question and the hypothesis answer to obtain corresponding evidence; traversing the logic tree, verifying the consistency between the hypothetical answer of each node and the evidence through LLM, if the contradiction exists, reconstructing the corresponding sub-tree, and dynamically correcting the reasoning path; and integrating the verified logic tree node information, and outputting an accurate answer to the multi-hop question and answer question.

The invention provides a similar picture retrieval method, which comprises the following steps of: constructing a hierarchical semantic-visual representation framework, and decomposing query and images into three levels, namely a core concept, attribute modification and relation description; a context-aware professional term understanding system is achieved, the professional field background and purpose of query are analyzed, context feature vectors are generated, and term ambiguity is eliminated; establishing a semantic-vision bidirectional mapping and optimization framework, realizing forward mapping from semantics to vision and reverse mapping from vision to semantics, and generating an optimization mapping matrix and a candidate set through iterative optimization; and performing multi-modal similarity calculation, converting the context feature vector into a hierarchical weight vector, fusing the hierarchical weight vector with the multi-level similarity into a comprehensive similarity score, and sorting and outputting a retrieval result set according to the comprehensive similarity score. According to the method, the accuracy of similar picture retrieval in the professional field is improved.

The invention provides a dynamic game difficulty self-adaptive adjustment method and system based on user behavior feedback, and relates to the technical field of game design, the method comprises the following steps: carrying out dynamic weighting calculation based on a difficulty adaptation index, generating a dynamic difficulty correction coefficient in combination with user real-time physiological feedback data, and adjusting the dynamic game difficulty according to the dynamic difficulty correction coefficient; the dynamic difficulty correction coefficient comprises a scene complexity adjustment parameter and an interaction response threshold adjustment parameter; inputting the user feedback parameter set into an image feature weight distributor, dynamically adjusting weight distribution of image retrieval feature vectors according to user attention distribution data and operation delay parameters, and generating an optimized scene element retrieval strategy; and performing feature space mapping on the dynamic difficulty correction coefficient and the optimized scene element retrieval strategy, and generating a final game difficulty control instruction set through nonlinear superposition. Game design can be optimized, and game adaptability and flexibility are enhanced.

The invention discloses a graph-driven intelligent medical image retrieval method and system, and relates to the field of image retrieval, and the method comprises the steps: firstly, coding a text query input by a user through constructing a medical knowledge graph, querying to generate a graph-driven enhanced embedded vector containing an advanced medical concept, and querying a related graph concept set; then, performing preliminary vector similarity retrieval by using the enhanced vector, and quickly screening out Top-K preliminary candidate images; and finally, on the basis of querying a related map concept set, carrying out map concept secondary sorting on the preliminary candidate images, and accurately matching deep medical semantics of the images by calculating a concept coincidence degree. The mechanism of two-stage retrieval and map deep fusion ensures that the retrieval result is not only similar in vision, but also highly related in medical concept, so that the accuracy and intelligent level of medical image retrieval are remarkably improved.

The invention relates to a construction safety risk early warning method and system based on cross-modal visual language retrieval, belongs to the technical field of combined image retrieval and multi-modal retrieval, and designs a multi-granularity image-text semantic feature decoupling module, a multi-granularity feature combination module and a multi-granularity combination-target alignment module. Through a multi-granularity semantic decoupling technology, local features and global features of an image and a text are decoupled respectively, alignment between multi-modal query and a target image is considered at the same time, multi-granularity combination is carried out on the decoupling features of the image and the text in the multi-modal query, and multi-granularity combination features of the multi-modal query are obtained. According to the method, combined features are pushed to approach target image features from a multi-granularity level, cross-modal alignment of a multi-granularity detail level is realized, semantic information between multi-modal query and a target image is aligned more accurately, and overall cross-modal understanding and application effects are improved.

A method, computer program product, and computing system for generating a plurality of chunks for a plurality of text portions of a document, wherein the document includes the plurality of text portions and a plurality of images. Each chunk is indexed using a word embedding. Each of the plurality of images is indexed based upon, at least in part, a position of a respective image relative to a corresponding chunk. An image placeholder is generated for each of the plurality of images. A plurality of image-enhanced embeddings is generated by inserting the image placeholder for each of the plurality of images into a respective word embedding for the corresponding chunk. The plurality of image-enhanced embeddings are provided for processing a query using a generative artificial intelligence (AI) model.

