Texture features and stability have attracted much attention in the field of biometric recognition. The inner-knuckle print is unique and not easy to forge, so it is widely used in personal identity authentication, criminal detection, and other fields. In recent years, the rapid development of deep learning technology has brought new opportunities for internal-knuckle recognition. We propose a deep inner-knuckle print recognition method named LSKNet network. By establishing a lightweight Siamese network model and combining it with a robust cost function, we can realize efficient and accurate recognition of the inner-knuckle print. Compared to traditional methods and other deep learning methods, the network has lower model complexity and computational resource requirements, which enables it to run under lower hardware configurations. In addition, this paper also uses all the knuckle prints of four fingers for concatenated fusion recognition. Experimental results demonstrate that this method has achieved satisfactory results in the task of internal-knuckle print recognition.
As a standard light source, medium and low temperature blackbody is widely used in the field of quantitative infrared remote sensing, so it is urgent to improve its calibration accuracy. This paper studies the calibration method of medium and low temperature blackbody radiation temperature based on standard transfer detector, and designs a dual channel thermal infrared transfer standard radiometer. The radiometer adopts optical path structure with achromatic off-axis reflection, its optical mechanical system is placed in liquid nitrogen refrigeration vacuum Dewar, and an optical chopper is used to improve the systematic noise ratio. Finally, through the correction experiment of commercial blackbody radiation temperature, we found that there is a certain deviation at different temperature points, and its maximum value is 3.22k. The results prove the effectiveness of the design.
Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. The experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion.
Polarization imaging is another photoelectric imaging detection technology. It has obvious technical advantages in revealing camouflage, penetrating haze, and getting target details. It can gain multiple polarization features images and achieve target detection and recognition through specific polarization information analysis methods such as synthesis and fusion. Because there is a mis-match problem between the polarization features images, polarization image registration performs first. However, existing methods such as mutual information registration and related registration methods are hard to solve the problem of mis-match because of distortion of the polarization imaging lens. In this paper, we present a matching optimization SIFT polarization image registration algorithm found on the standard SIFT registration algorithm. In the sub-matching description, a reversed matching is added, that is, matching in both directions performs to form a symmetrical matching. In the matching set of positive and negative directions, matched feature points pairs satisfying both sets extract. The pair of matching points are only when the pair of feature points are the best matching points. This increases the matching accuracy of feature points and reduces the mismatching rate of descriptions. At the same time, numbers of feature points add in the algorithm using the gray leveling method. Registration experimental results show the registration accuracy of this method is better than the mutual information registration method.
From image processing angle, the research on the infrared characteristics of burning decoy based on sets of measured infrared images is developed. After that, the infrared imaging model of the decoy is constructed. At last the lifetime infrared images of decoy are generated. In order to achieve the above objectives, firstly, an accurate segmentation and extraction for burning area, radiation area and burning trail of decoy is accomplished; secondly, the infrared imaging model of the decoy based on lifetime, size and gray of each part of the decoy is constructed; lastly, the infrared images of decoy are simulated by combination of billboard texture mapping technology and particle modeling. This paper provides the method combining Billboard and particle system with the trajectory of nodes, the system can enhance the precision of characteristics simulation and motion simulation for the infrared decoy, thus increase the real-time ability.
The difficult point that impacting the traditional polarization image registration based on Fourier - Mellin
transform is calculating rotation angle of the image α and shrinkage variable coefficient τ of polarization
image registration .Based on Fourier - Mellin transform method, this paper proposes a improved algorithm that
on account of improving resolution in the θ coordinate direction in the Log-polar coordinate system, improve the
calculation accuracy of rotation angle of the image α and shrinkage variable coefficient τ .Through the
experiment and simulation results show that the improved algorithm than the traditional has higher registration
precision.
Polarization information parsing plays an important role in polarization imaging detection. This paper focus on the
polarization information parsing method: Firstly, the general process of polarization information parsing is given, mainly
including polarization image preprocessing, multiple polarization parameters calculation, polarization image fusion
and polarization image tracking, etc.; And then the research achievements of the polarization information parsing method
are presented, in terms of polarization image preprocessing, the polarization image registration method based on the
maximum mutual information is designed. The experiment shows that this method can improve the precision of
registration and be satisfied the need of polarization information parsing; In terms of multiple polarization parameters
calculation, based on the omnidirectional polarization inversion model is built, a variety of polarization parameter images
are obtained and the precision of inversion is to be improve obviously; In terms of polarization image fusion , using
fuzzy integral and sparse representation, the multiple polarization parameters adaptive optimal fusion method is given,
and the targets detection in complex scene is completed by using the clustering image segmentation algorithm based on
fractal characters; In polarization image tracking, the average displacement polarization image characteristics of
auxiliary particle filtering fusion tracking algorithm is put forward to achieve the smooth tracking of moving targets.
Finally, the polarization information parsing method is applied to the polarization imaging detection of typical targets
such as the camouflage target, the fog and latent fingerprints.
This paper puts forward a infrared polarization image fusion algorithm by using Contourlet transform and 2-D Teager operator. First of all, based on Contourlet transform , infrared polarization image has been decomposed into low frequency and high frequency . Using Teager operator to process the high frequency sub-bands, and then select by different regional characteristics based on maximum contrast ratio. As for the low frequency sub-bands, judged by energy, selects the weighted weights to fuse the image . At last, we get the experimental results and our analysis. The results of the experiment show that compared with the traditional, the algorithm in this paper can give better characterization of polarization remote sensing image edge and texture information, has a higher resolution, and also improves the visual effect of the polarized remote sensing image.
