Method and system for identifying characteristics of hidden part based on ancient ceramic

Abstract

The invention relates to the technical field of ancient ceramic identification, in particular to an ancient ceramic hidden part feature identification method, which comprises the following steps: S1, database construction: collecting feature trace data of a standard ancient ceramic hidden part, and establishing a genuine product sample database; s2, data acquisition: acquiring hidden part feature trace data of the to-be-identified ancient ceramic through an endoscopic camera device; s3, autonomously comparing: autonomously comparing the characteristic trace data of the to-be-identified ancient ceramic with the genuine product sample database; s4, result judgment: judging the authenticity of the to-be-identified ancient ceramic according to the consistency degree of the comparison result, and establishing a complete classification standard of the hidden part features of the ancient ceramic; a genuine product database of key indexes such as feature traces is constructed, hidden part data of a to-be-identified product is obtained through an endoscopic camera shooting technology, and an identification result is output through an intelligent comparison algorithm; traditional identification experience is converted into scientific indexes capable of being quantitatively analyzed, and the limitation of identification depending on surface features is broken through.

Description

Technical Field

[0001] This invention relates to the field of ancient ceramics identification technology, specifically to a method and system for identification based on the characteristics of hidden parts of ancient ceramics. Background Technology

[0002] Current methods of visual and scientific identification of ancient ceramics are based on surface characteristics, such as craftsmanship, decoration, shape, bubbles, aging marks, chemical composition, and spectrum. However, with the advancement of counterfeiters' technology, all these characteristics can be replicated.

[0003] According to CN118411340A, a method for locating and dating ancient ceramics based on microscopic images is disclosed. This technology discloses "a method for locating and dating ancient ceramics based on microscopic images, including image acquisition: taking pictures of ancient ceramics and using high-resolution microscopic equipment to obtain image data of microscopic features such as the surface, body, glaze, bubble structure, cracks, and repair marks of ancient ceramics. The equipment can be a general tipscope mobile phone microscope, including specific image acquisition standards, lighting conditions, magnification selection, and image preprocessing algorithms." It has the technical effects of "reducing the reliance on senior expert resources, effectively reducing the workload of experts for a large number of ceramics to be identified, especially for the initial screening work, allowing them to focus on more complex cases that require in-depth professional knowledge, rationally allocating human resources, improving work efficiency, and more effectively protecting and utilizing expert knowledge resources, especially when facing a large demand for ancient ceramic identification."

[0004] Existing techniques for authenticating ancient ceramics face three key challenges: First, traditional authentication relies on expert experience to judge surface features (such as glaze color and decoration), but modern high-quality imitations can perfectly replicate these visible features, leading to a high rate of misjudgment. Second, scientific authentication methods (such as component analysis) usually require sampling and testing, causing irreversible damage to precious artifacts and failing to reflect the characteristics of the manufacturing process. Third, existing techniques have blind spots in collecting features from hidden areas inside the artifacts (such as the inner wall of narrow-mouthed bottles and the spout channel). These areas contain crucial authentication information due to the unique characteristics of ancient handcrafting (such as joining techniques and traces of repair tools) and signs of use (such as liquid immersion), but have long been underutilized due to limitations in testing equipment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for identification based on the characteristics of hidden parts of ancient ceramics. By establishing a complete classification standard for the characteristics of hidden parts of ancient ceramics, traditional identification experience is transformed into quantifiable scientific indicators, thus overcoming the limitations of relying on surface features for identification.

[0006] To achieve the above objectives, the present invention provides a method for identification based on the characteristics of concealed parts of ancient ceramics, comprising the following steps:

[0007] S1, Database Construction: Collect characteristic trace data of hidden parts of standard ancient ceramics and establish a database of genuine samples;

[0008] S2, Data Acquisition: Acquire characteristic trace data of hidden parts of the ancient ceramic to be identified using endoscopic camera equipment;

[0009] S3, Independent Comparison: The characteristic trace data of the ancient ceramic to be identified is independently compared with the database of genuine samples.

[0010] S4, Result Judgment: Based on the degree of consistency of the comparison results, determine the authenticity of the ancient ceramic to be identified.

