Probe tip recognition method and device in microscopic image

Abstract

The application discloses a probe tip recognition method in a microscopic image, a probe tip recognition device in a microscopic image, an electronic device and a storage medium, and belongs to the technical field of probe positioning of a probe station. The probe tip recognition method in the microscopic image comprises the following steps: acquiring a probe gray-scale image; converting the probe gray-scale image into a tip feature binary image; screening a connected domain with an edge topography conforming to a probe tip shape from the tip feature binary image as a tip connected domain; and generating a tip coordinate based on the tip connected domain. The method can effectively filter out interference substances with similar brightness to the probe tip through edge topography screening, thereby improving the accuracy and stability of probe tip recognition.

Description

Technical Field

[0001] This application belongs to the field of probe station and probe positioning technology, specifically relating to a method, device, electronic device and storage medium for identifying probe tips in microscopic images. Background Technology

[0002] Probe stations are primarily used in the semiconductor, optoelectronic, integrated circuit, and packaging industries for testing. Using probe cards as a test interface, they mainly test bare dies on wafers. By connecting the tester and the chip, signals are transmitted to test chip parameters. A crucial step in the testing process is probe alignment, which involves calculating the pixel coordinates of the probe center in the image, converting them to physical coordinates in the world coordinate system, and inputting these coordinates into the probe station system. This controls the actuator to insert the probe into the corresponding test area on the die. Probe cards are typically multi-station, with multiple probes distributed across them. Because the probes used are micrometer-sized, they need to be displayed under a magnified objective lens.

[0003] Probe stages are typically divided into high-magnification and low-magnification fields of view. High-magnification fields of view can magnify the probe to display its overall features. Usually, there is only one probe or several probes, which are used to locate the center of the probe and determine its coordinates in the image. Low-magnification fields of view cover a larger number of probes and are usually used to initially locate the position of the probes before further high-magnification probe identification.

[0004] Automated testing is the future trend for probe stations, effectively saving manpower and improving testing efficiency. Currently, the most common probe alignment process involves first confirming the initial probe position under low magnification, manually moving the field of view center to the initial probe position, and then switching to high magnification for high-magnification probe identification. To achieve automated testing, the confirmation of probe position under low magnification will inevitably evolve from manual to automatic confirmation. Therefore, probe identification and positioning under low magnification is necessary; however, there is currently no publicly available algorithm for probe identification under low magnification objectives. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for identifying probe tips in microscopic images to solve the problem of difficulty in identifying probe tips in existing microscopic images.

[0006] According to a first aspect of the embodiments of this application, a method for identifying probe tips in a microscopic image is provided, the method comprising:

[0007] Obtain the probe grayscale image;

[0008] The probe grayscale image is converted into a binary image of the probe tip features;

[0009] From the binary image of the needle tip features, connected regions whose edge morphology matches the shape of the probe tip are selected as the needle tip connected regions;

[0010] The needle tip coordinates are generated based on the needle tip connected components.

[0011] In some optional embodiments of this application, converting the probe grayscale image into a pinpoint feature binary image includes:

[0012] The probe grayscale image is binarized based on the first threshold to obtain a highlight feature binary image.

[0013] The probe grayscale image is binarized based on the second threshold to obtain the overall feature binary image;

[0014] The needle tip feature binary map is drawn based on the highlighted feature binary map and the overall feature binary map;

[0015] Wherein, the first threshold is greater than the second threshold.

[0016] In some optional embodiments of this application, drawing the needle tip feature binary map based on the highlight feature binary map and the overall feature binary map includes:

[0017] Obtain the connected components of the highlighted features from the binary image of the highlighted features;

[0018] Select the coordinates of a pixel from the highlighted feature connected component;

[0019] Obtain the overall feature connected components from the overall feature binary graph;

[0020] Obtain the overall feature connected component containing the pixel coordinates and whose pixel area is greater than the second preset area, and draw a binary map of the needle tip feature.

[0021] In some optional embodiments of this application, obtaining the bright feature connected components from the bright feature binary map includes:

[0022] The connected components with pixel areas larger than a first preset area are obtained from the binary image of the highlight features as the highlighted feature connected components.

[0023] In some optional embodiments of this application, obtaining the probe grayscale image includes:

[0024] Acquire probe images in the low-magnification objective lens field of view under the current light source brightness;

[0025] The probe image is converted to grayscale to obtain the current probe grayscale image;

[0026] Adjust the light source brightness based on the pixel mean of the current probe grayscale image;

[0027] Repeat the above steps until the average pixel value of the current probe grayscale image is greater than the first preset pixel value and less than the second preset pixel value, and then use the current probe grayscale image as the probe grayscale image.

