A New Sequential Image Prediction Method Based on LSTM and DCGAN

Image recognition technology is an important field of artificial intelligence.Combined with the development of machine learning technology in recent years,it has great researches value and commercial value.As a matter of fact,a single recognition function can no longer meet people’s needs,and accurate image prediction is the trend that people pursue.This paper is based on Long Short-Term Memory(LSTM)and Deep Convolution Generative Adversarial Networks(DCGAN),studies and implements a prediction model by using radar image data.We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better.This new method enables effective learning of image features and makes predictive models to have greater generalization capabilities.Experiments demonstrate that our network model is more robust and efficient in terms of timing prediction than 3DCNN and traditional ConvLSTM.The sequential image prediction model architecture proposed in this paper is theoretically applicable to all sequential images.

Employment of predictive search algorithm in digital image correlation

A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.

Converting a Display Screen into a Touchscreen

A method is presented to convert any display screen into a touchscreen by using a pair of cameras. Most state of art touchscreens make use of special touch-sensitive hardware or depend on infrared sensors in various configurations. We describe a novel computer-vision-based method that can robustly identify fingertips and detect touch with a precision of a few millimeters above the screen. In our system, the two cameras capture the display screen image simultaneously. Users can interact with a computer by the fingertip on the display screen. We have two important contributions： first, we develop a simple and robust hand detection method based on predicted images. Second, we determine whether a physical touch takes places by the homography of the two cameras. In this system, the appearance of the display screen in camera images is inherently predictable from the computer output images. Therefore, we can compute the predicted images and extract human hand precisely by simply subtracting the predicted images from captured images.