The invention relates to the technical field of image segmentation, and provides a portrait segmentation method and device. The portrait segmentation method comprises the following steps: preprocessing an original image to obtain a to-be-processed image; processing the to-be-processed image by using the trained portrait segmentation model; wherein the portrait segmentation model comprises an encoder and a decoder connected with the encoder, the encoder is used for extracting image features of a to-be-processed image by utilizing common convolution, depth separable convolution and hole convolution, and the decoder is used for generating a portrait segmentation mask according to the image features; and segmenting a portrait from the original image according to the portrait segmentation mask.The method does not depend on specific equipment and is wide in application range and low in implementation cost. Meanwhile, the method is small in calculation amount and high in portrait segmentation efficiency, and the segmentation result has good robustness.

Portrait segmentation method and device

The invention discloses a face image enhancement method and a corresponding computing device According to the face image enhancement method, the image quality enhancement is carried out through a trained image enhancement model A method for generating the image enhancement model comprises the steps of collecting a plurality of face images to generate a training set, wherein each face image is taken as a training image; inputting the training images in the training set into a pre-trained image enhancement model, carrying out at least one time of convolution-downsampling processing and at least one time of convolution-upsampling processing and generating output images, wherein the output images and the training images are the same in size; and optimizing the image enhancement model throughcomputing of the loss of the output images and target images, until a predetermined condition is satisfied, and obtaining the trained image enhancement model

Face image enhancement method and computing device

The invention discloses a method for generating a portrait segmentation model and a video key frame extraction method, and the method for generating the portrait segmentation model comprises the stepsof obtaining a training set which comprises a plurality of image groups consisting of a current frame image, a previous frame mask image, and a manually labeled current frame mask image; inputting anarray composed of the current frame of image and the previous frame of mask image into a pre-trained portrait segmentation model, the portrait segmentation model being an end-to-end model and comprising an encoding model and a decoding model, the encoding model being suitable for extracting features of the current frame of image from the array, and the decoding model being suitable for outputtingthe current frame of mask image based on the extracted features; and training the portrait segmentation model based on the current frame of mask image marked manually and the output current frame ofmask image to obtain the generated portrait segmentation model. According to the scheme, the video frames with large portrait motion difference can be extracted as the key frames based on the portraitmask image, so that the motion behavior of the figure is expressed very well.

A method for generating a portrait segmentation model and a video key frame extraction method

The invention discloses an image segmentation network generation method, an image segmentation method, a computing device and a mobile terminal for segmenting and processing a human image in a binocular image. The image segmentation network generation method comprises the following steps: a segmentation processing network is constructed; the segmentation processing network comprises a plurality ofprocessing blocks, a lower sampling layer and an upper sampling layer; the image segmentation processing network comprises a lower sampling layer and a mobile terminal. A parallax computing networkis constructed, the parallax computing network comprising a plurality of first convolution layers, transposed convolution layers and a second convolution layer; the segmentation processing network according to a first set of pre-generated image data is trained and = the parallax calculation network is trained according to a second set of pre-generated image data; the trained segmentation processing network and parallax computing network are fused to generate image segmentation network. The image segmentation network is trained according to the first set of image data such that an output of theimage segmentation network indicates a human image mask image corresponding to an input image.

Image segmentation network generation method and image segmentation method

An object tracking method comprises the steps of performing reconstruction of a low-resolution image for obtaining a high-resolution object image; training an object surface model according to the object image; acquiring a possible object position in the current frame according to the object surface model obtained through training; and determining the object position in the current frame. The invention further provides an object tracking system, a readable storage medium and electronic equipment. According to the object tracking method, the system thereof, the readable storage medium and the electronic equipment, a low-resolution image block can be converted to the high-resolution image, thereby facilitating reduction of a problem of information determining shortage of a traditional determining type tracker in a low-resolution scene, realizing object surface model updating according to the determined object position in the current frame and improving object tracking certainty.

Object tracking method, system thereof, readable storage medium and electronic equipment

The invention discloses a human image segmentation method. The method is suitable for being executed in a mobile terminal, and comprises the following steps of: segmenting a to-be-processed image by utilizing a predetermined segmentation network, wherein the predetermined segmentation network comprises an encoding stage and a decoding stage, the encoding stage comprises a first number of pairs ofconvolution layers and down-sampling layers, which are connected in sequence, and each pair of convolution layer and down-sampling layer forms a convolution-down-sampling pair; outputting a down-sampling characteristic pattern of the encoding stage after carrying out iteration through a first number of convolution-down-sampling pairs, wherein the decoding stage comprise a first number of pairs ofconvolution layer and up-sampling layers, which are connected in sequence, and each pair of convolution layer and up-sampling layer forms a convolution-up-sampling pair; and outputting a segmentationresult map after carrying out iteration through the first number of convolution-up-sapling pairs. The invention furthermore discloses the corresponding mobile terminal.

Human image segmentation method and mobile terminal

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Human image segmentation method and mobile terminal