[0011] Preferably, the concealed part in S1 is the area where the image acquisition instrument failed to acquire image data due to the shape structure or the repair process.

[0012] Preferably, the feature traces in S1 include:

[0013] Characteristics of tire joining: unrepaired manual tire joining marks at the inner wall joining point;

[0014] Fingerprint or tool marks formed by pinching with fingers at the inner opening;

[0015] The dripping characteristic is the traces of natural dripping of the glaze or the inner wall of the body;

[0016] Spiral pattern characteristics, traces of different spiral patterns formed on the inner wall due to different eras of craftsmanship;

[0017] Features of use: friction or wetting marks on the inner wall due to actual use.

[0018] Preferably, the genuine sample database in S1 includes:

[0019] Image acquisition range: Multi-angle image acquisition of hidden parts of known genuine ancient ceramics;

[0020] Feature parameter extraction: Based on the acquired image data, feature trace parameters of the concealed parts are extracted, including surface texture morphology, three-dimensional depth distribution and spatial arrangement features of material composition;

[0021] The data association logic associates the extracted feature parameters with the corresponding ancient ceramics' age information, kiln attributes, and production process tags, forming a structured feature database.

[0022] Preferably, the autonomous comparison in S3 is achieved through the following steps:

[0023] A1, Feature standardization processing: Denoising, registration and enhancement processing are performed on the collected image data of concealed parts, and multi-scale feature vectors are extracted;

[0024] A2, Matching Analysis: Employs a deep metric learning algorithm to calculate the similarity measure between the test sample and the database samples in the feature embedding space;

[0025] A3, Decision Output: Generates identification conclusions including confidence assessments based on similarity measurement results, and outputs a visual comparison report of feature matching.

[0026] This invention also discloses an identification system based on the characteristics of concealed parts of ancient ceramics, characterized by comprising:

[0027] The sample acquisition module is used to collect feature data of hidden parts of genuine products through endoscopic camera equipment and store them in the database;

[0028] The autonomous comparison module is used to extract the feature data of the items to be identified and perform intelligent comparison and analysis with the database;

[0029] The user feedback module is used to receive manually corrected data for the identification results and to optimize the database and comparison model.

[0030] Preferably, the sample acquisition module includes:

[0031] Optical acquisition components, equipped with a multispectral endoscopic imaging device with an adaptive focusing mechanism;

[0032] The data processing component is an image processing unit that integrates a 3D point cloud reconstruction algorithm, capable of generating feature models that include surface texture and three-dimensional shape.

[0033] The data storage component features a database architecture with multi-level classification indexes, and establishes a hierarchical tagging system based on chronological period, kiln affiliation, and vessel type.

[0034] Preferably, the sample acquisition module includes:

[0035] The feature analysis unit employs a deep convolutional neural network architecture to automatically identify and vectorize the microscopic features of hidden areas.

[0036] The intelligent matching engine integrates multi-scale feature similarity calculation algorithms and supports cross-sample comparison based on attention mechanisms.

[0037] The results presentation interface provides a visual interactive system that offers feature matching degree heatmaps and 3D comparison views, and generates structured identification reports.

[0038] This invention provides a method and system for identification based on the characteristics of concealed parts of ancient ceramics. Compared with existing technologies, it has the following advantages:

[0039] 1. The focus of identification is shifted to hidden parts inside the object, such as the inner wall of the bottle and the spout channel, which are difficult to observe using traditional techniques. Due to the special manufacturing process and the natural accumulation of marks during use, these parts have formed unique and difficult-to-replicate characteristics, including microscopic features such as joining marks and finger-molding patterns. By establishing a standard database of the characteristics of these hidden parts, a new scientific basis is provided for the identification of ancient ceramics, effectively solving the problem of identifying high-quality imitations.

[0040] 2. By integrating various hidden features such as tire-joining characteristics, finger-molding marks, drip patterns, swirl patterns, and signs of use, a comprehensive evaluation standard was established. Through three-dimensional modeling technology, the morphological distribution of various features was accurately recorded, and combined with the spatial arrangement characteristics of material components, a multi-parameter cross-validation identification method was formed.

