An image segmentation method is provided for a computing device. The method includes obtaining a general tumor image, performing tumor localization on the tumor image to obtain a candidate image indicating a position of a tumor region in the general tumor image, inputting the candidate image to a cascaded segmentation network constructed based on a machine learning model, and performing image segmentation on the general tumor region in the candidate image using a first-level segmentation network and a second-level segmentation network in the cascaded segmentation network to obtain a segmented image.

Embodiments of this application disclose methods, systems, and devices for video synthesis. In one aspect, a method comprises obtaining a plurality of frames corresponding to source image information of a first to-be-synthesized video, each frame of the source image information. The method also comprises obtaining a plurality of frames corresponding to target image information of a second to-be-synthesized video. For each frame of the plurality of frames corresponding to the target image information of the second to-be-synthesized video, the method comprises fusing a respective source image from the first to-be-synthesized video, a corresponding source motion key point, and a respective target motion key point corresponding to the frame using a pre-trained video synthesis model, and generating a respective output image in accordance with the fusing. The method further comprises repeating the fusing and the generating steps for the second to-be-synthesized video to produce a synthesized video.

An image processing method includes: acquiring an image frame sequence, including a to-be-processed image frame and one or more image frames adjacent thereto, and performing image alignment on the to-be-processed image frame and each of image frames in the image frame sequence to obtain multiple pieces of aligned feature data; determining, based on the multiple pieces of alignment feature data, multiple similarity features each between a respective one of the multiple pieces of aligned feature data and aligned feature data corresponding to the to-be-processed image frame, and determining weight information of each of multiple pieces of aligned feature data based on the multiple similarity features; and fusing the multiple pieces of aligned feature data according to the weight information to obtain fusion information of the image frame sequence, the fusion information being configured to acquire a processed image frame corresponding to the to-be-processed image frame.

A fundus image processing method comprising: receiving a collected fundus image; identifying the fundus image via a first neural network to generate a first feature set of the fundus image; identifying the fundus image via a second neural network to generate a second feature set of the fundus image, wherein the first feature set and the second feature set indicate different lesion attributes of the fundus image; combining the first feature set and the second feature set to obtain a combined feature set of the fundus image; and inputting the combined feature set into a classifier to obtain a classification result.

The embodiments of the disclosure provide a video description information generation method, a video processing method, and video description information generation apparatus, and a video processing apparatus. The video description information generation method includes: obtaining a frame-level video feature sequence corresponding to a video; generating a global part-of-speech sequence feature of the video according to the video feature sequence, the global part-of-speech sequence feature being a feature of a sequence of a combination of parts of speech in the video; and generating natural language description information of the video according to the global part-of-speech sequence feature and the video feature sequence.

The present invention extends to methods, systems, and computer program products for classifying time series image data. Aspects of the invention include encoding motion information from video frames in an eccentricity map. An eccentricity map is essentially a static image that aggregates apparent motion of objects, surfaces, and edges, from a plurality of video frames. In general, eccentricity reflects how different a data point is from the past readings of the same set of variables. Neural networks can be trained to detect and classify actions in videos from eccentricity maps. Eccentricity maps can be provided to a neural network as input. Output from the neural network can indicate if detected motion in a video is or is not classified as an action, such as, for example, a hand gesture.

An optoelectronic sensor for detecting objects in a monitored zone is provided having at least one light receiver for generating measurement data from received light from the monitored zone and having a safe evaluation unit that has at least two processing channels for a redundant processing of the measurement data and having a comparison unit for comparing processing results of the processing channels to uncover errors in a processing channel 30a-b. The processing channels are here each configured for a determination of a signature with respect to their processing results; and the comparator unit is configured for a comparison of the signatures.

Embodiments of this application disclose methods, systems, and devices for image region localization and medical image processing. In one aspect, a method comprises acquiring three-dimensional images of a target body part of a patient. The three-dimensional images comprise a plurality of magnetic resonant imaging (MRI) modalities. The method comprises registering a first image set of a first modality with a second image set of a second modality. After the registering, image features of the three-dimensional images are extracted. The image features are fused to obtain fused features. The method also comprises determining voxel types corresponding to voxels in the three-dimensional images according to the fused features. The method also comprises selecting, from the three-dimensional images, target voxels having a preset voxel type, obtaining position information of the target voxels, and localizing a target region within the target body part based on the position information of the target voxels.

The present disclosure relates a method for fingerprint image enhancement comprising applying a first low pass filter and a first weight to raw fingerprint image data to produce a first filtered fingerprint image data set. Applying a second low pass filter and a second weight to the raw fingerprint image data to produce a second filtered fingerprint image data set. Filter coefficients of the second filter are different from filter coefficients of the first filter. The first filtered fingerprint image data set and the second filtered fingerprint image data set are combined to produce a final enhanced fingerprint image. The disclosure also relates to a fingerprint sensing system and to an electronic device comprising a fingerprint sensing system.

A method for detecting boxes includes receiving a plurality of image frame pairs for an area of interest including at least one target box. Each image frame pair includes a monocular image frame and a respective depth image frame. For each image frame pair, the method includes determining corners for a rectangle associated with the at least one target box within the respective monocular image frame. Based on the determined corners, the method includes the following: performing edge detection and determining faces within the respective monocular image frame; and extracting planes corresponding to the at least one target box from the respective depth image frame. The method includes matching the determined faces to the extracted planes and generating a box estimation based on the determined corners, the performed edge detection, and the matched faces of the at least one target box.