The invention relates to a method for improving target detection performance by improving target classification and positioning accuracy. The method is characterized by extracting image characteristics according to a convolution neural network framework, selecting the first M layers of convolution layers to be output and carrying out characteristic fusion so as to form a multi-characteristic characteristic graph; carrying out grid division on the convolution layer M, and predicting target candidate frames with the fixed number and size in each grid; mapping the candidate frames to the characteristic graph for cutting, and carrying out multi-characteristic connection on cutting results; and passing the above results through a whole connection layer, classifying the image characteristics through the Softmax classification algorithm and using the overlapping area loss function to carry out online iteration regression positioning so as to obtain the final target detection result. According to the invention, the method is properly designed; characteristics are extracted through the convolution neural network, and multilayer fusion is performed on the image characteristics; and finally, the Softmax classification algorithm is used for classifying the image characteristics and the overlapping area loss function is used to carry out positioning, so a good target detection result is acquired.

Method for improving target detection performance by improving target classification and positioning accuracy

The invention relates to an image automatic cutting method The method comprises the following steps: extracting an aesthetic response graph and a gradient energy graph of an image to be cut; intensively extracting candidate cutting images from the image to be cut; based on the aesthetic response graph, screening the candidate cutting images; and based on the aesthetic response graph and the gradient energy graph, estimating composition fractions of screened candidate cutting images and determining that a candidate cutting image with the highest score is a cutting image According to the scheme, an aesthetic influence area of a picture is studied by use of the aesthetic response graph, an aesthetic reservation portion is determined by use of the aesthetic response graph, high aesthetic quality of the cutting image is better reserved to be greatest extent, at the same time, a gradient distribution rule is analyzed by use of the gradient energy graph, and the composition fraction of the cutting image is evaluated based on the aesthetic response graph and the gradient energy graph The image automatic cutting method provided by the embodiment of the invention makes up for the defect of image composition expression and solves the problem of how to improve the robustness and precision of image automatic cutting

Image automatic cutting method

The invention provides a video scaling method based on block segmentation and frame-by-frame optimization and relates to common image data processing. The method is characterized by segmenting a videosequence into N video subsequences according to shot cut, and carrying out scaling and optimization on the video subsequences in the horizontal direction and vertical direction respectively. The method comprises the following steps: inputting a video, and carrying out shot segmentation according to shot switching frames; tracking a video main body target in each video subsequence to obtain a video trajectory tracking box of the corresponding video subsequence; uniformly dividing the whole frame of video into x column-wise sub-blocks; merging labels and finally obtaining a frame of video imageframe fj_pt and a frame of shadow image frame gj_pt; carrying out frame-by-frame optimization based on scaling proportion; scaling a protection area and a non-protection area in the video image framefj_pt and the shadow image frame gj_pt according to the proportion respectively; and carrying out frame-by-frame optimization based on the main body target position. The method overcomes the defectsof video jitter and serious inconsistency between the target image and an original image in video scaling in the prior art.

Video scaling method based on block segmentation and frame-by-frame optimization

The invention relates to an image zooming method, relating to a method of applying electronic equipment to carry out image zooming The method is an image fast zooming method based on a threshold and a probability and is divided into two processes which comprise (A) a radial basis function neural network model training process and (B) the zooming process of a tested image with the need of zoom According to the invention, a method of using a radial basis function neural network to carry out machine learning is used to calculate a threshold, the image with the need of zooming is divided into a protection area and a non protection area, the random zoom with the probability is used in zooming, and a defect that the guarantee of an image zoom effect and the satisfaction of a real-time image zoom speed can not be achieved at the same time in the prior art is overcome

Image zooming method

Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on real-world data and situations. In this paper, we revisit the IQA research, and point out an ignored but interesting problem in IQA: the coarse-grained (i.e., when quality variation is sufficiently big, as the setting of most IQA databases up to date) statistical results evaluated on existing databases mask the fine-grained differentiation. Accordingly, we present a survey on image quality assessment from a new perspective: fine-grained image quality assessment (FG-IQA). Recent FG-IQA research on five major kinds of images is introduced, and some popular IQA methods are analyzed from FG-IQA perspective. The potential problems for current IQA research based on existing coarse-grained databases are analyzed and the necessity of more FG-IQA research is justified. Finally, we discuss some challenges and possible directions for future works in FG-IQA. 

Fine-Grained Image Quality Assessment: A Revisit and Further Thinking

The invention relates to an image scaling method based on content awareness, and relates to graphic image conversion in an image plane. An energy function is obtained by adopting a hybrid feature model fusing a salient map, an edge line map and a gradient map, and according to the energy function, a line clipping operation is carried out to complete image scaling. The image scaling method comprises the steps of: inputting a color image to carry out preprocessing; simultaneously carrying out extraction of the salient map and a salient target image of an original color image, extraction of the edge map of a gray scale image, which is fused with line information, and extraction of the gradient map of the gray scale image; fusing three feature maps by utilizing an HFPM (hybrid feature model) algorithm so as to obtain the energy function; and clipping the original image by using a line clipping algorithm. The method disclosed by the invention overcomes the defects that an existing line clipping method adopts a gradient map definition energy function of an image and still causes distortion and loss of partial image information in the image scaling process.

Image scaling method based on content awareness

The invention relates to a method for identifying a face micro expression image sequence of a monitoring video sequence and relates to a graph identification method of application electronic equipment The method comprises steps that Euler amplification of the monitoring video face micro expression image sequence is carried out; a monitoring video face micro expression image is pre-processed; dynamic space-time texture characteristics of the face micro expression image sequence is extracted through utilizing an SMTCTP-WTOP operator; an SVM classifier is utilized to carry out training and prediction, and identification of the face micro expression image sequence of the monitoring video sequence is accomplished The method is advantaged in that a problem of low identification rate caused by influence of illumination, shielding objects and noise on identification of the face micro expression image in the prior art is solved

Method for identifying face micro expression image sequence of monitoring video sequence

In targeting the low correlation between existing image scaling quality assessment methods and subjective awareness, a content-aware retargeted image quality assessment algorithm is proposed, which is based on the structural similarity index. In this paper, a similarity index, that is, a local structural similarity algorithm, which can measure different sizes of the same image is proposed. The Speed Up Robust Feature (SURF) algorithm is used to extract the local structural similarity and the image content loss degree. The significant area ratio is calculated by extracting the saliency region and the retargeted image quality assessment function is obtained by linear fusion. In the CUHK image database and the MIT RetargetMe database, compared with four representative assessment algorithms and other latest four kinds of retargeted image quality assessment algorithms, the experiment proves that the proposed algorithm has a higher correlation with Mean Opinion Score (MOS) values and corresponds with the result of human subjective assessment.

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Zhang Tingting

Papers

Image scaling method based on content awareness

Method for identifying face micro expression image sequence of monitoring video sequence

Content-Aware Retargeted Image Quality Assessment