Visual image processing method and apparatus based on strain mechanism, device, and medium

Abstract

Disclosed in the present invention are a visual image processing method and apparatus based on a strain mechanism, a device, and a medium. The method comprises: acquiring a plurality of pieces of photon energy information on the basis of a charge-coupled device camera or an infrared camera, and extracting variable elements between photons on the basis of the photon energy information, wherein the variable elements include a fluctuation difference variation, a dimensionality variation, and a frequency variation; generating a plurality of energy fields on the basis of the variable elements between the photons, wherein each energy field comprises a plurality of pieces of feature information, and types of the feature information include boundary features, form features, and scale features; and performing spatial reasoning on the basis of the feature information of the energy fields, and splicing the energy fields to obtain a geometric shape of an object. The present invention relates to the field of image processing. The visual image processing technology based on the strain mechanism of the present invention achieves a "general algorithm" for visual image processing, solves global application problems, can accurately and reliably find a target of interest in any environment, and reduces energy consumption.

Description

Visual image processing methods, devices, equipment, and media based on strain mechanisms Technical Field

[0001] This invention relates to the field of image processing, and more particularly to visual image processing methods, apparatuses, devices, and media based on strain mechanisms. Background Technology

[0002] Visual image processing is an interdisciplinary field that combines knowledge from computer science, mathematics, physics, and engineering, focusing on extracting useful information from digital images or video sequences. Visual image processing aims to simulate the capabilities of the human visual system to automatically interpret, understand, and manipulate image data, thereby enabling a range of applications such as object recognition, scene understanding, image enhancement, and medical diagnosis.

[0003] With the continuous development of artificial intelligence technology, visual image processing technology has also developed rapidly. Currently, the most widely used technologies are deep learning and large-scale model technology based on neural networks. However, visual image processing technology requires a large amount of sample data to train models, but the environment is constantly changing, making it difficult to obtain high-quality training samples under various conditions. Furthermore, the number of model parameters is increasing, leading to greater complexity and requiring powerful hardware support, resulting in problems such as high computing power, high energy consumption, and high cost. Therefore, there is an urgent need to propose a low-cost visual image processing method. Summary of the Invention

[0004] This invention solves the technical problem of high energy consumption in image visual processing in the prior art by providing a visual image processing method, apparatus, device and medium based on strain mechanism, and achieves the technical effect of image processing with low energy consumption.

[0005] In a first aspect, the present invention provides a visual image processing method based on a strain mechanism, the method comprising:

[0006] Several photon energy information is acquired using a charge-coupled device camera or an infrared camera, and the changing elements between photons are extracted based on the photon energy information. The changing elements include fluctuation difference changes, dimensional changes, and frequency changes.

[0007] Based on the changing elements between light quanta, several energy fields are generated. These energy fields include several characteristic information types, such as boundary characteristics, morphological characteristics, and scale characteristics.

[0008] Based on the characteristic information of the energy field, spatial reasoning is performed, and the energy fields are spliced ​​together to obtain the geometric shape of the object.

[0009] Furthermore, acquiring some photon energy information based on a charge-coupled device camera or an infrared camera also includes:

[0010] A CCD camera or infrared camera acquires a number of photon energy information and converts the photon energy information into electronic signals;

[0011] CCD cameras or infrared cameras generate target images corresponding to several photon energy information based on electronic signals.

[0012] Furthermore, based on photon energy information, the changing elements between photons are extracted, including:

[0013] Based on a preset detection algorithm, the fluctuation difference between the photon energies of each pixel in each row of pixels in the target image is extracted;

[0014] Based on the fluctuation differences existing in each row, several energy segments are obtained by dividing the data.

[0015] Obtain the frequency of each energy segment and determine the starting spatial position, ending spatial position, and length of that energy segment.

[0016] Furthermore, based on the fluctuation differences present in each row, several energy segments are obtained, including:

[0017] The fluctuation difference between each pixel in each row of the target image is determined sequentially according to the arrangement order of each pixel in each row of the target image;

[0018] When the fluctuation difference between pixels in each row is greater than the preset fluctuation threshold, the data is divided once to obtain an energy segment.

