Numerous tracking-by-detection methods have been proposed for robust visual tracking, among which compressive tracking (CT) has obtained some promising results. A scale-adaptive CT method based on multifeature integration is presented to improve the robustness and accuracy of CT. We introduce a keypoint-based model to achieve the accurate scale estimation, which can additionally give a prior location of the target. Furthermore, by the high efficiency of data-independent random projection matrix, multiple features are integrated into an effective appearance model to construct the naïve Bayes classifier. At last, an adaptive update scheme is proposed to update the classifier conservatively. Experiments on various challenging sequences demonstrate substantial improvements by our proposed tracker over CT and other state-of-the-art trackers in terms of dealing with scale variation, abrupt motion, deformation, and illumination changes.
This paper presents a novel infrared and visual image registration method based on phase grouping and mutual information of gradient orientation. The method is specially designed for infrared image navigation, which is different from familiar multi-sensor image registration methods in the field of remote sensing. The central idea is to firstly extract common salient structural features from visual and infrared images through phase grouping, then registering infrared image to visual image and estimating the exterior parameters of the infrared camera. Two subjects are involved in this reports: (1) In order to estimate image gradient orientation accurately, a new method based on Leguerre-Gauss filter is presented. Then the image are segmented by grouping of pixels based on their gradient orientations and ling support regions are extracted as common salient structural features from infrared and visual images of the same ground scene. (2)In order for registering infrared and visual image, coordinate systems are constructed, coordinate transformations are formularized, and the new similarity measures based on orientation mutual information is presented. Quantitative evaluations on real and simulated image data reviews that the proposed method can provide registration results with improved robustness and accuracy.
As one of widely applied nonlinear smoothing filtering methods, median filter is quite effective for removing salt-andpepper noise and impulsive noise while maintaining image edge information without blurring its boundaries, but its computation load is the maximal drawback while applied in real-time processing systems. In order to solve the issue, researchers have proposed many effective fast algorithms and published many papers. However most of the algorithms are based on sorting operations so as to make real-time implementation difficult. In this paper considering the large scale Boolean calculation function and convenient shift operation which are two of the advantages of FPGA(Field Programmable Gate Array), we proposed a novel median value finding algorithm without sorting, which can find the median value effectively and its performing time almost keeps changeless despite how large the filter radius is. Based on the algorithm, a real-time median filter has been realized. A lot of tests demonstrate the validity and correctness of proposed algorithm.
An infrared target enhancement method based on optimization in the whole directional polarization is studied in this paper. By using the description relationship between the stokes vector of incident light and the intensity of emergent light, the analytical formula between the intensity of emergent light and the polarizing angle is deduced, and thus virtually derives the intensity of emergent light from 0°to 360° polarizing angle. Then according to the criterion of maximum contrast between target and background, the searching of optimal polarizing angle is iteratively realized, and finally gets the enhanced infrared target image. The feasibility and validity of the algorithm are validated by using real long wave infrared (LWIR) polarization images of target. Experimental results show that, the enhanced image using proposed algorithm possesses obvious suppression effect of background clutter, and the quantitative evaluation under two kinds of image quality evaluation indexes of average gradient and image entropy also validates the effectiveness of our algorithm in infrared target enhancement.
KEYWORDS: Laser range finders, Spherical lenses, 3D image processing, 3D modeling, Object recognition, Principal component analysis, Data modeling, Feature extraction, Error analysis, Lithium
The description of local surface features is a critical step in surface matching and object recognition. We present a descriptor for three-dimensional shapes based on the bispectrum of spherical harmonics (BSH). First, points in a support region of a feature point are used to construct a local reference frame, and a histogram is formed by accumulating the points falling within each bin in the support region. Second, spherical harmonic coefficients of the histogram and its bispectrum are calculated. Finally, the feature descriptor is obtained via principal component analysis. We tested our BSH descriptor on public datasets and compared its performance with that of several existing methods. The results of our experiments show that the proposed descriptor outperforms other methods under various noise levels and mesh resolutions.
Liquid crystal optical phased array (LCOPA) is a kind of spatial light modulator(SLM) which is now widely studied in the field of laser radar, adaptive optics, optical information processing, etc. The calibration of the voltage-phase characteristic of a LCOPA is an important step which will seriously affect the performance of a LCOPA. Firstly, derived the relationship between the phase distribution of the emergent light and light intensity in the far-field. Designed an optic path to calibrate the voltage-phase characteristic. And built up a observation equation. Introduced a weight matrix to reduce the errors caused by the impact of phenomena such as the fly-back. Proposed a new calibration algorithm based on the measurement of light intensity. A checkerboard pattern with a period of M pixels per check was used in the calibration routine. Fix the control voltage of one region, and change the voltage in another region. The light pattern in the far-field changes with the control voltage. Measure the intensity of the light beam at the center of the far-field. Then, obtain the raw data. Filter and normalize the raw data. And calculate the phase difference between two regions. Use weighted least square method to get the relationship between the control voltage and the phase retardation. Lastly, using this method to calibrate a LCOPA which is produced by BNS corp.
