Infrared imaging system has been used widely for its ability of working day and night. The performance of dim target detection is one important performance guideline for many applications, such as surveillance system, alarm system, searching and rescuing system, and so on. Multi-spectral infrared image fusion could help to get more information by complementing difference from different wave band images. One method was proposed to realize the dim target detection based on image fusion by using one character filter in transformational domain in the process of image fusion to enhance target and constrain background in fused image. The experimental results demonstrate that proposed method increased the ratio of target-to-background in fused image, and improved the probability of target detection and reduced the probability of false alarm on noisy infrared images.
More and more infrared imaging systems were applied in military and civil fields and the performance of detecting dim
target is one important index for infrared imaging system. The performance of the method based on background
suppression is declined with many false alarms and even loss target in image with clutter background. In order to
improve the performance of dim target detection, the method base on target enhance is developed. In this paper, after
analyzing the grey value distribution of target, background and noise in local area, the pixel's shape parameter was
defined, maximum strategy was used to obtain one size of pixel's shape parameter, and enumerate strategy was used to
search the possible target size. And then, one character filter based on shape parameter was designed to enhanced target
and suppress clutter background. Compared with the method based on background suppression, experimental results
show that proposal method is effective to enhance target and suppress clutter background.
More and more infrared imaging systems were applied in military and civil fields due to the ability of detecting in
night. Performance of detecting dim target is one important index for infrared imaging system. But it is difficult to detect
dim target from infrared image due to weak in luminous and small in sizes. An important approach based on background
suppression was used in many applications for it was effective in most applications and computed easily and quickly. But
the performance of target detection were descended with many numbers of false alarms when there exists noise pixels in
infrared image. In order to resolve the problem induced by noise pixels, in this paper a modify detection model was
proposed. Modify detection model use two step filter processes to suppress noise and estimate background image. After
selecting the first filter operator, a detection system base on modify detection model was realized. Experiment results
show that modify detection model improve target detection ratio and reduce the numbers of false alarm on noisy infrared
images.
The estimation of the blurred parameters is the key to recover the blurred image. In the rotational motion blurred image,
the track of blurred is arc and the blurred is not identical in the whole image space. The method based on LOG-polar
transform introduced by this paper firstly transform the blurred track from arc to linear, and then estimate the parameter
of the rotational motion image with the method based on self-correlation. The experiments showed that the proposal
method was effective to estimate the parameter with good precision.
The spatial non-uniformity in the photo-response of the detectors strongly influences the quality of infrared image. There
was no one effective means to evaluate the quality of infrared imaging systems and the performance of the NUC
algorithms. A real-time testing system was designed and implemented, which could be used to acquire the data from
infrared imaging system, measure the system and analyze the data, and provide researching platform both on software
and hardware for developing the infrared processing system. The experimental results show that the system implemented
acquiring infrared data in real-time with high speed and high precision. The system has been applied and it was helpful
for the researcher to exploit and improve the NUC algorithms.
We propose an adaptive model update mechanism for face tracking based on mean-shift, we employ the Kalman filter to
predict a proper original position for mean shift tracking algorithm. To overcome the problem of appearance change, an
adaptive modal update is introduced. We classify the occlusion problems into two main cases specified as partial occlusion
and complete occlusion according to the number of similar sub blocks between object and candidate. We fuss Kalman
predictor into Mean-shift tracker in case of partial occlusion, for case of full occlusion, we divide object and candidate into
four parts respectively, according to the previous exact tracking result, we compute the average velocity of the target, and
then check the condition for face reappearing, with which we present an efficient target search strategy to deal with full
occlusion. Various tracking sequences demonstrate the superior behavior of our tracker and its robustness to appearance
changes and occlusions.
Peaks extraction is a kind of post-process in many image application or vision tasks that can be used for finding the
optimum solution in the solution space. In this paper a real time method is proposed. A candidate queue is first build for
containing highest peaks in the image in ascending order. Then the image is scanned in sequence. At each scanning
position every candidate in the queue is updated respectively by some criterions given in this paper. After the image is
scanned over, the highest peaks in the image is achieved in the queue. All the process can be accomplished by logic
circuit, so the method is very suitable for hardware system such as FPGA and so on.
Background constructing and updating are the two key problems for the background subtraction method used to the
detection of the moving target. In the paper, confident exponent is proposed to construct the original background, and an
adaptive strategy is applied to update the background in time. This method was applied on tracking the car in the exam of
driver's license. The experiments showed that this method was very promising and could overcome the background
change and target change.
This paper presents a fast hierarchical knowledge-based approach for automatically detecting multi-scale upright faces in still color images. The approach consists of three levels. At the highest level, skin-like regions are determinated by skin model, which is based on the color attributes hue and saturation in HSV color space, as well color attributes red and green in normalized color space. In level 2, a new eye model is devised to select human face candidates in segmented skin-like regions. An important feature of the eye model is that it is independent of the scale of human face. So it is possible for finding human faces in different scale with scanning image only once, and it leads to reduction the computation time of face detection greatly. In level 3, a human face mosaic image model, which is consistent with physical structure features of human face well, is applied to judge whether there are face detects in human face candidate regions. This model includes edge and gray rules. Experiment results show that the approach has high robustness and fast speed. It has wide application perspective at human-computer interactions and visual telephone etc.
This paper introduces principal component analysis into matching and correlation tracking, and presents a matching algorithm based on principal component analysis. This matching method can bear some image distortions in the image matching and visual tracking. Experimental results are presented to support it.
This paper proposed a method to segment the texture image based on multiscale analysis of wavelet transform. The features at different scales need study when the number of the textures is to be determined. The proposed method firstly transform the texture image with wavelet transform, then extract texture features on different scales and different frequency, and perform the coarse segmentation on those different channels of the same scale and the different scales. At last, the coarsely segmented results at same scale are incorporated together, followed by a inter-scale fusion procedure. Promising results have been achieved.
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