The invention relates to the technical field of image retrieval, in particular to an image retrieval method based on big data mining, which comprises the following steps: collecting images and metadata, screening metadata fields, determining a candidate image set and obtaining a metadata screening result, according to the method, in the image retrieval process, image metadata screening, geographic information clustering, visual feature fusion, index construction and matching strategy optimization are combined, and the retrieval accuracy and response efficiency are improved. In the metadata screening link, data is screened by using information such as time, places and equipment types, so that interference of irrelevant data is reduced, and a candidate image set is more targeted. Geographic information and timestamps are combined for classification, and clustering analysis is performed by utilizing a geographic information system, so that spatial distribution information plays a greater role in a retrieval process, and a more accurate result can be obtained for query of a specific region.

The invention relates to the technical field of image retrieval, and discloses a multi-modal image retrieval method, system and device, a medium and a program product, the method comprises the following steps: obtaining a to-be-retrieved image and / or character description information of the image, inputting the to-be-retrieved image and / or the character description information of the image into a pre-constructed multi-modal image retrieval model, and taking a feature vector output by the model as a query vector; performing similarity calculation on the query vector and a feature vector corresponding to each image in a preset image library, and taking the image of which the similarity is greater than a preset threshold as a matched image; and displaying the matched images as retrieval results according to the sequence of the similarity from high to low. The multi-mode image retrieval method can provide retrieval modes of searching images by text, searching images by images and searching images by image-text mixing, realizes multi-mode image retrieval capability, provides ideas of attribute learning and sorting learning, realizes image retrieval of information of other dimensions except categories, adapts to the characteristic of complexity of an image retrieval task, and improves the image retrieval efficiency. And the convenience, accuracy and robustness of image retrieval are improved.

The invention relates to the technical field of trademark image retrieval, and discloses a trademark image retrieval method and system based on deep learning, and the method comprises the steps: obtaining a trademark image; scoring the trademark image to obtain image complexity; according to the trademark image and the image complexity, wavelet transformation is carried out to obtain a feature vector, dynamic adjustment is carried out, and feature representation is obtained; according to the feature representation, inputting the feature representation into a preset semantic association model to obtain candidate association information; according to the candidate association information, calculating the similarity between the image semantic vector and the brand node by using a knowledge graph reasoning technology, and obtaining an association result based on a similarity threshold value; dynamically generating a text label of the trademark image according to an association result, and mapping the text label and an entity in the brand knowledge base by adopting an entity linking technology to obtain a mapping result; and inputting the mapping result into a preset semantic link network to obtain a trademark retrieval result. According to the method, the accuracy of trademark image retrieval can be improved.

The invention provides an image retrieval method and system supporting sharing of multiple data sources, and relates to the technical field of image retrieval, the method comprises the steps that secret keys are generated and distributed for retrieval members through a trusted third party, a data owner constructs a ciphertext cluster and a ciphertext image set based on a public key in an index secret key pair, an image secret key and an image set, and the ciphertext cluster and the ciphertext image set are stored; uploading to a first cloud server; the cloud server group re-encrypts the ciphertext cluster and constructs a hierarchical index; the query user processes the query image by using a public key in the retrieval key pair to obtain a query trap door, and uploads the query trap door to the first cloud server; the cloud server group re-encrypts the query trap door to obtain a new query trap door, and searches the hierarchical index based on the new query trap door to obtain a search result; and the query user obtains the image under the plaintext based on the search result. According to the method, the security of privacy information during retrieval is ensured, the complete secret culture of calculation in the retrieval process is realized, and the scene of multi-data source sharing is supported.

The invention relates to the technical field of image processing, in particular to an image search precision optimization method and system based on the combination of rough arrangement and fine arrangement, and the method comprises the steps: a rough arrangement model stage: calculating the rough arrangement similarity of a query image and all images in a database, and sorting and screening out a preset number of candidate images according to the rough arrangement similarity; a model fine arrangement stage: performing multi-scale reconstruction on the query image to generate reconstructed images with different resolutions; extracting feature vectors of the reconstructed images through a fine arrangement model, performing expansion operation on the feature vectors, and calculating fine arrangement similarity between the query image and the candidate image; and a similarity weighting combination stage: performing normalization processing on the rough ranking similarity and the fine ranking similarity, dynamically adjusting the weight coefficients of the rough ranking similarity and the fine ranking similarity according to the difference of the rough ranking similarity, generating a final similarity through weighted average, and finally sequencing and outputting a retrieval result. And by dynamically combining the distance of rough arrangement and the distance of fine arrangement, the image retrieval precision is further improved.