In this paper, by using the theoretical analysis and computer simulation method, the lower boundary requirements of the infrared imaging sensor are analyzed when detecting the thermal surface features of the wake behind a moving underwater body. Firstly, the computer simulation model of the underwater body wake’s surface temperature field and the corresponding surface infrared features are established. Secondly, the measures of the sensor’s detection performance and the computation method of these measures are described. Thirdly, by using the infrared features’ simulation model and the performance measures, we have done simulation tests to analyze the given infrared imaging sensor’s detection ability for detecting underwater body wake, and the requirements of infrared sensor to detect the wake’s infrared surface feature under some given working states are also investigated. Lastly, the simulation results and the conclusions of the paper are given. The problem-solving flow chart and the simulation results on underwater object wake’s detectability given in this paper may be useful for the designment and performance evaluation of the infrared imaging sensors.
The criminal’s fingerprints often refer to those fingerprints that are extracted from crime scene and have played an important role in police’ investigation and cracking the cases, but these fingerprints have features such as blur, incompleteness and low-contrast of ridges. Traditional fingerprint enhancement and identification methods have some limitations and the current automated fingerprint identification system (AFIS) hasn’t not been applied extensively in police’ investigation. Since the Gabor filter has drawbacks such as poor efficiency, low preciseness of the extracted ridge’s orientation parameters, the enhancements of low-contrast fingerprint images can’t achieve the desired effects. Therefore, an improved Gabor enhancement for low-quality fingerprint is proposed in this paper. Firstly, orientation image templates with different scales were used to distinguish the orientation images in the fingerprint area, and then orientation parameters of ridge were calculated. Secondly, mean frequencies of ridge were extracted based on local window of ridge’s orientation and mean frequency parameters of ridges were calculated. Thirdly, the size and orientation of Gabor filter were self-adjusted according to local ridge’s orientation and mean frequency. Finally, the poor-quality fingerprint images were enhanced. In the experiment, the improved Gabor filter has better performance for low-quality fingerprint images when compared with the traditional filtering methods.
Infrared small target detection is a crucial and yet still is a difficult issue in aeronautic and astronautic applications. Sparse representation is an important mathematic tool and has been used extensively in image processing in recent years. Joint sparse representation is applied in dual-band infrared dim target detection in this paper. Firstly, according to the characters of dim targets in dual-band infrared images, 2-dimension Gaussian intensity model was used to construct target dictionary, then the dictionary was classified into different sub-classes according to different positions of Gaussian function’s center point in image block; The fact that dual-band small targets detection can use the same dictionary and the sparsity doesn’t lie in atom-level but in sub-class level was utilized, hence the detection of targets in dual-band infrared images was converted to be a joint dynamic sparse representation problem. And the dynamic active sets were used to describe the sparse constraint of coefficients. Two modified sparsity concentration index (SCI) criteria was proposed to evaluate whether targets exist in the images. In experiments, it shows that the proposed algorithm can achieve better detecting performance and dual-band detection is much more robust to noise compared with single-band detection. Moreover, the proposed method can be expanded to multi-spectrum small target detection.
The task of small target detection is to extract the small targets from the background image including clutter, noise and jitter background, so it is difficult to deal with. In this paper, after analyzing infrared small targets, noise and clutter model, we use a small window median filter to estimate the infrared background. Then using background cancelling method, that is, subtracting the estimated background from the source image, the resident image can be obtained. Finally, an adaptive threshold is used to segment the residual image to obtain the potential targets. Considering the computational load, the two-dimensional filter is simplified into a one-dimensional filter. Experimental results show that the algorithm achieved good performance and satisfy the requirement of real-time processing of large size infrared image.
This paper presents a novel image feature extraction algorithm based on multiple ant colonies cooperation. Firstly, a low resolution version of the input image is created using Gaussian pyramid algorithm, and two ant colonies are spread on the source image and low resolution image respectively. The ant colony on the low resolution image uses phase congruency as its inspiration information, while the ant colony on the source image uses gradient magnitude as its inspiration information. These two ant colonies cooperate to extract salient image features through sharing a same pheromone matrix. After the optimization process, image features are detected based on thresholding the pheromone matrix. Since gradient magnitude and phase congruency of the input image are used as inspiration information of the ant colonies, our algorithm shows higher intelligence and is capable of acquiring more complete and meaningful image features than other simpler edge detectors.
