The detection of shadow is the first step to reduce the imaging effect that is caused by the interactions of the light
source with surfaces, and then shadow removal can recover the vein information from the dark region. In this paper, we
have presented a new method to detect the shadow in a single nature image with the saliency map and to remove the
shadow. Firstly, RGB image is transferred to 2D module in order to improve the blue component. Secondly, saliency
map of blue component is extracted via graph-based manifold ranking. Then the edge of the shadow can be detected in
order to recover the transitional region between the shadow and non-shadow region. Finally, shadow is compensated by
enhancing the image in RGB space. Experimental results show the effectiveness of the proposed method.
In planetary or lunar landing missions, hazard avoidance is critical for landing safety. Therefore, it is very important to correctly detect hazards and effectively find a safe landing area during the last stage of descent.
In this paper, we propose a passive sensing based HDA (hazard detection and avoidance) approach via descent images to lower the landing risk. In hazard detection stage, a statistical probability model on the basis of the hazard similarity is adopted to evaluate the image and detect hazardous areas, so that a binary hazard image can be generated. Afterwards, a safety coefficient, which jointly utilized the proportion of hazards in the local region and the inside hazard distribution, is proposed to find potential regions with less hazards in the binary hazard image. By using the safety coefficient in a coarse-to-fine procedure and combining it with the local ISD (intensity standard deviation) measure, the safe landing area is determined. The algorithm is evaluated and verified with many simulated descent downward looking images rendered from lunar orbital satellite images.
Images are usually degraded by haze and fog due to atmospheric absorption and scattering. Especially for remote sensing, degraded images which suffer from haze will highly probably lose detailed information. Nowadays researches on haze removal mainly focus on visible images. However, as infrared imaging is more and more widely used in many fields, a comprehensive study of the effects of fog on infrared images is urgent. In this paper, we firstly introduce that the obvious attenuation effects of fog on infrared images by analyzing the mid-infrared images captured under different weather conditions. Therefore, we propose a unified dehazing approach for infrared images. The proposed approach mainly includes three steps. The first step is to obtain the local transmission of the original hazed images by using the statistical prior knowledge known as “dark channel” which is similar to visible images. With local transmission, the refined transmission map is estimated in a soft matting framework. Due to atmospheric scattering model, dehazed infrared images can be recovered finally. From experimental results, it is quantitatively demonstrated that the proposed approach can significantly improve the quality of hazed images by using principle component analysis technique.
Aiming at the problem of tracking 3D target in forward-looking infrared (FLIR) image, this paper proposes a high-accuracy robust tracking algorithm based on SIFT and particle filter. The main contribution of this paper is the proposal of a new method of estimating the affine transformation matrix parameters based on Monte Carlo methods of particle filter. At first, we extract SIFT features on infrared image, and calculate the initial affine transformation matrix with optimal candidate key points. Then we take affine transformation parameters as particles, and use SIR (Sequential Importance Resampling) particle filter to estimate the best position, thus implementing our algorithm. The experiments demonstrate that our algorithm proves to be robust with high accuracy.
Frost is a kind of ground coagulation phenomena, and if the temperature of dew point is below 0Co , the water vapor
condenses as solid, which is called frost. The frost phenomena observing is an important step in daily ground observation
work, and the results is one of 36 critical data in meteorological observation field. This work is usually accomplished by
manual. In this paper, we propose an effective method for frost observation based on image processing. The changing of
frost formation process is well simulated by using the curve fitting of gray correlation coefficient between certain lengths
of frames, while the characteristic of frost surface texture is also well described by texture analysis based on texture
descriptor. The experiment results show that our method can get high detection accuracy in the different kinds of
continuous changing environment.
SIFT tracking algorithm is an excellent point-based tracking algorithm, which has high tracking performance and
accuracy due to its robust capability against rotation, scale change and occlusion. However, when tracking a huge 3D
target in complicated real scenarios in a forward-looking infrared (FLIR) image sequence taken from an airborne moving
platform, the tracked point locating in the vertical surface usually shifts away from the correct position. In this paper, we
propose a novel algorithm for 3D target tracking in FLIR image sequences. Our approach uses SIFT keypoints detected
in consecutive frames for point correspondence. The candidate position of the tracked point is firstly estimated by
computing the affine transformation using local corresponding SIFT keypoints. Then the correct position is located via
an optimal method. Euclidean distances between a candidate point and SIFT keypoints nearby are calculated and formed
into a SIFT-based distance histogram. The distance histogram is defined a cost of associating each candidate point to a
correct tracked point using the constraint based on the topology of each candidate point with its surrounding SIFT
keypoints. Minimization of the cost is formulated as a combinatorial optimization problem. Experiments demonstrate
that the proposed algorithm efficiently improves the tracking performance and accuracy.
We present an algorithm for infrared target recognition based on edge features, aiming to improve the performance of
recognition within low contrast and low SNR infrared images. The main process including: using nonlinear anisotropic
diffusion equation to smooth noise as preprocessing after enhancing the image contrast through equalizing the image
gray-level histogram, so as to filter out noise as well as maintain edge and shape features; then considering the greater
differences between the target template and the fragmentary edges extracted directly from infrared images, Otsu
threshold segmentation algorithm is selected to extracted the region feature before we use Canny operator to extract
target edge features; finally, we match the target edge template with infrared images to recognize the target, using
Hausdorff distance as a matching measure. Through experiment on several infrared image sequences, the results show
that comparing with other preprocessing algorithms, our method achieve better performance for target recognition.
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