The invention provides a cross-modal joint contrast learning method and device and electronic equipment, and the method comprises the steps: constructing a sample data set which covers a plurality of task types, such as a text retrieval image, an image retrieval text, a text retrieval text, an image retrieval image, an image-text joint retrieval image and an image-text joint retrieval text; and generating prompt words for identifying task types for each task sample, splicing the prompt words with sample data to form task input, and determining modal types of a retrieval object and a retrieval target. A retrieval object and a retrieval target are respectively input into encoders of corresponding modes to extract features, the features are mapped to the same semantic space through a unified projection layer to obtain an embedded vector, and the parameters of the encoders and the projection layer are optimized by utilizing a contrast learning loss function based on the similarity of the retrieval object and the retrieval target, so that multi-task unified training is realized. Various cross-modal retrieval tasks can be supported in a unified semantic space at the same time, and the overall retrieval effect is improved on the premise of ensuring multi-task performance balance.

The invention discloses a cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning, and the method specifically comprises the following steps: introducing a difficult sample feature mining method, dynamically adjusting the loss weight of a sample according to the change of similarity, and building a dynamic difficult sample triple loss model; the method comprises the following steps: dynamically adjusting a confidence threshold value of a sample by adopting an index moving average weighting method, iteratively training and screening an unlabeled sample, namely a pseudo label, establishing a pseudo label self-training mechanism of a dynamic threshold value, mining and utilizing non-paired data, and solving the problem of high manual labeling cost; a reference image most similar to a query image is found through image retrieval, and the offset of a query position is predicted. Experiments on CVUSA and CVACT data sets show that as the distance threshold increases, the accuracy of the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning presents a stable rising trend, and the cross-view image geographic positioning method based on dynamic threshold pseudo tag self-training learning is superior to other methods under the same threshold condition.

The invention discloses a photoelectric pod image stabilization and flyback integrated measuring device and method, and relates to the technical field of aircraft photoelectric pods. Comprising the following steps: acquiring a rotation angle of a photoelectric pod in a working state within a time interval, and calculating a rotation angular velocity of the photoelectric pod within the time interval; and an active stability control model is established, and when the rotation angular speed of the photoelectric pod within the time interval is smaller than a set value, the position of the optical axis of the binocular camera is adjusted through an inertia original element of the photoelectric pod so as to capture a target object. According to the method, the active stability control model is established, multiple time intervals are divided, synchronous frequency rotation is carried out on the binocular camera in the opposite direction while the aircraft or the photoelectric pod rotates, it is ensured that the target object can be captured by the binocular camera in each time interval, and the purpose of rapidly capturing the target object is achieved; the tracking precision of the aircraft is improved, and the aircraft can effectively track the target.

Provided in the embodiments of the present description are a design patent image retrieval method and apparatus, a device and a medium. The method comprises: constructing a data set consisting of data pairs consisting of design images and text labels representing attributes of the design images; inputting the data set into an initial Clip model for training same; then inputting the data set into the model to generate first feature vectors and second feature vectors corresponding to text features, and on the basis of a preset vector weighting proportion, calculating respective fusion feature vectors; clustering the fusion feature vectors on the basis of a clustering algorithm to obtain sub-data sets, the sub-data sets storing fusion feature vectors, or the sub-data sets storing the first feature vectors of image features; and inputting an image to be retrieved into an image-text encoder of the trained Clip model to obtain an image feature vector, so as to obtain a retrieval image result. The present invention solves the problems of inaccurate categories of retrieval and time consumption of retrieval.