KEYWORDS: Temperature metrology, Black bodies, Infrared radiation, Signal attenuation, Distortion, Pyrometry, Infrared imaging, Rockets, Missiles, Signal detection
Temperature is an important feature of infrared targets. However, due to the attenuation and distortion parameters in radiation transmission process are unknown, precise temperature measurement is a difficult task. In this paper, a modified Dual-Band Ratio (DBR) temperature measurement method for remote target is proposed. The method is based on a new presented variation derived from the temperature change process named Dual-Band Differential Ratio (DBDR). Firstly, the temperature of the target is estimated by the traditional DBR method, and then a correction using DBDR information is carried out to improve the measurement accuracy. Experiment results showed that the proposed method can improve the temperature measurement accuracy and it could also be carried out without any prior information about the target.
After a deep study of the principle of infrared polarization imaging detection, the infrared polarization information of target and background is modeled. Considering the partial polarized light can be obtained by the superposition of natural light (unpolarized light) and linearly polarized component while ignoring the component of circularly polarized light, and combing with the degree of polarization (DOLP) and the angle of polarization (AOP), the infrared polarization information is expressed by the multiplying of an intensity factor by a polarization factor. What we have modeled not only can be used to analyze the infrared polarization information visually and profoundly, but also make the extraction of polarized features convenient. Then, faced with different application fields and based on the model, a target information enhancement program is proposed, which is achieved by extracting a linear polarization component in a certain polarized direction. This program greatly improves the contrast between target and background, and can be applied in target detection or identification, especially for camouflage or stealth target. At last, we preliminarily tested the proposed enhancement method exploiting infrared polarization images obtained indoor and outdoor, which demonstrates the effectiveness of the enhancement program.
It is a difficult point to detect and recognize artificial targets under the disturbance of the complex ground clutter when remote sensing and detection to the earth. Using the different polarization information between artificial object and natural scenery, the ability to distinguish artificial targets from natural scenery can be promoted effectively. On account that the differences of polarization characteristics is an important factor in designing the target recognition method, this paper focuses attention on the application of remote sensing and reconnaissance and makes detailed research on the long wave infrared polarization characteristics of several typical metallic targets, such as aluminum plate and iron plate and the aluminum plate that be coated with black paint or yellow green camouflage. Then, the changing rules of the degree and angle of the long wave infrared polarization changing with the measurement temperature are analyzed and researched. Work of this paper lays the theoretical foundation for the design of remote sensing and detection system based on the infrared polarization information in the future.
The study of moving target detection has high research value and wide developing perspective. Considering of real-time detection of typical moving ground targets, a novel algorithm is proposed, which is based on background estimation via using Gaussian mixture model and reference background frame updating. Firstly the image gray of the target and background is supposed to obey Gaussian distribution, then the whole image is divided into three Gaussian distribution and estimated to form the reference image, finally detection results can be obtained via subtracting the reference image from current frame image. At the mean time the reference image is updated with time to keep the adaptability of the background image. Experimental results show that the algorithm is effective for moving ground targets such as vehicle.
KEYWORDS: LIDAR, Computer simulations, Detection and tracking algorithms, Optical simulations, 3D acquisition, 3D image processing, 3D modeling, Image processing, Motion models, Signal processing
Scanning Laser Radar has been widely used in many military and civil areas. Usually there are relative movements between the target and the radar, so the moving target image modeling and simulation is an important research content in the field of signal processing and system design of scan-imaging laser radar. In order to improve the simulation speed and hold the accuracy of the image simulation simultaneously, a novel fast simulation algorithm is proposed in this paper. Firstly, for moving target or varying scene, an inequation that can judge the intersection relations between the pixel and target bins is obtained by deriving the projection of target motion trajectories on the image plane. Then, by utilizing the time subdivision and approximate treatments, the potential intersection relations of pixel and target bins are determined. Finally, the goal of reducing the number of intersection operations could be achieved by testing all the potential relations and finding which of them is real intersection. To test the method’s performance, we perform computer simulations of both the new proposed algorithm and a literature’s algorithm for six targets. The simulation results show that the two algorithm yield the same imaging result, whereas the number of intersection operations of former is equivalent to only 1% of the latter, and the calculation efficiency increases a hundredfold. The novel simulation acceleration idea can be applied extensively in other more complex application environments and provide equally acceleration effect. It is very suitable for the case to produce a great large number of laser radar images.
Electronic digital image stabilization technique plays important roles in video surveillance or object acquisition.