[0019] Furthermore, based on the changing elements between light quanta, several energy fields are generated, including:

[0020] Based on the starting spatial position, ending spatial position, and length of several energy segments, adjacent energy segments with the same photon energy are merged to obtain several energy fields.

[0021] Boundary information is extracted from each energy field to obtain the corresponding feature information.

[0022] Furthermore, based on the starting and ending spatial positions and lengths corresponding to several energy segments, adjacent energy segments with the same photon energy are merged to obtain several energy fields, including:

[0023] Based on the starting and ending spatial positions of energy segments with the same photon energy, determine whether the energy segments with the same photon energy are adjacent;

[0024] By splicing together adjacent energy segments that have the same photon energy according to the length of each energy segment, an energy field is obtained.

[0025] Furthermore, based on the characteristic information of the energy field, spatial reasoning is performed, and the energy fields are pieced together to obtain the geometric shape of the object, including:

[0026] Based on the morphological characteristics of each energy field, spatial adjacent parts are spliced ​​together to obtain the body shape;

[0027] Geometric feature analysis of the body shape is performed to obtain the geometric center point;

[0028] Construct a geometric skeleton line segment using the geometric center point;

[0029] The geometric shape of the object in the target image is obtained from the geometric skeleton line segments.

[0030] Secondly, the present invention provides a visual image processing apparatus based on a strain mechanism, the apparatus comprising:

[0031] The element extraction module is used to acquire several photon energy information based on a charge-coupled device camera or an infrared camera, and to extract the changing elements between photons based on the photon energy information. The changing elements include fluctuation difference changes, dimensional changes, and frequency changes.

[0032] The energy field partitioning module is used to generate several energy fields based on the changing elements between photons. The energy fields include several characteristic information, and the types of characteristic information include boundary characteristics, morphological characteristics, and scale characteristics.

[0033] The shape generation module is used to perform spatial reasoning based on the characteristic information of the energy field, and to stitch the energy fields together to obtain the geometric shape of the object.

[0034] Thirdly, the present invention provides an electronic device, comprising:

[0035] processor;

[0036] Memory used to store processor-executable instructions;

[0037] The processor is configured to execute a strain-based visual image processing method as provided in the first aspect.

[0038] Fourthly, the present invention provides a non-transitory computer-readable storage medium that, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform a strain-based visual image processing method as provided in the first aspect.

[0039] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0040] This invention provides a visual image processing method based on a strain mechanism. The method includes: acquiring several photon energy information using a charge-coupled device (CCD) camera or an infrared camera, and extracting variation elements between photons based on the photon energy information, wherein the variation elements include fluctuation difference changes, dimensional changes, and frequency changes; generating several energy fields based on the variation elements between photons, wherein the energy fields include several feature information, the types of feature information including boundary features, morphological features, and scale features; performing spatial reasoning based on the feature information of the energy fields, and stitching the energy fields together to obtain the geometric shape of the object. This invention's visual image processing technology based on a strain mechanism realizes a "universal algorithm" for visual image processing, solves the global application problem, and can accurately and reliably find the target of interest in any environment; it does not require training and learning, reducing computing power and energy consumption. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 is a schematic flowchart of the visual image processing method based on strain mechanism provided by the present invention. Detailed Implementation

[0043] The embodiments of the present invention provide a visual image processing method based on a strain mechanism, which solves the technical problem of high energy consumption in the prior art when performing image visual processing.

[0044] The technical solution of this invention is to solve the above-mentioned technical problems, and the overall idea is as follows:

[0045] A visual image processing method based on strain mechanisms includes: acquiring several photon energy information using a charge-coupled device (CCD) camera or an infrared camera, and extracting the variation elements between photons based on the photon energy information, wherein the variation elements include fluctuation difference variation, dimensional variation, and frequency variation; generating several energy fields based on the variation elements between photons, wherein the energy fields include several feature information, the types of feature information including boundary features, morphological features, and scale features; performing spatial reasoning based on the feature information of the energy fields, and stitching the energy fields together to obtain the geometric shape of the object.