[0028] In some optional embodiments of this application, the grayscale image of the probe is further preprocessed before being converted into a binary image of the probe tip feature.

[0029] Preprocessing the probe grayscale image includes:

[0030] The grayscale image of the probe is subjected to median filtering to obtain a median-filtered image;

[0031] The preprocessed probe grayscale image is obtained by subtracting the median-filtered image from the probe grayscale image.

[0032] In some optional embodiments of this application, selecting connected regions whose edge morphology conforms to the probe tip shape from the binary image of the tip features as tip connected regions includes:

[0033] The connected regions with the minimum bounding moment and an aspect ratio less than a preset ratio and a pixel area within a preset threshold range are obtained from the binary image of the needle tip feature as the needle tip connected regions.

[0034] In some optional embodiments of this application, generating needle tip coordinates based on the needle tip connected components includes:

[0035] Obtain the minimum bounding moment of the connected region of the needle tip;

[0036] The center coordinates of the minimum enclosing moment are used as the needle tip coordinates.

[0037] In some optional embodiments of this application, generating needle tip coordinates based on the needle tip connected components includes:

[0038] The connected region of the needle tip is fitted with a circle, and the coordinates of the circle center are used as the coordinates of the needle tip.

[0039] According to a second aspect of the embodiments of this application, a probe station is provided, which identifies probe tips using a probe tip identification method in a microscopic image as described in any of the above embodiments.

[0040] According to a third aspect of the embodiments of this application, a probe tip identification device in a microscopic image is provided, comprising:

[0041] The acquisition module is used to acquire the probe grayscale image;

[0042] The conversion module is used to convert the probe grayscale image into a pin tip feature binary image;

[0043] The filtering module is used to filter out connected regions whose edge morphology conforms to the probe tip shape from the binary image of the tip feature as the tip connected regions;

[0044] A generation module is used to generate needle tip coordinates based on the needle tip connected domain.

[0045] According to a fourth aspect of the embodiments of this application, an electronic device is provided, which may include:

[0046] processor;

[0047] Memory used to store processor-executable instructions;

[0048] The processor is configured to execute instructions to implement the probe tip identification method in a microscopic image as shown in any embodiment of the first aspect.

[0049] According to a fifth aspect of the embodiments of this application, a storage medium is provided, which, when the instructions in the storage medium are executed by a processor of an information processing device or a server, enables the information processing device or server to implement the probe tip identification method in a microscopic image as shown in any embodiment of the first aspect.

[0050] The above-mentioned technical solution of this application has the following beneficial technical effects:

[0051] The method described in this application converts the probe grayscale image into a binary image of probe tip features. Based on the edge morphology of connected components in the binary image, connected components that match the probe tip shape are selected, thereby achieving probe tip identification. This method effectively filters out interference objects with similar brightness to the probe tip through edge morphology selection, thus improving the accuracy and stability of probe tip identification. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of a probe tip identification method in a microscopic image according to an exemplary embodiment of this application;

[0053] Figure 2 This is a grayscale image of the probe in an exemplary embodiment of this application;

[0054] Figure 3 This is a flowchart illustrating step S102 in an exemplary embodiment of this application;

[0055] Figure 4 This is a flowchart illustrating step S1020 in an exemplary embodiment of this application;

[0056] Figure 5 This is a median filter diagram in an exemplary embodiment of this application;

[0057] Figure 6This is a preprocessed probe grayscale image in an exemplary embodiment of this application;

[0058] Figure 7 This is a feature map of the illuminated pixels at the probe tip in an exemplary embodiment of this application;

[0059] Figure 8 This is a flowchart illustrating step S1023 in an exemplary embodiment of this application;

[0060] Figure 9 This is a binary image of the highlighted features in an exemplary embodiment of this application;

[0061] Figure 10 This is a binary image of the overall features in an exemplary embodiment of this application;

[0062] Figure 11 This is a flowchart illustrating step S10231 in an exemplary embodiment of this application;

[0063] Figure 12 This is a flowchart illustrating step S101 in an exemplary embodiment of this application;

[0064] Figure 13 This is a flowchart illustrating step S103 in an exemplary embodiment of this application;