Researchers have presented many useful algorithms, which can be classified to three kinds: gray based methods,
transformation based methods and feature based methods. When scenario is simple or flat, feature based methods
sometimes have imperfect results. Transformation based methods usually accompany large computation cost and high
computation complexity. Here we presented an algorithm based on gray projection which divided the whole image into
four sub-regions: the upper one, the bottom one, the left one and the right one. For making the translation estimation
easier, a central region is also chosen. Then the gray projections of the five sub-regions were counted. From the five pairs
of gray projections five group offsets including rotation and translation were obtained via cross correlation between
current frame and reference frame gray projections. Then according to the above offsets, the required parameters can be
estimated. The expected translation parameters(x axis offset and y axis offset) can be estimated via the offsets from the
central region image pair, the rotation angle can be calculated from the left four groups offsets. Finally, Kalman filter was
adopted to compute the compensation. Test results show that the algorithm has good estimation performance with less
than one pixel translation error and 10 percent rotation error. Based on this kind of gray projection algorithm, a real-time
electronic digital image stabilization system has been designed and implemented. System tests demonstrate the system
performance reaches the expected aim.
According to the imaging mechanism of the IR image, the histogram of the IR image, whose background is composed of the sea area and the sky area, usually has two apices and one vale. Based on this character of the histogram, the paper proposes a background suppression method, which combines the sea-sky area segmentation with the median filtering. Then, in order to reduce random noises in the image whose background has been suppressed, the local threshold-a digitizing method-has been showed in the paper. In this method, the image whose background has been suppressed is divided into some blocks, and the different block has the different threshold. The target motion is continuous and the noise motion is random, so the paper presents a sequential target detection method on the centroid track accumulation. A large number of experiments show that the methods presented in the paper can exactly detect the moving small target in the IR clutter background containing sea and sky areas.
Extracting Airport Runways in Infrared aerial images is a task of great important for many applications. Some traditional methods rarely give a complete outline of the desired structures. A new method has been proposed to extract the airport runways. It contains three steps: Firstly, we extract the straight lines form the original infrared aerial images. Secondly, we get rid of some short lines on the basis of rule-based knowledge that we have got form field experts. Thirdly, we confirm whether there is an airport according to the position interrelation among these straight lines and rule-based knowledge. If there is an airport, we shall extract the runway regions from it. Our techniques have been tested on several typical of infrared aerial images that contain airports. The experimental results show that all the airports have been found, and the runways have been extracted entirely. This algorithm also adapts to aerial optical images.
The background noise of the images from passive sensors normally is non-Gaussian, it is strong relativity in column direction. This paper will present an IR target's detection method using difference filter based on space difference to deal with such image data. From the simulations, we can find that this method is effective for the correlative background.
This paper will not only discuss the precision tracking using segmentation in infrared (IR) images, but also describe how to avoid using the a priori information in implementing the precision tracking with segmentation. The method presented above is not limited to single target case, it can be extended to multiple target tracking, too. For the two cases, we have done Monte Carlo simulations. The considerable simulation tests show that this tracking method is successful not only in recognizing and tracking targets automatically but also in tracking the specified target. Though the noise background is complicated, they all have good effect and high precision.
A real-time system has been developed for automatic target detection and tracking in infrared image sequence. The system comprises the algorithm software and a small processor using for image processing. The algorithm includes these techniques: space filtering; background cancelling technique; target detection; acquisition and precise tracking. The leading features of the system are: (1) High speed in seeking targets. (2) High detection probability. (3) Sub-pixel level of tracking precision. (4) The capability of working reliably and effectively in complicated background. In this paper, the theory and architecture of the system are proposed and the testing results in various situations are shown.
This paper describes a new detection approach for small moving objects in noisy image sequences. This algorithm consists of pre-detection and post-detection. The pre-detection algorithm uses a multiple-median filter and an adaptive thresholding to suppress background clutter and enhance small targets. Where an estimate of the local clutter and noise are first done, the clutter and noise estimate are then subtracted from the primary image data to yield residuals that are potential targets. Finally the adaptive thresholding is used to turn residual images into binary images. Post-detection is performed on the binary image sequences. The only a priori information required in the post-detection technique is the maximum velocity of objects in sequence images. It uses the temporal continuity of the trajectories of moving targets to enhance the probability of detection and suppresses the probability of false alarm. Some results on two-dimensional infrared image sequences are presented.
In this paper, we propose two adaptive filters - the multiple median related (MMR) filter and the least-mean- square-error (LMSE) filter. The MMR filter can reduce edge drifting and preserve more fine detail than the median filter. The LMSE filter, which is based on the local signal- to-noise ratio (SNR), combines with MMR filter, yielding a filter that can remove non-impulsive noise. Our experimental results the proposed algorithm is robust in preserving edges,e reducing edge drifting and suppressing mixed noise in low SNR images. In addition, the algorithm can be implemented by parallel architecture, which enables real- time filter processing of noise-corrupted images.
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