As to extract better texture from an infrared polarization image, variational decomposition has a good effect which extracts texture in the premise of energy index. First this paper introduces description of infrared polarization image in Stokes vector and hierarchical variational decomposition (BV, Gp, L2) model. And we use this model method for multiscale texture extraction of the infrared polarization image. A given infrared polarization image is decomposed into the characteristics of the three different components of u, v, r through the minimization of the energy functional. In this decomposition, v represents the fixed scale texture of f , which is measured by the parameter λ. To achieve a multiscale representation, we proceed to capture essential textures of f which have been absorbed by the residuals. Then we make energy as an index of the texture evaluation. The experiments show that the algorithm is effective to extract better texture from an infrared polarization image.
KEYWORDS: Infrared radiation, Infrared imaging, Modulation transfer functions, Sensors, Infrared detectors, Target detection, 3D modeling, Atmospheric modeling, Atmospheric optics, Signal to noise ratio
A rapid method to generate infrared images based on image synthesis is proposed in this paper. At first, a three-dimension geometric model of the airplane is created by 3DMax software. Infrared radiance model of the airplane in accordance with infrared radiation theory is established, and the impact of atmospheric attenuation is considered, then the infrared images of airplane are generated. Finally, the synthesis of the generated images and actual shooting background images is achieved. To improve simulation reliability and fidelity, several aspects are thought in this paper for the synthesis, they are the atmospheric effect, the optical of imaging system effect, the random noise of detector, the synthesis revision of generated image and actual shooting background image. Experiment show that the simulation credibility is improved obviously, and the synthesis speed is advanced to 100 frames per second. The running environment is: PC, 512MB of RAM, 1.60GHz of CPU frequency. This method will be reference for testing and evaluating infrared search and track system.
Polarization imaging provides abundant information of object, i.e. surface roughness, texture, physical and chemical characters. Independently, intensity and polarimetric features give incomplete representations of an object of interest. These representations are complementary, and it is expected that the combination of complementary information will reduce false alarms, improve confidence in target identification, and improve the quality of the scene description. Polarization parameter images include the degree of polarization, the angle of polarization, azimuth angle etc. There are not only strong correlations between polarization parameter images, but also different characters, which gives image fusion challenges, namely, how to find the optimal polarization parameter image to take part in image fusion with intensity image. This paper presents a polarization image fusion method based on choquet fuzzy integral. Using this algorithm the best polarization parameter image and intensity image are fused, and the fusion result is evaluated. The experiments show that this method could automatically select the best polarization parameter images from multi-polarization parameters image, the resulting images can yield more detail and higher contrast, and can reduce the noise effectively. It is conducive to the subsequent target detection.
In this paper we analyse the polarization imaging theory and the commonly process of the polarization imaging detection. Based on this, we summarize our many years’ research work especially in the mechanism, technology and system of the polarization imaging detection technology. Combined with the up-to-date development at home and abroad, this paper discusses many theory and technological problems of polarization imaging detection in detail from the view of the object polarization characteristics, key problem and key technology of polarization imaging detection, polarization imaging detection system and application, etc. The theory and technological problems include object all direction polarization characteristic retrieving, the optical electronic machinery integration designing of the polarization imaging detection system, the high precision polarization information analysis and the polarization image fast processing. Moreover, we point out the possible application direction of the polarization imaging detection technology both in martial and civilian fields. We also summarize the possible future development trend of the polarization imaging detection technology in the field of high spectrum polarization imaging. This paper can provide evident reference and guidance to promote the research and development of the polarization imaging detection technology.
Study the laser pulses transmission time characteristics in discrete random medium using the
Monte Carlo method. Firstly, the medium optical parameters have been given by OPAC software. Then, create
a Monte Carlo model and Monte Carlo simulation of photon transport behavior of a large number of tracking,
statistics obtain the photon average arrival time and average pulse broadening case, the calculation result with
calculation results of two-frequency mutual coherence function are compared, the results are very consistent.
Finally, medium impulse response function given by polynomial fitting method can be used to correct discrete
random medium inter-symbol interference in optical communications and reduce the rate of system error.
Optical ranging is one of the most precise techniques for distance measurement. The effects of the
density variation of atmosphere, aerosols and clouds on optical ranging precision are generally
considered, a new method is proposed for calculating the ranging precision in the presence of aerosol
particles and clouds. The size distribution spectrum models for aerosols and clouds in the Optical
Properties of Aerosols and Clouds Package (OPAC) are adopted. Results show that aerosols and clouds
could introduce errors of several centimeters to several ten meters to the ranging. The relationship
between the ranging precision and the relative humidity, the zenith angle of ranging direction and the
optical wavelength is also analyzed. The ranging error doesn't have an obvious relationship with the
wavelength, but depends on the zenith angle, especially for the angle larger than 70 degree. The
ranging error depends on the relative humidity as well. The ranging error induced by aerosols increases
gradually with the increase of the relative humidity when the relative humidity is less than 80%, but it
increases rapidly when the relative humidity is larger than 80%. Our results could provide a theoretical
basis and reference for the application of optical ranging.
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