[0065] Figure 14 This is a schematic diagram of the probe tip identification result in an exemplary embodiment of this application;

[0066] Figure 15 This is a flowchart illustrating step S104 in an exemplary embodiment of this application;

[0067] Figure 16 This is a flowchart illustrating step S104 in another exemplary embodiment of this application;

[0068] Figure 17 This is a schematic diagram of a probe tip identification device in a microscopic image according to an exemplary embodiment of this application;

[0069] Figure 18 This is a schematic diagram of the electronic device structure in an exemplary embodiment of this application;

[0070] Figure 19 This is a schematic diagram of the hardware structure of an electronic device in an exemplary embodiment of this application. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0072] The accompanying drawings illustrate layer structure diagrams according to embodiments of this application. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0073] Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0074] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0075] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0076] Studies have revealed that probe tips in low-magnification fields of view exhibit a bright spot appearance. Furthermore, low-magnification fields may contain contaminants or wear / corrosion on the probe shaft that resemble the tip shape. Because probe tips are small and difficult to distinguish from contaminants and wear, directly segmenting and extracting them based on a fixed brightness threshold is susceptible to the effects of contaminants and wear, leading to incorrect localization. To address this issue, this application proposes a method, device, electronic equipment, and storage medium for probe tip recognition in microscopic images.

[0077] The following description, in conjunction with the accompanying drawings, details the probe tip identification method, probe tip identification device, electronic device, and storage medium in the microscopic images provided in this application, through specific embodiments and application scenarios.

[0078] like Figure 1 As shown, in a first aspect of this application embodiment, a method for identifying probe tips in a microscopic image is provided, the method including:

[0079] Step S101: Obtain the probe grayscale image;

[0080] Step S102: Convert the probe grayscale image into a pin tip feature binary image;

[0081] Step S103: Select connected regions whose edge morphology matches the probe tip shape from the binary image of the tip features as the tip connected regions;

[0082] Step S104: Generate needle tip coordinates based on the needle tip connected components.

[0083] like Figure 2 As shown, in this embodiment, during the probe stage alignment process, a probe grayscale image is obtained by converting the probe image under a low-magnification objective lens. The magnification of the low-magnification objective lens can be greater than or equal to 6x and less than or equal to 10x. The pin tip feature binary image obtained from the probe grayscale image conversion consists of a first pixel value and a second pixel value. The first pixel value is greater than the second pixel value. The first pixel value can be 255, and the second pixel value can be 0. In the pin tip feature binary image, pixels with the first pixel value and connected to each other form a connected region, and there can be multiple connected regions. In this embodiment, the connected regions are used to characterize high-brightness areas, including the pin tip and other interfering objects. Other interfering objects include dirt and pin wear. This embodiment analyzes the edge morphology features of the connected regions, excludes connected regions corresponding to interfering objects such as dirt and pin wear, and selects connected regions that conform to the probe tip morphology as pin tip connected regions. There can be multiple pin tip connected regions. The pin tip connected regions are used to characterize the position of the pin tip, and thus pin tip coordinates can be generated based on the position of the pin tip connected regions. There can be multiple pin tip coordinates. This method can effectively filter out interference objects with similar brightness to the probe tip by screening edge morphology, thereby improving the accuracy of probe tip identification.

[0084] like Figure 3 As shown, in some embodiments, step S102 may include:

[0085] Step S1021: Binarize the probe grayscale image based on the first threshold to obtain a highlight feature binary image;

[0086] Step S1022: Binarize the probe grayscale image based on the second threshold to obtain the overall feature binary image;

[0087] Step S1023: Draw the needle tip feature binary map based on the highlight feature binary map and the overall feature binary map;

[0088] The first threshold is greater than the second threshold.

[0089] like Figure 4 As shown, in some embodiments, step S1021 is further included before:

[0090] Step S1020: Preprocess the probe grayscale image. Specifically, the preprocessing of the probe grayscale image includes:

[0091] S10201: Perform median filtering on the probe grayscale image, such as... Figure 5 As shown, the median-filtered image is obtained. S10202: Subtract the median-filtered image from the probe grayscale image, as shown. Figure 6 As shown, the preprocessed probe grayscale image is obtained. The preprocessed probe grayscale image can further highlight the probe tip, and the preprocessed probe grayscale image is used as the probe grayscale image for subsequent processing.