[0046] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0047] First, it should be clarified that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0048] This invention provides a visual image processing method based on a strain mechanism, as shown in Figure 1, comprising steps S11-S13:

[0049] Step S11: Acquire several photon energy information based on a charge-coupled device camera or an infrared camera, and extract the change elements between photons based on the photon energy information. The change elements include fluctuation difference change, dimension change and frequency change.

[0050] Specifically, it also includes: a CCD camera or an infrared camera acquiring a number of photon energy information and converting the photon energy information into electronic signals; the CCD camera or infrared camera generating a target image corresponding to the photon energy information based on the electronic signals.

[0051] Infrared cameras or charge-coupled device (CCD) cameras use a charge-coupled device as a photosensitive element. When a photon strikes a pixel on the CCD, a certain number of electron-hole pairs are generated according to the photoelectric effect. The number of electrons generated is proportional to the energy of the incident photon. The electrons are collected and stored in a capacitor. The accumulated charge is converted into a digital signal by an analog-to-digital converter (ADC), forming the energy value in the image.

[0052] Extracting the variation elements between photons based on photon energy information includes: extracting the fluctuation difference between the photon energies of each pixel in each row of pixels in the target image based on a preset detection algorithm; dividing the image into several energy segments based on the fluctuation differences in each row; obtaining the frequency of each energy segment and determining the starting spatial position, ending spatial position, and length of the corresponding energy segment.

[0053] The preset detection algorithm can be an edge detection algorithm, etc., and there are no restrictions here. It can be understood that the target image consists of several pixels, and there are several rows of pixels in the target image. Each row contains several pixels, and each pixel has a fixed photon energy value (or a fixed grayscale value). The difference in energy values ​​between pixels in each row is the fluctuation difference between pixels.

[0054] Based on the fluctuation differences existing in each row, several energy segments are obtained by dividing the image into several segments. This includes: determining the fluctuation differences between each pixel in each row of the target image according to the arrangement order of each pixel in each row of the target image; when the fluctuation difference between each pixel in each row is greater than a preset fluctuation threshold, the image is divided once to obtain an energy segment.

[0055] For example, the first row of the target image contains 10 pixels with energy values ​​of 1, 1, 2, 2, 3, 1, 5, 4, 6, and 5, respectively. The energy value of the first pixel is then calculated by subtracting the energy values ​​of the next nine pixels. When the difference between the energy value of the first pixel and the energy value of a pixel in the same row exceeds a preset fluctuation threshold, the pixels preceding that pixel in the same row are grouped into an energy segment. For example, if the preset fluctuation threshold is 2, the energy value of the first pixel only exceeds 2 when its difference from the energy value of the seventh pixel is greater than 2. Therefore, the first to sixth pixels are grouped into an energy segment, and the calculation restarts from the seventh pixel.

[0056] The frequency of an energy range refers to the number of photons falling within that energy range within a certain time period. The number of photons indicates the radiation intensity or brightness level corresponding to that energy range.

[0057] For the target image, each pixel has coordinates (x, y). The starting spatial position of the energy segment is the position where the pixel first appears in the energy segment, that is, the coordinate pair composed of the smallest row number and column number. The ending spatial position of the energy segment is the position where the pixel last appears in the energy segment, that is, the coordinate pair composed of the largest row number and column number. The length of the energy segment is the number of pixels in the region (or the maximum distance in the row direction).

[0058] Step S12: Based on the changing elements between light quanta, several energy fields are generated. The energy fields include several characteristic information, and the types of characteristic information include boundary characteristics, morphological characteristics, and scale characteristics.

[0059] Based on the changing elements between photons, several energy fields are generated, including: merging adjacent energy segments with the same photon energy according to the starting spatial position, ending spatial position, and length of several energy segments to obtain several energy fields; extracting boundary information for each energy field to obtain the characteristic information corresponding to each energy field.

[0060] Having the same photon energy means that the energy values ​​of the two energy segments are the same.

[0061] Based on the starting and ending spatial positions and lengths of several energy segments, adjacent energy segments with the same photon energy are merged to obtain several energy fields. This includes: determining whether each energy segment with the same photon energy is adjacent based on the starting and ending spatial positions of the energy segments with the same photon energy; and splicing adjacent energy segments with the same photon energy according to the length of each energy segment to obtain an energy field.

