Ricoh Company, Ltd.

Inspur (Beijing) Electronic Information Industry Co., Ltd

Lingyan Liang

Ying Zhao

Liyan Liu

Gang Wang

Yiwei Wang

Yaqian Zhao

Gang Dong

Qichun Cao

Wenfeng Yin

An image processing method, apparatus and device, and a readable storage medium are disclosed, including: obtaining a target image; inputting the target image into a quantized target deep neural network model for classification/detection to obtain an output result; and processing the target image according to a policy corresponding to the output result. A process of performing quantization to obtain the target deep neural network model includes: obtaining a pre-trained floating point type deep neural network model; extracting weight features of a deep neural network model; determining a quantization policy using the weight features; and quantizing the deep neural network model according to the quantization policy to obtain the target deep neural network model.

Image processing method, apparatus and device, and readable storage medium

Image processing method and device are disclosed. The image processing method is inclusive of a step of obtaining a target image; a step of performing motion estimation on the target image so as to attain motion paths; a step of determining, on the basis of the motion paths, whether there exists a fluctuation meeting a predetermined requirement in the target image; and a step of taking, if there exists a fluctuation meeting the predetermined requirement in the target image, the target image as a reference image. The reference image is utilized to carry out inter frame motion estimation with respect to the respective images acquired.

Image processing method and device and non-transitory computer-readable medium

Panoramic video processing method and device are disclosed. The method includes steps of receiving a panoramic video waiting for processing; conducting inter frame motion estimation in regard to any two neighboring 2D panoramic images in the panoramic video so as to obtain an inter frame motion estimation result between the same two neighboring 2D panoramic images; acquiring, based on the respective inter frame motion estimation results, a historical motion trajectory of the panoramic video, and carrying out smoothing with respect to the historical motion trajectory; calculating, based on the historical motion trajectory after smoothing, a rotation matrix for motion compensation pertaining to each 2D panoramic image in the panoramic video; and performing reconstruction on each 2D panoramic image in the panoramic video by means of the corresponding rotation matrix for motion compensation so as to attain a panoramic video after image stabilization, and outputting the panoramic video after image stabilization.

Panoramic video processing method and device and non-transitory computer-readable medium

A moving object detection method includes acquiring two depth image frames including depth information, which are obtained by continuously taking images of a moving object, the two depth image frames including a present depth image frame and at least one past depth image frame; dividing each of the two depth image frames into a plurality of blocks; calculating differences between numbers of pixels positioned in respective different depth areas in each of the plurality of blocks in the present depth image frame, and numbers of pixels positioned in respective different depth areas in each of the corresponding plurality of blocks in each of the at least one past depth image frame, which correspond to the plurality of blocks in the present depth image frame; and detecting a moving block in the present depth image frame based on the calculated difference and constituting the detected moving object.

Moving object detection method and system

Disclosed is an object tracking method comprising steps of obtaining a grayscale image sequence and a depth image sequence; predicting a candidate target region of a tracking object in a current image; determining a depth weight image of the candidate target region; acquiring a target template which is expressed by a probability density distribution of features; expressing, by a probability density distribution of features, a candidate target whose position moves in the candidate target region; calculating, based on a probability density expression of the target template and a probability density expression of the candidate target, a similarity between the target template and the candidate target, so as to get a confidence level image of the candidate target region; and locating, based on the confidence level image of the candidate target region, a position of the tracking object in the current image.

Object tracking method and device

Disclosed is a method comprising steps of conducting image preprocessing so as to respectively obtain a candidate object region including a foreground image in a depth map and a color image; determining whether it is necessary to conduct a region growing process with respect to the candidate object region of the depth map; in the former case, conducting the region growing process with respect to the candidate object region of the depth map; and conducting, after the region growing process is conducted with respect to the candidate object region of the depth map, a depth value restoration process with respect to a candidate region.

Depth value restoration method and system

A method and an apparatus for tracking an object are disclosed. The method includes determining a first position by a first tracking template and determining a second position by a second tracking template, the first and second tracking templates being formed based on first and second feature sets, respectively, the first feature set being different from the second feature set, and the first feature set and the second feature set including one or more features; and determining a final position based on the first position and the second position, wherein the first tracking template is updated for each of a predetermined number of frames, the second tracking template is updated based on a predetermined rule, the second tracking template and the first tracking template are independently updated, and the update frequency of the second tracking template is lower than the first tracking template.

Growing community of inventors

Beijing, China

Lingyan Liang