[0092] In this embodiment, a first threshold is used to characterize the brightness of the central region during probe tip imaging, and a second threshold is used to characterize the brightness of the edge region during probe tip imaging. Binarizing the probe grayscale image based on the first threshold includes converting pixels with values ​​not less than the first threshold to first pixel values, and converting pixels with values ​​less than the first threshold to second pixel values, resulting in a highlight feature binary image. In this embodiment, the highlight feature binary image is used to characterize the central region of the probe tip. Binarizing the probe grayscale image based on the second threshold includes converting pixels with values ​​not less than the second threshold to first pixel values, and converting pixels with values ​​less than the second threshold to second pixel values, resulting in an overall feature binary image. In this embodiment, the overall feature binary image is used to characterize the entire region of the probe tip. Figure 7 As shown, the central region of the needle tip is bright and sharp, and its brightness gradually decreases towards the surrounding area. This embodiment uses a dual-threshold segmentation design to address the brightening characteristics of the needle tip. By introducing two thresholds for threshold segmentation, the bright area at the center of the needle tip and the overall area of ​​the needle tip can be obtained separately. This allows for joint analysis to improve the accuracy of probe tip identification.

[0093] like Figure 8 As shown, in some embodiments, step S1023 may include:

[0094] Step S10231: Obtain the connected components of the highlighted features from the highlighted feature binary map;

[0095] Step S10232: Select the coordinates of a pixel from the highlighted feature connected component;

[0096] Step S10233: Obtain the global feature connected components from the global feature binary graph;

[0097] Step S10234: Obtain the overall feature connected component containing pixel coordinates and pixel area greater than the second preset area, and draw the pin tip feature binary map.

[0098] In this embodiment, as Figure 9 As shown, in the binary image of the highlight feature, pixels with the first pixel value and connected to each other form a highlighted feature connected region. There can be multiple highlighted feature connected regions. A pixel is randomly selected from each highlighted feature connected region as a seed point, and the coordinates of the seed point are used as the coordinates of the pixel. For example... Figure 10 As shown, in the overall feature binary map, pixels with the second pixel value and connected pixels form an overall feature connected region. There can be multiple overall feature connected regions. From these regions, overall feature connected regions containing pixel coordinates and with a pixel area greater than a second preset area are selected to draw the pin tip feature binary map. This embodiment, based on the characteristic that the central region of the probe tip image is bright and gradually decreases in brightness towards the surrounding areas, selects regions with a bright center and decreasing brightness around the edges. This ensures that the selected regions conform to the brightness distribution characteristics of probe tip imaging, thereby improving the accuracy of probe tip recognition.

[0099] like Figure 11 As shown, in some embodiments, step S10231 may include:

[0100] Step S102311: Obtain the connected components with pixel areas larger than the first preset area from the highlight feature binary map as the highlight feature connected components.

[0101] In this embodiment, the pixel area is the number of pixels. For example, if a connected component consists of 10 pixels, then the pixel area of ​​that connected component is 10. The first preset area is used to characterize the area of ​​the probe tip imaging. In this embodiment, the first preset area is used to filter the connected components in the highlight feature binary map, so that the filtered connected components meet the size characteristics of probe tip imaging, thereby improving the accuracy of probe tip recognition.

[0102] Because the brightness of the probe tip in a low-magnification field of view is greatly affected by the brightness of the light source, if the light source is too dim, the brightness of the probe tip will decrease, making it difficult to locate the probe tip. If the light source is too bright, it will introduce luminous interference. Therefore, it is necessary to control the brightness of the light source to improve the stability of probe tip recognition. Figure 12 As shown, in order to solve the above problems, in some embodiments, step S101 may include:

[0103] Step S1011: Acquire probe images in the field of view of the low-magnification objective lens under the current light source brightness;

[0104] Step S1012: Perform grayscale processing on the probe image to obtain the current probe grayscale image;

[0105] Step S1013: Adjust the light source brightness based on the pixel mean of the current probe grayscale image;

[0106] Step S1014: Repeat the above steps until the average pixel value of the current probe grayscale image is greater than the first preset pixel value and less than the second preset pixel value, and use the current probe grayscale image as the probe grayscale image.

[0107] Both excessively dark and excessively bright probe grayscale images can affect probe tip extraction. Too dark an image reduces the sharpness of the probe tip, making it difficult to distinguish from bright interference objects. Too bright an image introduces white background interference. This embodiment adjusts the light source brightness by introducing a first preset pixel value and a second preset pixel value. When the average pixel value of the probe grayscale image is greater than the first preset pixel value and less than the second preset pixel value, the probe tip can be clearly imaged, making it easy to distinguish from interference objects. This avoids introducing white background interference, thereby improving the accuracy of probe tip recognition.

