A method for point target enhancement based on temporal-spatial over-sampling and adaptive filtering is proposed in this paper. First of all, an over-sampling scanning imaging system is designed for target imaging enhancement. Two separate detector arrays are offset to each other by a half detector in the cross-scan direction and the sampling frequency in the in-scan direction is increased. A sub-pixel image of point target is obtained by interlace-combined two frame image from the two detector arrays. Secondly, image filtering is used to enhance the target embedded in clutter background. Clutter background suppression, local enhancement and contrast extension are performed. Experimental results show that the method presented in this paper can enhance the point target effectively in scanning detection system.
Ground target detection is very important in precise infrared imaging guidance. To address this problem, an accurate tracking algorithm of the key points, i.e., vertex of buildings is proposed. First, the feature points are extracted by Kanade-Lucas-Tomasi (KLT) algorithm, and the template of feature points is updated constantly in the tracking process according to the offset. Then based on the extracted feature points, the key point can be positioned using the geometric relation between the feature points and the key point. Third, the algorithm tracks the feature points and uses the geometric relation to track the key point in the next frame. The experimental results demonstrate the effectiveness of the proposed algorithm in tracking the key point of buildings in front-lower infrared image sequences for long time precise guidance.
Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti’s visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.
This paper mainly studies how to detect a wide variety of ships from the ship-borne infrared images in order to implement sea monitoring. Different types of ships have significant differences in their appearance. The traditional detection method which uses the global texture features of the object is not suitable to detect varied ships. This paper presents a novel detection algorithm which extracts spatial partial texture features trained by Adaboost to establish the ship model for detection. We first extract all the partial regions of the object through random traversal, and then extract the texture features by using the “Uniform LBP” operator. Compared to the traditional way, we save each partial feature individually as one feature vector, which not only reduces the vector dimension but also highlights the key regions when the partial regions with strong generality are selected by Adaboost at the second step. Finally, the selected partial features are boosted with weights to establish ship model for the ship detection. The proposed approach is efficient and robust in the infrared ship detection.
Object tracking in infrared image sequences is a challenging research topic due to the extremely low signal to noise ratio
of IR image. In this paper, a new tracking method based on multiple cues fusion particle filter framework is proposed. In
order to make full use of the object appearance information, both the spatial distribution and the gray distribution of the
object are considered in object modeling. Meanwhile, an affine transform model is used to estimate the motion of the
object which is integrated in the tracking framework. Firstly, the motion information is used to represent the state of each
particle. Secondly, each object is modeled by intensity template and gray histogram which are independent to each other.
The weights of the particles are obtained through the similarity of each feature model. Finally, to overcome the problems
relating to the changes in the object appearance, the object model is dynamically updated according to the tracking result
using kernel density estimation. It uses the complementarities of the two features to improve the reliability in tracking
task. The experimental results show that the fusion of multiple cues makes the tracking performance effective in infrared
A novel marker based watershed through image enhancement is proposed to segment the dim infrared target. The dim
infrared target is firstly enhanced by CB top-hat transformation and image quantization. Then, the accurate marker of the
target can be easily obtained through image binarisation and marker filtering. To calculate an efficient gradient image of
the dim target for the watershed segmentation, the gradient image is firstly calculated through Sobel operator and then
efficiently enhanced through pseudo top-hat transformation and gradient quantization. Because of the enhancement of
the dim target and the gradient image, the watershed can efficiently segment the dim infrared image. Experimental
results show that the proposed algorithm is much efficient for dim infrared target segmentation.
To reduce the influences of the dim target intensity and heavy clutter on infrared small target detection and tracking, a
novel algorithm is presented in this paper. The algorithm proposes a modified top-hat transformation by importing the
property of the small target region firstly, which largely enhances the dim target and apparently suppresses the heavy
clutter. Consequently, the potential targets are easy to be segmented by the iterative thresholding method. After
decreasing the false alarms through the dilation cumulation, the real target and the trajectory are correctly given by using
the data association formed by the motion property of the real target. Various experiments verified that the proposed
algorithm was efficient and robust for dim target detection and tracking under the condition of heavy clutter.