[0062] It is important to note that energy segments that are not adjacent but have the same photon energy cannot be classified into the same energy field. Adjacent means that there is at least one identical y-value between the pixels of an energy segment, and the difference between their x-values ​​is 1. For example, the starting spatial position of energy segment A in the first row is (1.1), and the ending spatial position is (1.10); the starting spatial position of energy segment B in the second row is (2.5), and the ending spatial position is (2.10); the starting spatial position of energy segment C in the third row is (3.9), and the ending spatial position is (3.10); and the starting spatial position of energy segment D in the third row is (3.20), and the ending spatial position is (3.22). The energy values ​​of energy segments A, B, C, and D are all the same.

[0063] Obviously, energy segment A is adjacent to energy segment B, energy segment B is adjacent to energy segment C, and energy segment B is not adjacent to energy segment D. Therefore, by splicing together energy segment A, energy segment B, and energy segment C, an energy field is obtained.

[0064] By performing boundary information extraction on each energy field, the external contour of each energy segment can be accurately identified and depicted. Furthermore, the geometry, structural complexity, and spatial expansion of the energy field can be analyzed to obtain boundary features (such as boundary length and curvature), morphological features (such as area, perimeter, roundness, and principal axis direction), and scale features (such as centroid location, bounding box size, and equivalent diameter).

[0065] Step S13: Based on the characteristic information of the energy field, perform spatial reasoning and splice the energy field to obtain the geometric shape of the object.

[0066] Based on the characteristic information of the energy field, spatial reasoning is performed, and the energy fields are spliced ​​together to obtain the geometric shape of the object. This includes: splicing adjacent objects in space according to the morphological characteristics of each energy field to obtain the body shape; performing geometric feature analysis on the body shape to obtain the geometric center point; constructing geometric skeleton line segments based on the geometric center point; and obtaining the geometric shape of the object in the target image based on the geometric skeleton line segments.

[0067] First, by analyzing the morphological characteristics of each energy field, the specific shape, size, and spatial distribution of each energy segment are identified. Based on these morphological characteristics, spatial adjacency stitching technology is used to recombine parts belonging to the same object but divided into different energy segments, forming a complete body shape. After stitching, geometric feature analysis is performed on the body shape to accurately calculate its geometric center point. The geometric center point is the average position of the coordinates of all points within the shape, representing the spatial positioning core of the shape. Based on this geometric center point, geometric skeleton segments are constructed. This involves finding the main extension directions of the shape through principal component analysis (PCA) or other methods, and drawing line segments along these directions. These line segments reflect the basic framework and structural characteristics of the shape. Finally, based on the constructed geometric skeleton segments, the geometric shape of the object in the target image can be obtained.

[0068] In summary, this invention provides a visual image processing method based on a strain mechanism. The method includes: acquiring several photon energy information using a charge-coupled device (CCD) camera or an infrared camera; extracting variation elements between photons based on the photon energy information, wherein the variation elements include fluctuation difference changes, dimensional changes, and frequency changes; generating several energy fields based on the variation elements between photons, wherein the energy fields include several feature information types, including boundary features, morphological features, and scale features; performing spatial reasoning based on the feature information of the energy fields; and stitching the energy fields together to obtain the geometric shape of the object. This invention's strain mechanism-based visual image processing technology realizes a "universal algorithm" for visual image processing, solves the global application problem, and can accurately and reliably find the target of interest in any environment; it eliminates the need for training and learning, reducing computational power and energy consumption.

[0069] Based on the same inventive concept, the present invention provides a visual image processing device based on a strain mechanism, the device comprising:

[0070] The element extraction module is used to acquire several photon energy information based on a charge-coupled device camera or an infrared camera, and to extract the changing elements between photons based on the photon energy information. The changing elements include fluctuation difference changes, dimensional changes, and frequency changes.

[0071] The energy field partitioning module is used to generate several energy fields based on the changing elements between photons. The energy fields include several characteristic information, and the types of characteristic information include boundary characteristics, morphological characteristics, and scale characteristics.

