In order to realize the machine-independent decision-making and control of ship intelligent navigation, this paper explores and builds a universal and practical ship-driving machine language system, analyzes the command requirements of driving behavior and ship state according to the three collision avoidance behaviors of ship navigation, and creatively designs the coding form of ship driving behavior index and ship state index by using 01 code and ILP method, so as to provide a man-machine and machine-machine collaborative dialogue for ship intelligent navigation. And the intelligent development of shipping provides important basic theory and technical support.
According to the actual needs of autonomous navigation and safety control of ships in open and busy waters, this study puts forward a complete technical chain of “autonomous navigation environment construction - collision avoidance and track control algorithm research - verification and incremental update of real ship data”, and gives the specific technical scheme and process route. That is to construct the virtual reality integrated simulation environment of ship autonomous navigation. Considering the multiple responses of large merchant ships, the ship handling model is coupled with the environment, and the ship autonomous collision avoidance algorithm model is established. By comparing the VDR data and simulation results, the algorithm and model are constantly updated, so as to finally realize the safe and efficient autonomous collision avoidance decision-making ability of ship intelligent navigation. The research results have important theoretical and technical support for expanding and improving the technical methods of ship navigation autonomous decision-making, and finally realizing the commercial application of ship intelligent navigation.
This paper presents a novel generative adversarial network for the task of human pose transfer, which aims at transferring the pose of a given person to a target pose. In order to deal with pixel-to-pixel misalignment due to the pose differences, we introduce an attention mechanism and propose Pose-Guided Attention Blocks. With these blocks, the generator can learn how to transfer the details from the conditional image to the target image based on the target pose. Our network can make the target pose truly guide the transfer of features. The effectiveness of the proposed network is validated on DeepFasion and Market-1501 datasets. Compared with state-of-the-art methods, our generated images are more realistic with better facial details.
Cloud and snow detection is one of the most important tasks in remote sensing (RS) image processing areas. Distinguishing cloud and snow from RS images is a challenging task. Short-wave infrared (SWIR) band has been widely used for ice/snow detection. However, due to the lack of SWIR in high-resolution multispectral images, such as ZY-3 satellite imagery, traditional SWIR-based methods are no longer practical. In order to mitigate the adverse effects of cloud and snow detection, in this work, we propose an effective convolutional neural network (CNN) with a multilevel/ scale feature fusion module (MFFM), a channel and spatial attention module, and an encoder-decoder network structure for cloud and snow detection form ZY-3 satellite imageries. The MFFM can aggregate multiple-level/scale feature maps from the backbone network, ResNet50, for providing representative semantic feature information for cloud and snow detection. Channel and spatial attention module (CSAM) is used to further refine the semantic feature maps that outputs by MFFM thus making the network have better detection performance. The encoder-decoder structure allows the proposed CNN to restore detailed object boundaries thus making the detection results more accuracy. Experimental results on the ZY-3 satellite imageries dataset demonstrate that the proposed network can accurately detect cloud and snow, and outperforms several state-of-the-art methods.
The point spread function (PSF) of imaging system with coded mask is generally acquired by practical measure-
ment with calibration light source. As the thermal radiation of coded masks are relatively severe than it is in
visible imaging systems, which buries the modulation effects of the mask pattern, it is difficult to estimate and
evaluate the performance of mask pattern from measured results. To tackle this problem, a model for infrared
imaging systems with masks is presented in this paper. The model is composed with two functional components,
the coded mask imaging with ideal focused lenses and the imperfection imaging with practical lenses. Ignoring
the thermal radiation, the systems PSF can then be represented by a convolution of the diffraction pattern of
mask with the PSF of practical lenses. To evaluate performances of different mask patterns, a set of criterion
are designed according to different imaging and recovery methods. Furthermore, imaging results with inclined
plane waves are analyzed to achieve the variation of PSF within the view field. The influence of mask cell size
is also analyzed to control the diffraction pattern. Numerical results show that mask pattern for direct imaging
systems should have more random structures, while more periodic structures are needed in system with image
reconstruction. By adjusting the combination of random and periodic arrangement, desired diffraction pattern
can be achieved.
Accurate Point Spread Function (PSF) estimation of coded aperture cameras is a key to deblur defocus images.
There are mainly two kinds of approaches to estimate PSF: blind-deconvolution-based methods, and
measurement-based methods with point light sources. Both these two kinds of methods cannot provide accurate
and convenient PSFs due to the limit of blind deconvolution or imperfection of point light sources. Inaccurate
PSF estimation introduces pseudo-ripple and ringing artifacts which influence the effects of image deconvolution.
In addition, there are many inconvenient situation for the PSF estimation.
This paper proposes a novel method of PSF estimation for coded aperture cameras. It is observed and verified
that the spatially-varying point spread functions are well modeled by the convolution of the aperture pattern
and Gaussian blurring with appropriate scales and bandwidths. We use the coded aperture camera to capture
a point light source to get a rough estimate of the PSF. Then, the PSF estimation method is formulated as the
optimization of scale and bandwidth of Gaussian blurring kernel to fit the coded pattern with the observed PSF.
We also investigate the PSF estimation at arbitrary distance with a few observed PSF kernels, which allows us to
fully characterize the response of coded imaging systems with limited measurements. Experimental results show
that our method is able to accurately estimate PSF kernels, which significantly make the deblurring performance
convenient.
Image denoising manages to recover a digital image from its noisy version by exploring the statistical features inside a
given noisy image. Most denoising methods perform well at low noise levels but lose efficiency at higher ones. In this
paper, we propose a novel image denoising method, which restores an image by exploiting the correlations between the
noisy image and the images retrieved from the cloud. Given a noisy image, we first retrieve relevant images based on
feature-level similarity. These images are then geometrically aligned to the noisy image to increase global statistical
correlation. Using the aligned images as references, we propose recovering the image with patch-level noise removal.
For each noisy patch, we first retrieve similar patches from the references and stack these patches (including the noisy
one) into a three dimensional (3D) group. We then obtain the noise free (NF) patches by collaborative filtering over the
3D groups. These recovered NF patches are aggregated together, producing the desired NF image. Experimental results
demonstrate that our scheme achieves significantly better results compared to state-of-the-art methods in terms of both
objective and subjective qualities.
KEYWORDS: Cameras, Video, Global Positioning System, Error analysis, Sensors, 3D acquisition, Digital imaging, Detection and tracking algorithms, Image processing, Digital cameras
Smartphones are becoming popular nowadays not only because of its communication functionality but also, more
importantly, its powerful sensing and computing capability. In this paper, we describe a novel and accurate image and
video based remote target localization and tracking system using the Android smartphones, by leveraging its built-in
sensors such as camera, digital compass, GPS, etc. Even though many other distance estimation or localization devices
are available, our all-in-one, easy-to-use localization and tracking system on low cost and commodity smartphones is
first of its kind. Furthermore, smartphones' exclusive user-friendly interface has been effectively taken advantage of by
our system to facilitate low complexity and high accuracy. Our experimental results show that our system works
accurately and efficiently.
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