[0041] 3. Employing endoscopic imaging technology, a miniature camera is used to penetrate deep into the interior of the artifact, capturing the complete three-dimensional morphology and microscopic texture of hidden areas without damaging the artifact. Combined with multispectral imaging and three-dimensional reconstruction technology, key identification indicators such as splicing characteristics and flow marks can be comprehensively recorded. This not only protects the integrity of the artifact but also greatly improves the comprehensiveness and accuracy of the detection, providing reliable technical support for the scientific identification of precious cultural relics. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method steps of the present invention;

[0043] Figure 2 This is a block diagram of the feature traces in this invention;

[0044] Figure 3 This is a block diagram of the genuine sample database in this invention;

[0045] Figure 4 This is a flowchart illustrating the steps of the autonomous comparison method in this invention;

[0046] Figure 5 This is a system block diagram of the present invention;

[0047] Figure 6 This is a block diagram of the sample acquisition module in this invention;

[0048] Figure 7 This is a block diagram of the autonomous comparison module in this invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 - Figure 7 This invention provides a technical solution: a method for identification based on the characteristics of concealed parts of ancient ceramics, comprising the following steps:

[0051] S1, Database Construction: Collect characteristic trace data of hidden parts of standard ancient ceramics and establish a database of genuine samples;

[0052] S2, Data Acquisition: Acquire characteristic trace data of hidden parts of the ancient ceramic to be identified using endoscopic camera equipment;

[0053] S3, Independent Comparison: The characteristic trace data of the ancient ceramic to be identified is independently compared with the database of genuine samples.

[0054] S4, Result Judgment: Based on the degree of consistency of the comparison results, determine the authenticity of the ancient ceramic to be identified.

[0055] This implementation plan achieves a technological breakthrough in cultural relic authentication by analyzing the characteristics of hidden parts that are difficult to access using traditional methods; it constructs a database of genuine artifacts based on key indicators such as characteristic traces, then uses endoscopic imaging technology to obtain data on hidden parts of the artifacts to be authenticated, and finally outputs the authentication results through intelligent comparison algorithms; it effectively solves the problem of identifying high-quality forgeries by analyzing the unique internal craftsmanship traces; the non-contact detection method ensures the safety of cultural relics; and the visualized authentication report enhances the credibility of the results.

[0056] Specifically, the concealed parts in S1 are the areas where the image acquisition instrument failed to acquire image data due to the device's structure or the finishing process.

[0057] In this embodiment, the concealed parts include, but are not limited to, the inner walls of plum vases, jade pot spring vases, flat bottles, and the inner wall of the spout of wine pots.

[0058] Specifically, the feature traces in S1 include:

[0059] Characteristics of tire joining: unrepaired manual tire joining marks at the inner wall joining point;

[0060] Fingerprint or tool marks formed by pinching with fingers at the inner opening;

[0061] The dripping characteristic is the traces of natural dripping of the glaze or the inner wall of the body;

[0062] Spiral pattern characteristics, traces of different spiral patterns formed on the inner wall due to different eras of craftsmanship;

[0063] Features of use: friction or wetting marks on the inner wall due to actual use.

[0064] In this embodiment, a complete classification standard for the characteristics of hidden parts of ancient ceramics was established, breaking through the limitations of traditional identification that relies on surface features. It transforms traditional identification experience into quantifiable scientific indicators, overcoming the limitations of identification based on surface features. Through comprehensive comparison of multi-dimensional features, the technological characteristics of different periods and production areas can be accurately identified. The non-contact detection method not only protects the safety of cultural relics but also improves the efficiency of identification.

[0065] Specifically, the genuine product sample database in S1 includes:

[0066] Image acquisition range: Multi-angle image acquisition of hidden parts of known genuine ancient ceramics;

[0067] Feature parameter extraction: Based on the acquired image data, feature trace parameters of hidden parts are extracted, including surface texture morphology, three-dimensional depth distribution and spatial arrangement features of material composition;

[0068] The data association logic associates the extracted feature parameters with the corresponding ancient ceramics' age information, kiln attributes, and production process tags, forming a structured feature database.