The invention discloses a cross-modal remote sensing image retrieval method and device, electronic equipment and a storage medium, and belongs to the technical field of remote sensing image processing.The method comprises the steps that a remote sensing image data set is obtained; constructing a retrieval model; inputting the optical image data set into a retrieval model to obtain optical target domain fusion features; inputting the radar image data set into a retrieval model to obtain radar target domain fusion features; performing semantic alignment on the optical target domain fusion features and the radar target domain fusion features according to an alignment module to obtain aligned radar target domain fusion features; calculating a loss value between the optical hash code and the radar hash code according to a loss function module, and determining the retrieval model with the minimum loss value as a target retrieval model; and performing retrieval based on the target retrieval model. And cross-modal processing is performed on the image through the cross-modal image generation module, and the features of the two modals are fused to realize retrieval based on the fused features, so that cross-modal remote sensing image retrieval is realized.

The invention discloses a video geographic positioning method and device based on iron tower monitoring, and the method comprises the steps: constructing a monitoring video homonymy point sample library, collecting the homonymy point data of a monitoring video and a remote sensing orthoimage, and storing a reference image; establishing an initial calibration mapping relation, and realizing coordinate transformation of the remote sensing image and the video image through projection transformation; matching a reference image closest to the real-time video frame from a reference image library through image retrieval; dense matching of inclined images is carried out by using a deep learning model, and automatic alignment of feature points is realized; and resolving a video image coordinate to a geographic coordinate through homography transformation and an initial calibration model to complete high-precision positioning. According to the method, the bidirectional conversion function of finding the land by the video and finding the video by the land is supported, the application efficiency of iron tower monitoring in the fields of cultivated land protection and the like is effectively improved, a good positioning effect is still achieved for complex scenes such as plains, mountainous regions and the like, and corresponding technical support is provided for wide application of iron tower video monitoring.

The invention discloses a fine-grained costume image retrieval method and a fine-grained costume image retrieval device based on large language model common knowledge injection. According to the method, firstly, fine-grained visual features of an input image are extracted through an image encoder, and optimization is carried out in combination with a low-rank adapter, so that the representation capability of an image patch level is improved; thirdly, generating attribute-enhanced common knowledge context through a pre-trained large language model, and enriching image attribute representation, thereby helping the model to understand and infer unknown attribute information in an open scene; according to the method, a switchable mode prompt and interpolation mechanism is introduced, and it is guaranteed that agent embedding can be dynamically supplemented when attributes or texts are missing. In the retrieval process, fine-grained image content matching is carried out based on the relation between image features and attribute enhancement contexts through an attribute-guided cross-modal attention mechanism. According to the method, through multi-modal feature alignment and optimization, the accuracy and robustness of clothing image retrieval in an open world scene are improved.

The invention discloses a zero-sample cross-modal image retrieval method and device based on low-rank adaptation. The method comprises the following steps: inputting a to-be-retrieved first modal image into a retrieval model to obtain an output vector; and comparing the output vectors with predetermined output vectors of various types of second modal image test samples, and screening out low-difference second modal image test samples to form a retrieval set. The retrieval model is obtained by replacing a corresponding layer in a transformer block of a large model DINOv2 with a domain-independent and domain-related module by using a preset low-rank technology so as to increase parallel low-rank matrix branches, and freezing original network parameter gradient training and verification; according to the method, for similar points and unique points of two different modalities, domain-independent and domain-related modules are designed to replace partial layers in DINOv2, so that the low-rank technology can better adapt to cross-modal zero sample image retrieval tasks, and the model robustness is improved. During training, original parameters are frozen, only the low-rank matrix is optimized, and better accuracy can be provided while training parameters are reduced.

The present disclosure provides a method, a device, and a product for retrieval. The method includes acquiring context information related to an image and determining a representation of the image based on image data and the context information of the image, where the context information includes at least one of environment parameters, user behavior data, time elements, or field metadata. The method further includes encoding the representation as an image vector in a high-dimensional vector space and storing it into an image vector database. When retrieval is performed, a query that includes text information and that is for the image vector database is received, and an image associated with the text information is determined from the image vector database. The method according to the present disclosure can improve accuracy and efficiency for image retrieval.

