IR Image Sequences Research Articles

Robust infrared small target detection with multi-feature fusion

The performance of the infrared search and track system depends on the robust of the infrared small target detection. In this paper, a novel small target detection with multi-feature fusion is proposed. At first, the salient proposal region is obtained with phase spectrum Fourier transform (PFT). Then, geometric moment is utilized to design an adaptive steerable edges awareness factor, which effectively eliminates the strong edge clutters. Afterwards, we adopt a three-layers local contrast structure and the difference-ration form is introduced to enhance the target and suppress the complex backgrounds. Finally, the adaptive threshold is applied to extract the target in the saliency map. Experiments are conducted on four real IR image sequences, which demonstrate that the proposed method outperforms the comparing methods with various complex backgrounds.

Comparing the efficiency of defect depth characterization algorithms in the inspection of CFRP by using one-sided pulsed thermal NDT

The efficiency of eight algorithms of defect depth characterization (pulse phase thermography – PPT, thermographic signal reconstruction by analyzing the first and second derivatives– TSR, early observation – EO, apparent thermal inertia – ATI, thermal quadrupoles - TQ, non-linear fitting - NLF and neural networks – NN) has been comparatively analyzed on both theoretical and experimental IR image sequences obtained in the inspection of CFRP composite. Synthetic noise-free image sequences have been calculated by means of the ThermoCalc-3D software, while experimental results have been obtained by applying a one-sided procedure of pulsed thermal NDT to the inspection of artificial defects in CFRP. A relative error in the evaluation of defect depth has been chosen as a figure of merit. It has been demonstrated that a simple and robust processing technique is the use of the Fourier transform resulting in phase-domain data (PPT). The technique of TSR ensures maximal values of signal-to-noise ratio and is less susceptible to uneven heating and lateral heat diffusion. The calculation of ATI has allowed the characterization of defects at depths up to 1.5 mm, but it is sensitive to uneven heating thus requiring to carefully choose a non-defect area. The EO method, as well as the technique of TQ, have revealed inferior results in defect depth identification because of a noisy character of raw signals. Non-linear fitting is a convenient processing technique allowing simultaneous characterization of some test parameters, such as material thermal properties, defect depth and thickness, etc., but this technique is time-consuming and can hardly be applied to full-format images. In the whole defect depth range, minimal characterization errors have been ensured by the use of the NN that is a promising tool for automated identification of hidden defects.

О восстановлении мезомасштабных структур полей скорости поверхностных течений Чёрного моря при помощи вариационной ассимиляции последовательностей спутниковых изображений NOAA AVHRR в ИК-диапазоне

О восстановлении мезомасштабных структур полей скорости поверхностных течений Чёрного моря при помощи вариационной ассимиляции последовательностей спутниковых изображений NOAA AVHRR в ИК-диапазоне

Automatic multiple moving humans detection and tracking in image sequences taken from a stationary thermal infrared camera

Several Particle Filter (PF)-based methods for human tracking in thermal IR image sequences have been proposed in the literature. Unfortunately, the majority of these methods are developed for tracking only a single human. Moreover, this human is manually pre-selected in the first frame of the image sequence, which is not practical for the real case of intelligent and efficient video surveillance system that needs tracking more than one human and without any external operator intervention. To contribute to addressing this need, in this paper, we propose a novel PF-based method that detects and tracks multiple moving humans using a thermal IR camera, without prior knowledge about their number and initial locations in the monitored scene. This method consists of three main parts. In the first one, all the moving objects are extracted from the image sequence by using the Gaussian Mixture Model (GMM) and then, for each extracted object, a combined shape, appearance, spatial and temporal-based similarity function that allows us to detect a human without any prior training of a mathematical model is calculated. The second part consists in tracking the human previously detected by using a PF and an adaptive combination of spatial, intensity, texture and motion velocity cues. In each cue, a model for the detected human is created, and when new observations arrive in the next frames, the similarity distances between each created model and the observed moving regions are calculated. The human tracking is achieved by combining individual similarity distances using adaptive weights, into a PF algorithm. The third part is devoted to detect and handle occlusions by using simple heuristic rules and grayscale Vertical Projection Histogram (VPH). Each part of the proposed method was separately tested on a set of real-world thermal IR image sequences containing background clutters, appearance and disappearance of multiple moving objects, occlusions, illumination and scales changes. A comparative study with several state-of-the-art methods has shown that the proposed method performs consistently better in terms of Center Location Error (CLE) and the Success Rate (SR), and it can also run at speed of about 15 Frame Per Second per human, which is considerably enough for real-time applications.