[0069] In this embodiment, the surface texture morphology includes, but is not limited to, the direction and bifurcation of the joining line and the geometry of the finger indentation; the three-dimensional depth distribution is obtained by using stereoscopic vision or structured light technology to acquire the concave and convex features of the traces; the spatial arrangement of material composition is combined with X-ray fluorescence (XRF) point scanning data to reflect the compositional gradient changes of the glaze flow; by establishing a systematic collection standard for the features of hidden parts of ancient ceramics, the data gap in this field is filled; multi-dimensional feature parameter extraction technology is used to comprehensively record the microscopic features of cultural relics; and the structured storage method realizes the intelligent association between feature data and the background information of cultural relics.

[0070] Specifically, the autonomous comparison in S3 is achieved through the following steps:

[0071] A1, Feature standardization processing: Denoising, registration and enhancement processing are performed on the collected image data of concealed parts, and multi-scale feature vectors are extracted;

[0072] A2, Matching Analysis: Employs a deep metric learning algorithm to calculate the similarity measure between the test sample and the database samples in the feature embedding space;

[0073] A3, Decision Output: Generates identification conclusions including confidence assessments based on similarity measurement results, and outputs a visual comparison report of feature matching.

[0074] In this embodiment, a standardized feature extraction and comparison process was established to ensure the consistency and repeatability of the identification results; advanced deep metric learning technology was adopted to effectively capture subtle feature differences in hidden parts of ancient ceramics; and the output of a visual report made the identification process more transparent and credible.

[0075] This invention also discloses an identification system based on the characteristics of concealed parts of ancient ceramics, comprising:

[0076] The sample acquisition module is used to collect feature data of hidden parts of genuine products through endoscopic camera equipment and store them in the database;

[0077] The autonomous comparison module is used to extract the feature data of the items to be identified and perform intelligent comparison and analysis with the database;

[0078] The user feedback module is used to receive manually corrected data for the identification results and to optimize the database and comparison model.

[0079] In this embodiment, a sample acquisition module uses professional endoscopic imaging equipment to acquire detailed feature data of hidden parts of genuine artifacts and establish a standard database. Subsequently, an autonomous comparison module uses intelligent algorithms to extract features of the items to be identified and performs multi-dimensional comparative analysis with the database. Finally, a user feedback module collects expert correction opinions to continuously optimize system performance. By systematically applying artificial intelligence technology to the field of hidden part identification of ancient ceramics, the limitations of traditional identification methods are broken through. The closed-loop design of data acquisition, intelligent comparison, and manual verification ensures both identification efficiency and result reliability. The modular architecture design enables the system to continuously learn and evolve.

[0080] Specifically, the sample collection module includes:

[0081] Optical acquisition components, equipped with a multispectral endoscopic imaging device with an adaptive focusing mechanism;

[0082] The data processing component is an image processing unit that integrates a 3D point cloud reconstruction algorithm, capable of generating feature models that include surface texture and three-dimensional shape.

[0083] The data storage component features a database architecture with multi-level classification indexes, and establishes a hierarchical tagging system based on chronological period, kiln affiliation, and vessel type.

[0084] In this embodiment, the limitations of traditional identification techniques are overcome. By acquiring three-dimensional feature information of hidden parts, an unreplicable digital fingerprint is established for cultural relics. The intelligent data processing process significantly improves the accuracy and efficiency of feature extraction. The standardized data storage system provides a reliable foundation for subsequent comparative research. The non-contact acquisition method completely avoids damage to cultural relics during the detection process.

[0085] Specifically, the sample collection module includes:

[0086] The feature analysis unit employs a deep convolutional neural network architecture to automatically identify and vectorize the microscopic features of hidden areas.

[0087] The intelligent matching engine integrates multi-scale feature similarity calculation algorithms and supports cross-sample comparison based on attention mechanisms.

[0088] The results presentation interface provides a visual interactive system that offers feature matching degree heatmaps and 3D comparison views, and generates structured identification reports.

