Visual saliency prediction has obtained a significant popularity these years but the majority research is for static saliency prediction. An approach to detect dynamic saliency of videos is proposed in this paper, which exploits a spatial-temporal fusion way. Spatial saliency is detected by a trained convolutional neutral network, and we use a larger convolutional kernel for some layers in our network because saliency is influenced by global contrast according to visual psychology. While temporal saliency is extracted by optical flow and we combine it with K-means cluster, which brings a more accurate result. In addition, the two are fused in an optimal weighted way. Our experiments on DIEM datasets outperforms compared to four other dynamic saliency models on two metrics.
In this paper, a method for measuring the non-uniform refractive index field based on the light field imaging technique is proposed. First, the light field camera is used to collect the four-dimensional light field data, and then the light field data is decoded according to the light field imaging principle to obtain image sequences with different acquisition angles of the refractive index field. Subsequently PIV (Particle Image Velocimetry) technique is used to extract ray offset of each image. Finally, the distribution of non-uniform refractive index field can be calculated by inversing the deflection of light rays. Compared with traditional optical methods which require multiple optical detectors from multiple angles to synchronously collect data, the method proposed in this paper only needs a light field camera and shoot once. The effectiveness of the method has been verified by the experiment which quantitatively measures the distribution of the refractive index field above the flame of the alcohol lamp.
Feature correspondence is one of the essential difficulties in image processing, given that it is applied within a wide range in computer vision. Even though it has been studied for many years, feature correspondence is still far from being ideal. This paper proposes a multigeometric-constraint algorithm for finding correspondences between two sets of features. It does so by considering interior angles and edge lengths of triangles formed by third-order tuples of points. Multigeometric-constraints are formulated using matrices representing triangle similarities. The experimental evaluation showed that the multigeometric-constraint algorithm can significantly improve the matching precision and is robust to most geometric and photometric transformations including rotation, scale change, blur, viewpoint change, and JPEG compression as well as illumination change. The multigeometric-constraint algorithm was applied to object recognition which includes extraprocessing and affine transformation. The results showed that this approach works well for this recognition.
The increased desire for visible light/infrared dual band imaging systems with compact structure, high resolution coverage and without significantly complicates the optical design is increasing. Such systems are particularly desirable for aerial imaging applications where increased levels of target reconnaissance, precision target location and designation are required in cost-effective and light weight systems. Therefore, in this paper, a high resolution Electro-optical/infrared (EO/IR) system to simultaneously operate in the visible band and short wave infrared band (SWIR) band using ZEMAX software are presented. It has a common aperture using Ritchey-Chrétien (R-C) system and the spectrum is separated using a dichroic beam splitter. The results of the optical design indicated that our proposed approach meets the technical performance requirements for high image performance and compact size. This system can be applied in aerial remote sensing applications.
In general practical applications, the point spread function (PSF) of the imaging system, the imaging process, and the
observation noise, are unknown a priori information. Therefore, the identification of the PSF is a challenging and
difficult problem in the world. The algorithm of identification of the PSF and the restoration of the blurred images based
on the priori blur models (known as the PBM algorithm) is proposed. In practical application, the priori models of the
PSF mainly consist of the linear motion blur, out of focus blur and the Gaussian blur. In the situation of that the
degradation process is formed by the one of the above point spread functions, the PSF can be formulated in parametric
model. The corresponding parameters of the model are determined by the algorithm proposed in this paper. Thus, the
PSF is obtained according to the parameter of the model consequently. First, the parameter changing scope and the
increment step length of the parameters are provided based on the original guess. Second, the criterion that the Euclid
length of the difference between the observed image and blurred image corresponding to the PSF is minimized is
incorporated in order to determine the parameter of the PSF. Therefore, the PSF is identified by the parametric model
and the original image is estimated via the ordinary image restoration algorithms. In this paper, we applied the Wiener
filter to restore the original images. The experimental results show that the identified result of the PSF is reliable and
accurate and the restoration effect with the identified PSF is better when the observed image have high signal to noise
A satellite navigation performance evaluation and test system framework structure based on simulation has been
conceived and developed targeting the windows operating system, so as to extract and analyze he key performance of
satellite navigation system The evaluation methods of Ionospheric model errors, Space vehicles (SVs) clock errors and
Ephemeris prediction errors were presented. While testing physical receiver performance, the mathematical simulation
satellite navigation subsystem except receiver was used to help. It can provide references for satellite navigation
performance evaluation and test.
This paper makes improvements on the algorithm of identifying blur support size proposed by Li Chen in blind image
restoration to accommodate to the moderate/intense noise circumstance. The prior method mainly constructs a whiten
filter referring to the image characteristics based on ARMA (Autoregressive Moving Average) model, and calculates the
correlation of whiten filtered image with different shifts, thus gets the estimated blur size equal to the shifts at the
minimum of correlation. However, this method is difficult to accurately identify blur size even with moderate additive
noise. Some procedures are taken before calculating the correlation to ameliorate the estimation accuracy, including
regarding edge neighborhoods as valid regions for calculating correlation in the whiten filtered image and implementing
filtering to further reduce noise interference. Experimental results represent that the correlation attains its minimum when
the shift distance meets certain relationship with actual blur size, thus show the improvements are valid for the case of
A closed-form maximum entropy image restoration method is proposed in this paper. First, the least square solution in the presence of measurement error is found by Weiner filter. Then a small perturbation from the solution is determined in the null space of the blur matrix by maximum entropy criterion. The closed-form least square solution and its perturbation are deserved which means no iterative, less complexity and rapid processing. Compared with Weiner filter, the method proposed is also a stabilizing algorithm, and more details can be deserved due to the introduced perturbation.