A method is presented. The method includes determining a number of landmarks in an image comprising multiple pixels. The method also includes determining a number of channels for the image based on a function of the number of landmarks. The method further includes determining, for each one of the number of channels, a confidence of each pixel of the multiple pixels corresponding to a landmark. The method still further includes identifying the landmark in the image based on the confidence.

Provided by the present disclosure are a method and device for removing handwritten content from a text image, and a storage medium. The method for removing handwritten content from a text image comprises: acquiring an input image of a text page to be processed, wherein the input image comprises a handwritten area, and the handwritten area comprises the handwritten content; using an image segmentation model to recognize the input image so as to obtain an initial handwritten pixel of the handwritten content; performing blurring processing on the initial handwritten pixel to obtain a handwritten pixel mask area; determining the handwritten content according to the handwritten pixel mask area; and removing the handwritten content from the input image to obtain an output image.

Provided by the present disclosure are a method and device for removing handwritten content from a text image, and a storage medium. The method for removing handwritten content from a text image comprises: acquiring an input image of a text page to be processed, wherein the input image comprises a handwritten area, and the handwritten area comprises the handwritten content; using an image segmentation model to recognize the input image so as to obtain an initial handwritten pixel of the handwritten content; performing blurring processing on the initial handwritten pixel to obtain a handwritten pixel mask area; determining the handwritten content according to the handwritten pixel mask area; and removing the handwritten content from the input image to obtain an output image.

In a sample observation device, an image acquisition unit 6 acquires a plurality of pieces of image data of a sample in a Y-axis direction, and an image generation unit generates luminance image data on luminance of the sample on the basis of the plurality of pieces of image data, binarizes luminance values of each of the plurality of pieces of image data to generate a plurality of pieces of binarized image data, and generates area image data on an existing area of the sample on the basis of the plurality of pieces of binarized image data.

In a sample observation device, an image acquisition unit 6 acquires a plurality of pieces of image data of a sample in a Y-axis direction, and an image generation unit generates luminance image data on luminance of the sample on the basis of the plurality of pieces of image data, binarizes luminance values of each of the plurality of pieces of image data to generate a plurality of pieces of binarized image data, and generates area image data on an existing area of the sample on the basis of the plurality of pieces of binarized image data.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

An apparatus includes a memory and a processor. The memory may be configured to store image data of an input image. The processor may be configured to detect one or more objects in the input image using a quantized multi-stage object detection network, where quantization of the quantized multi-stage object detection network includes (i) generating quantized image data by performing a first data range analysis on the image data of the input image, (ii) generating a feature map and proposal bounding boxes by applying a region proposal network (RPN) to the quantized image data, (iii) performing a region of interest pooling operation on the feature map and a plurality of ground truth boxes corresponding to the proposal bounding boxes generated by the RPN, (iv) generating quantized region of interest pooling results by performing a second data range analysis on results from the region of interest pooling operation, and (v) applying a region-based convolutional neural network (RCNN) to the quantized region of interest pooling results.

An apparatus includes a memory and a processor. The memory may be configured to store image data of an input image. The processor may be configured to detect one or more objects in the input image using a quantized multi-stage object detection network, where quantization of the quantized multi-stage object detection network includes (i) generating quantized image data by performing a first data range analysis on the image data of the input image, (ii) generating a feature map and proposal bounding boxes by applying a region proposal network (RPN) to the quantized image data, (iii) performing a region of interest pooling operation on the feature map and a plurality of ground truth boxes corresponding to the proposal bounding boxes generated by the RPN, (iv) generating quantized region of interest pooling results by performing a second data range analysis on results from the region of interest pooling operation, and (v) applying a region-based convolutional neural network (RCNN) to the quantized region of interest pooling results.

A system for automated comprehensive assessment of clinical lumbar MRIs includes a MRI standardization component that reads MRI data from raw lumbar MRI files, uses an artificial intelligence (AI) model to convert the raw MRI data into a standardized format. A core assessment component automatically generates MRI assessment results, including multi-tissue anatomical annotation, multi-pathology detection and multi-pathology progression prediction based on the structured MRI data package. The core assessment component contains a semantic segmentation module that utilizes a deep learning artificial intelligence (AI) model to generate an MRI assessment results that contains multi-tissue anatomical annotation, a pathology detection module to generate multi-pathology detection, and a pathology progression prediction module to generate multi-pathology progression prediction. A model optimization component archives clinical MRI data and MRI assessment results based on comments provided by a specialist, and periodically optimizes the AI deep learning model of the core assessment component.

A system for automated comprehensive assessment of clinical lumbar MRIs includes a MRI standardization component that reads MRI data from raw lumbar MRI files, uses an artificial intelligence (AI) model to convert the raw MRI data into a standardized format. A core assessment component automatically generates MRI assessment results, including multi-tissue anatomical annotation, multi-pathology detection and multi-pathology progression prediction based on the structured MRI data package. The core assessment component contains a semantic segmentation module that utilizes a deep learning artificial intelligence (AI) model to generate an MRI assessment results that contains multi-tissue anatomical annotation, a pathology detection module to generate multi-pathology detection, and a pathology progression prediction module to generate multi-pathology progression prediction. A model optimization component archives clinical MRI data and MRI assessment results based on comments provided by a specialist, and periodically optimizes the AI deep learning model of the core assessment component.