[0089] In this embodiment, the focus shifts to the hidden features that are difficult to observe in traditional identification methods, effectively avoiding the replication of high-quality forgeries; intelligent automatic analysis significantly improves the efficiency and accuracy of identification; non-contact detection completely avoids damage to cultural relics; and the three-dimensional visualization interface makes the identification results more intuitive and credible.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0091] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for identification based on the characteristics of concealed parts of ancient ceramics, characterized in that, Includes the following steps: S1, Database Construction: Collect characteristic trace data of hidden parts of standard ancient ceramics and establish a database of genuine samples; S2, Data Acquisition: Acquire characteristic trace data of hidden parts of the ancient ceramic to be identified using endoscopic camera equipment; S3, Independent Comparison: The characteristic trace data of the ancient ceramic to be identified is independently compared with the database of genuine samples. S4, Result Judgment: Based on the degree of consistency of the comparison results, determine the authenticity of the ancient ceramic to be identified.

2. The method for identification based on the characteristics of concealed parts of ancient ceramics according to claim 1, characterized in that: The concealed part in S1 is the area where the image acquisition instrument failed to acquire image data due to the shape structure or the repair process.

3. The method for identification based on the characteristics of concealed parts of ancient ceramics according to claim 1, characterized in that: The feature traces in S1 include: Characteristics of tire joining: unrepaired manual tire joining marks at the inner wall joining point; Fingerprint or tool marks formed by pinching with fingers at the inner opening; The dripping characteristic is the traces of natural dripping of the glaze or the inner wall of the body; Spiral pattern characteristics, traces of different spiral patterns formed on the inner wall due to different eras of craftsmanship; Features of use: friction or wetting marks on the inner wall due to actual use.

4. The method for identification based on the characteristics of concealed parts of ancient ceramics according to claim 1, characterized in that: The genuine product sample database in S1 includes: Image acquisition range: Multi-angle image acquisition of hidden parts of known genuine ancient ceramics; Feature parameter extraction: Based on the acquired image data, feature trace parameters of the concealed parts are extracted, including surface texture morphology, three-dimensional depth distribution and spatial arrangement features of material composition; The data association logic associates the extracted feature parameters with the corresponding ancient ceramics' age information, kiln attributes, and production process tags, forming a structured feature database.

5. The identification system based on the characteristics of concealed parts of ancient ceramics according to claim 1, characterized in that: The autonomous comparison in S3 is achieved through the following steps: A1, Feature standardization processing: Denoising, registration and enhancement processing are performed on the collected image data of concealed parts, and multi-scale feature vectors are extracted; A2, Matching Analysis: Employs a deep metric learning algorithm to calculate the similarity measure between the test sample and the database samples in the feature embedding space; A3, Decision Output: Generates identification conclusions including confidence assessments based on similarity measurement results, and outputs a visual comparison report of feature matching.

6. A system for identifying ancient ceramics based on the characteristics of concealed parts, according to any one of claims 1-5, characterized in that, include: The sample acquisition module is used to collect feature data of hidden parts of genuine products through endoscopic camera equipment and store them in the database; The autonomous comparison module is used to extract the feature data of the items to be identified and perform intelligent comparison and analysis with the database; The user feedback module is used to receive manually corrected data for the identification results and to optimize the database and comparison model.

7. The identification system based on the characteristics of concealed parts of ancient ceramics according to claim 6, characterized in that: The sample acquisition module includes: Optical acquisition components, equipped with a multispectral endoscopic imaging device with an adaptive focusing mechanism; The data processing component is an image processing unit that integrates a 3D point cloud reconstruction algorithm, capable of generating feature models that include surface texture and three-dimensional shape. The data storage component features a database architecture with multi-level classification indexes, and establishes a hierarchical tagging system based on chronological period, kiln affiliation, and vessel type.

8. The identification system based on the characteristics of concealed parts of ancient ceramics according to claim 6, characterized in that: The sample acquisition module includes: The feature analysis unit employs a deep convolutional neural network architecture to automatically identify and vectorize the microscopic features of hidden areas. The intelligent matching engine integrates multi-scale feature similarity calculation algorithms and supports cross-sample comparison based on attention mechanisms. The results presentation interface provides a visual interactive system that offers feature matching degree heatmaps and 3D comparison views, and generates structured identification reports.

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