The invention provides a deep hash image retrieval method based on hierarchical multi-scale feature fusion. The method comprises the following steps: firstly, adopting a Nested Hierarchical Transform as a backbone feature extraction network, and synchronously capturing global context information and local detail features of an image through a nested local self-attention mechanism and a hierarchical feature aggregation module; then, constructing a multi-scale feature fusion module, extracting visual features of different receptive fields in parallel by using a multi-branch unequal-ratio expansion rate dilated convolution kernel, and adaptively weighting important features in combination with attention to generate discriminative multi-scale fusion features; and finally, designing a mixed loss function, optimizing intra-class feature compactness through center similarity loss, and realizing efficient and semantic similarity keeping Hash code generation in combination with a quantization loss constraint Hash code discretization process. According to the method, full expression of image features is realized through a collaborative mechanism of Transform and multi-scale feature fusion, and the precision and efficiency of large-scale image retrieval are improved through an end-to-end deep hash learning framework.

The invention discloses a complex text image retrieval method and system based on a multi-modal large model thinking chain. The method comprises the following steps: S1, carrying out adaptive semantic disassembly; s2, reconstruction and optimization are carried out; s3, constructing a matching probability matrix; elements of the matching probability matrix represent probability scores of matching degrees between the candidate images and the matching texts; combining the matched text with the candidate image, and sending the combined image to a pre-training visual language model pair by pair to obtain a corresponding matching score; then a binary discriminant scoring mechanism is used to convert the matching score into a probability score that the cue word is' YES '; and S4, according to the matching probability matrix, calculating the matching degree between each candidate image and the original proposition, and selecting the candidate image with the highest matching degree as an image retrieval result. The invention aims to extract and optimize semantic features from complex text description by using a self-adaptive local deconstruction and global optimization method and a binary discriminant scoring mechanism guided by cue words, so as to improve the accuracy and generalization ability of image retrieval.

The invention relates to the technical field of image retrieval, provides a dual-path unsupervised Hash retrieval method and system based on mutual information maximization, and remarkably improves the characterization capability and retrieval performance of Hash codes through a dual-coding network, triple mutual information optimization, graph convolutional network processing and decoder reconstruction. Firstly, image features are extracted, approximate hash codes and continuous latent variables are generated by adopting a dual-coding network, optimization training is performed through a triple mutual information optimization module, and separability and semantic consistency of the hash codes are enhanced. Then, the Hash codes and the latent variables are further optimized by using the GCN, and the structured expression ability of the Hash codes on data distribution is improved, so that the Hash codes of similar data points are closer. Finally, the optimized latent variables are input into a decoder for reconstruction, high-quality hash codes can still be generated under the unsupervised condition, higher discrimination, compactness and generalization ability are achieved, and the accuracy and efficiency of image retrieval are remarkably improved.

The invention discloses an image retrieval method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is provided with a retrieval system applied to an insurance marketing scene image. According to the method and the device, the to-be-processed image is segmented and coded, the content features of the image are combined with the position information to form high-quality image representation, and the corresponding image index information is generated by utilizing the preset image index generator, so that efficient indexing and retrieval of the image are realized. In the encoding process, local features of the image are reserved, spatial position information is fused, discrimination and uniqueness of image indexing are improved, generated image indexing information and an original image are stored in an image retrieval database in an associated mode, it is ensured that the image retrieval process has high matching precision and response speed, and the image retrieval efficiency is improved. The image retrieval accuracy and processing efficiency are effectively improved, and the method is suitable for large-scale image library management and rapid retrieval.

A cross-view-angle image retrieval method for unmanned aerial vehicle navigation comprises the following steps: step 1, enhancing an image, step 2, based on a ConvNeXt network for cross-view-angle image retrieval for unmanned aerial vehicle navigation, performing multi-scale feature extraction on the enhanced image, and step 3, performing multi-scale feature extraction on the enhanced image based on a ConvNeXt network for cross-view-angle image retrieval for unmanned aerial vehicle navigation. 3, performing multi-dimensional feature fusion based on an attention mechanism; 4, after multi-dimensional feature fusion, calculating a loss value, and based on a determined weight, obtaining a detection model for image retrieval; through the multi-scale feature convolution and multi-dimensional feature fusion technology, the technical problems of insufficient matching precision, poor retrieval robustness and the like caused by view angle, scale and structure differences between the satellite image and the unmanned aerial vehicle image are solved; the invention further comprises a system, equipment and a medium for implementing the method.