[0108] like Figure 13 As shown, in some embodiments, step S103 may include:

[0109] Step S1031: Obtain the connected components with the smallest bounding moments and aspect ratios less than a preset ratio and pixel areas within a preset threshold range from the binary image of the needle tip features as the needle tip connected components.

[0110] In this embodiment, the preset ratio can be greater than or equal to 1 and less than or equal to 2. The preset threshold range is used to characterize the pixel area of ​​the pin-tip imaging. For example... Figure 14 As shown, since the tip of the probe is roughly circular, its shape can be effectively filtered by aspect ratio. In this embodiment, the aspect ratio of the minimum bounding moment is used to filter the connected regions so that the filtered regions are nearly circular, which matches the edge shape characteristics of the probe tip. By filtering by pixel area, the filtered connected regions are made to match the size of the probe tip, thereby improving the accuracy of probe tip recognition.

[0111] like Figure 15 As shown, in some embodiments, step S104 may include:

[0112] Step S1041: Obtain the minimum bounding moment of the connected components at the tip;

[0113] Step S1042: Use the center coordinates of the minimum enclosing moment as the needle tip coordinates.

[0114] This embodiment can determine the coordinates of the needle tip center by calculating the center of the minimum enclosing moment.

[0115] like Figure 16 As shown, in some embodiments, step S104 may include:

[0116] Step S1043: Fit a circle to the connected region of the needle tip and use the coordinates of the circle center as the coordinates of the needle tip.

[0117] This embodiment uses a circle to fit the connected region of the needle tip, so that the circular coordinates can be used as the coordinates of the needle tip.

[0118] In a second aspect of the embodiments of this application, a probe station is provided, which identifies probe tips using a probe tip identification method in a microscopic image provided in any of the above embodiments.

[0119] like Figure 17 As shown, in a third aspect of the embodiments of this application, a probe tip identification device in a microscopic image is provided, comprising:

[0120] Module 1601 is used to acquire the probe grayscale image;

[0121] The conversion module 1602 is used to convert the probe grayscale image into a pin tip feature binary image;

[0122] The filtering module 1603 is used to filter out connected regions whose edge morphology conforms to the probe tip shape from the binary image of the tip feature as the tip connected regions.

[0123] Generation module 1604 is used to generate tip coordinates based on the tip connected component.

[0124] The probe tip identification device in the microscopic image in this application embodiment can also be a component, integrated circuit, or chip in a terminal. This device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0125] The probe tip identification device in the microscopic image provided in this application embodiment can implement the various processes of the probe tip identification method in the microscopic image provided in any of the above embodiments. To avoid repetition, it will not be described again here.

[0126] Optionally, such as Figure 18As shown, this application embodiment also provides an electronic device 1100, including a processor 1101, a memory 1102, and a program or instructions stored in the memory 1102 and executable on the processor 1101. When the program or instructions are executed by the processor 1101, they implement the various processes of the above-described probe tip identification method embodiment in microscopic images and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0127] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0128] Figure 19 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0129] The electronic device 1200 includes, but is not limited to, components such as: radio frequency unit 1201, network module 1202, audio output unit 1203, input unit 1204, sensor 1205, display unit 1206, user input unit 1207, interface unit 1208, memory 1209, and processor 1210.

[0130] Those skilled in the art will understand that the electronic device 1200 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1210 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 13 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0131] It should be understood that, in this embodiment, the input unit 1204 may include a graphics processing unit (GPU) 12041 and a microphone 12042. The GPU 12041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1206 may include a display panel 12061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 1207 includes a touch panel 12071 and other input devices 12072. The touch panel 12071 is also called a touch screen. The touch panel 12071 may include a touch detection device and a touch controller. Other input devices 12072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, joysticks, etc., which will not be described in detail here. The memory 1209 can be used to store software programs and various data, including but not limited to applications and operating systems. Processor 1210 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 1210.