[0072] The shape generation module is used to perform spatial reasoning based on the characteristic information of the energy field, and to stitch the energy fields together to obtain the geometric shape of the object.

[0073] Based on the same inventive concept, the present invention also provides an electronic device, comprising:

[0074] processor;

[0075] Memory used to store processor-executable instructions;

[0076] The processor is configured to execute a strain-based visual image processing method as described above.

[0077] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the visual image processing method based on the strain mechanism as described above.

[0078] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiments of the present invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the information processing method described in the embodiments of the present invention. Therefore, how the electronic device implements the method in the embodiments of the present invention will not be described in detail here. Any electronic device used by those skilled in the art to implement the information processing method in the embodiments of the present invention falls within the scope of protection of the present invention.

[0079] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0083] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0084] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A visual image processing method based on strain mechanism, characterized in that, The method includes: The system acquires several photon energy information based on a charge-coupled device (CCD) camera or an infrared camera, and extracts the variation elements between photons based on this information. These variation elements include fluctuation difference changes, dimensional changes, and frequency changes. The acquisition of photon energy information based on the CCD camera or infrared camera also includes: acquiring several photon energy information using a CCD camera or infrared camera and converting this information into electronic signals; generating a target image corresponding to the photon energy information based on the electronic signals; and extracting the variation elements between photons based on the photon energy information, including: extracting the fluctuation difference between the photon energies of each pixel in each row of the target image using a preset detection algorithm; dividing the image into several energy segments based on the fluctuation differences in each row; obtaining the frequency of each energy segment and determining its starting spatial position, ending spatial position, and length. Based on the changing elements between photons, several energy fields are generated. Each energy field includes several characteristic information types, including boundary features, morphological features, and scale features. Specifically, based on the starting spatial position, ending spatial position, and length of several energy segments, adjacent energy segments with the same photon energy are merged to obtain several energy fields. Boundary information is extracted from each energy field to obtain the characteristic information corresponding to each energy field. Based on the characteristic information of the energy field, spatial reasoning is performed, and the energy fields are spliced ​​together to obtain the geometric shape of the object. This includes: splicing adjacent objects in space according to the morphological characteristics of each energy field to obtain the body shape; performing geometric feature analysis on the body shape to obtain the geometric center point; constructing geometric skeleton line segments with the geometric center point; and obtaining the geometric shape of the object in the target image based on the geometric skeleton line segments.

2. The visual image processing method based on strain mechanism as described in claim 1, characterized in that, Based on the fluctuation differences present in each row, several energy segments are obtained, including: The fluctuation difference between each pixel in each row of the target image is determined sequentially according to the arrangement order of each pixel in each row of the target image; When the fluctuation difference between pixels in each row is greater than the preset fluctuation threshold, the data is divided once to obtain an energy segment.

3. The visual image processing method based on strain mechanism as described in claim 1, characterized in that, Based on the starting and ending spatial positions and lengths of several energy segments, adjacent energy segments with the same quantum energy are merged to obtain several energy fields, including: Based on the starting and ending spatial positions of energy segments with the same photon energy, determine whether the energy segments with the same photon energy are adjacent; By splicing together adjacent energy segments that have the same photon energy according to the length of each energy segment, an energy field is obtained.

4. A visual image processing device based on a strain mechanism, characterized in that, The apparatus for the strain-mechanism-based visual image processing method according to any one of claims 1-3 comprises: The element extraction module is used to acquire several photon energy information based on a charge-coupled device camera or an infrared camera, and to extract the changing elements between photons based on the photon energy information. The changing elements include fluctuation difference changes, dimensional changes, and frequency changes. The energy field partitioning module is used to generate several energy fields based on the changing elements between photons. The energy fields include several characteristic information, and the types of characteristic information include boundary characteristics, morphological characteristics, and scale characteristics. The shape generation module is used to perform spatial reasoning based on the characteristic information of the energy field, and to stitch the energy fields together to obtain the geometric shape of the object.

5. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the strain-based visual image processing method as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the visual image processing method based on the strain mechanism as described in any one of claims 1 to 3.

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