[0132] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described microscopic image probe tip identification method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0133] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0134] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described microscopic image probe tip identification method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0135] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0136] It should be noted that, in this document, 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0137] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0138] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for identifying probe tips in microscopic images, characterized in that, include: Obtain the probe grayscale image; Converting the probe grayscale image into a tip feature binary image includes: binarizing the probe grayscale image based on a first threshold to obtain a highlight feature binary image, which is used to characterize the central region of the tip; binarizing the probe grayscale image based on a second threshold to obtain an overall feature binary image, which is used to characterize the overall region of the tip; obtaining a highlight feature connected component from the highlight feature binary image; selecting a pixel coordinate from the highlight feature connected component; obtaining an overall feature connected component from the overall feature binary image; and drawing the tip feature binary image by obtaining an overall feature connected component containing the pixel coordinate and having a pixel area greater than a second preset area; wherein, the first threshold is greater than the second threshold; the first threshold is used to characterize the brightness of the central region when the probe tip is imaged, and the second threshold is used to characterize the brightness of the edge region when the probe tip is imaged. From the binary image of the needle tip features, connected regions whose edge morphology matches the shape of the probe tip are selected as the needle tip connected regions; The needle tip coordinates are generated based on the needle tip connected components.

2. The method for identifying probe tips in a microscopic image according to claim 1, characterized in that, Obtaining the connected components of the highlighted features from the highlighted feature binary map includes: The connected components with pixel areas larger than a first preset area are obtained from the binary image of the highlight features as the highlighted feature connected components.

3. The method for identifying probe tips in a microscopic image according to claim 1, characterized in that, Obtaining the probe grayscale image includes: Acquire probe images in the low-magnification objective lens field of view under the current light source brightness; The probe image is converted to grayscale to obtain the current probe grayscale image; Adjust the light source brightness based on the pixel mean of the current probe grayscale image; Repeat the above steps until the average pixel value of the current probe grayscale image is greater than the first preset pixel value and less than the second preset pixel value, and then use the current probe grayscale image as the probe grayscale image.

4. The method for identifying probe tips in a microscopic image according to claim 1, further comprising preprocessing the probe grayscale image before converting the probe grayscale image into a binary image of tip features; Preprocessing the probe grayscale image includes: The grayscale image of the probe is subjected to median filtering to obtain a median-filtered image; The preprocessed probe grayscale image is obtained by subtracting the median filtered image from the probe grayscale image.

5. A method for identifying probe tips in a microscopic image according to any one of claims 1-3, characterized in that, Connected regions whose edge morphology matches the probe tip shape are selected from the binary image of the tip features as tip connected regions, including: The connected regions with the minimum bounding moment and an aspect ratio less than a preset ratio and a pixel area within a preset threshold range are obtained from the binary image of the needle tip feature as the needle tip connected regions.

6. The method for identifying probe tips in a microscopic image according to claim 1, characterized in that, Generating needle tip coordinates based on the aforementioned connected component of the needle tip includes: Obtain the minimum bounding moment of the connected region of the needle tip; The center coordinates of the minimum enclosing moment are used as the needle tip coordinates.

7. The method for identifying probe tips in a microscopic image according to claim 1, characterized in that, Generating needle tip coordinates based on the aforementioned connected component of the needle tip includes: The connected region of the needle tip is fitted with a circle, and the coordinates of the circle center are used as the coordinates of the needle tip.

8. A probe station, characterized in that, The probe tip is identified using the probe tip identification method in a microscopic image as described in any one of claims 1-7.

9. A probe tip identification device in a microscopic image, characterized in that, include: The acquisition module is used to acquire the probe grayscale image; A conversion module is used to binarize the probe grayscale image based on a first threshold to obtain a highlight feature binary image, which is used to characterize the central region of the probe tip; to binarize the probe grayscale image based on a second threshold to obtain an overall feature binary image, which is used to characterize the overall region of the probe tip; to obtain a highlight feature connected component from the highlight feature binary image; to select a pixel coordinate from the highlight feature connected component; to obtain an overall feature connected component from the overall feature binary image; and to draw a probe tip feature binary image by obtaining an overall feature connected component containing the pixel coordinate and having a pixel area greater than a second preset area; wherein, the first threshold is greater than the second threshold; the first threshold is used to characterize the brightness of the central region when the probe tip is imaged, and the second threshold is used to characterize the brightness of the edge region when the probe tip is imaged. The filtering module is used to filter out connected regions whose edge morphology conforms to the probe tip shape from the binary image of the tip feature as the tip connected regions; A generation module is used to generate needle tip coordinates based on the needle tip connected domain.

10. An electronic device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement a probe tip identification method in a microscopic image as described in any one of claims 1-7.

11. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement a probe tip identification method in a microscopic image as described in any one of claims 1-7.

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