Putnikovye dannye dlya issledovaniya dinamiki poverkhnostnogo sloya Chernogo morya: al'timetriya na regulyarnoy setke i IK-izobrazheniya vysokogo razresheniya

Purpose. During more than 20 years, very detailed notions of the sea level variability in the World Ocean and its particular parts have been obtained based on satellite altimetry observations. Their advantage consists in possibility of a fairly rapid assessment of the surface currents’ velocities at synoptic scales. The alternative method for studying surface dynamics is estimation of the motions using a sequence of visible/infrared (IR) satellite images of the sea surface. The purpose of the present study is to compare the results obtained from application of two described methods used to analyze general circulation of the Black Sea surface layer. Methods and Results. The structure of the current fields in the northwestern Black Sea in winter, 1999 is investigated using the results of analysis of the IR image sequence from the NOAA/AVHRR sensors, as well as the gridded sea level anomaly data (processing level L4) and the along-track measurements (level processing L3) from the Copernicus Marine Environment Monitoring Service. The surface currents’ velocities are estimated based on the sea level field, which is calculated using two versions of mean dynamic topography. To compare the gridded altimetry and the results of the image sequence processing, a simple procedure is proposed for reconstructing the sea level using the current velocities’ components. The results of reconstructing the surface circulation features by two methods were compared and demonstrated, in particular, the anticyclonic eddy locations in the northwestern part of the Black Sea. It is noted that the locations of the eddy center in the sea level fields reconstructed from the altimetry data and by processing of the IR image sequence are different. Evolution of the eddy is investigated using the sea level anomaly data. It is shown that its motion is rather intermittent in time that can be a result of applying the procedure of optimal interpolation. Conclusions. It is noted that the gridded satellite altimetry product from the CMEMS, being applied to the Black Sea basin, should be used with due regard for the provided information on the mapping errors.

Improved Method of Study on the Photothermal Effect of Plasmonic Nanoparticles by Dynamic IR Thermography

Efficient heat generation by plasmon-resonant gold nanoparticles, together with their biocompatibility and high specificity of biomolecular recognition, opens new possibilities for applications in biomedical applications. In this work, we present an improved method of monitoring surface temperature changes subjected to external stimulation by dynamic IR thermography. The method is based on the careful analysis of an IR image sequence recorded before, during, and after the stimulation that allows one to select areas with significant temperature variation and evaluate temporal behavior of the surface temperature. The method was applied for the experimental study on the photothermal effect in a gold hydrosol containing hollow gold nanoparticles heated with laser beam. Under these conditions, it was seen that the surface temperature of the gold hydrosol (measured with a FLIR SC655 InfraRed Camera, resolution 640 × 480 pixels) under the laser beam gradually increases and reaches a saturation level. It was shown that the developed method is capable of producing a quantitative analysis of the changes in the surface temperature distribution of the gold hydrosol, as well as characterizing the photothermal properties of the nanoparticles.

Mobile thermal mapping for matching of infrared images with 3D building models and 3D point clouds

Two workflows for mobile thermal mapping including geometric camera calibration, automatic image orientation, 3D reconstruction and extraction of textures of building façades from thermal IR image sequences are introduced. In the first method, a coregistration of a terrestrial image sequence is done with a given 3D building model with the 3D building model being included in the image sequence orientation directly. Homologue image points are tracked through the sequence and checked whether they are on the 3D model façade. This is done to remove outliers and to refine the image sequence orientation and 3D reconstruction process as additional constraint in the bundle adjustment. The second method is used to match a high resoluted RGB-image based 3D point cloud and a TIR-image based 3D point cloud to assign thermal intensities to the dense RGB point cloud. The matching is done based on given pre-orientation of the two-point clouds performing Iterative Closest Point to minimise the distance of the two-point clouds. The corrected orientation is transferred to the orientation parameters of the TIR-images. The final image orientations are used to extract thermal textures for the 3D building model. Both methods are evaluated on their geometric accuracy using experimental image sequences.

Aspect ratio considerations for flat bottom hole defects in active thermography

Flaw detection capability of a Thermographic NDT system is often characterised using machined Flat-Bottom Holes (FBH) to simulate defects. For large diameter to depth (aspect) ratios, the FBH is typically indicated by the emergence of a localised hot spot in the IR image sequence. The FBH initially behaves identically to an extended adiabatic interface, allowing the centre-point depth to be determined using a one-dimensional model, before three-dimensional effects become dominant. However, for FBH with smaller aspect ratios, three-dimensional effects become dominant increasingly early, disrupting depth measurement using one-dimensional techniques. The dependence of these three-dimensional effects on aspect ratio is evident in the first and second logarithmic derivatives obtained through Thermographic Signal Reconstruction® (TSR®), when viewed using the Fourier number, and can be used to produce a simple model of FBH centre-point cooling behaviour. In both numerical modelling and experimental results, the maximum values of the logarithmic derivatives indicate the transition between one-dimensional adiabatic behaviour and the low aspect ratio regime, which occurs at an aspect ratio of ~9. At aspect ratios below 9, the one-dimensional depth measurement accuracy decreases proportionally with aspect ratio and the composition of a flaw cannot be reliably determined from a single pixel.

An Investigation on Eddy Current Pulsed Thermography to Detect Surface Cracks on the Tungsten Carbide Matrix of Polycrystalline Diamond Compact Bit

Polycrystalline diamond compact (PDC) bits are commonly used drill bits in the petroleum drilling industry. Cracks often occur on the surface of a bit, which may result in the unexpected suspension of the drilling operation, or even accidents. Therefore, the detection of surface cracks on PDC bits is of great importance to ensure continuous drilling operation and to prevent accidents. However, it is extremely difficult to detect such cracks by visual inspection or other traditional nondestructive testing (NDT) techniques due to the small size of cracks and the irregular geometry of bits. As one emerging NDT technique, eddy current pulsed thermography (ECPT) can instantly detect surface cracks on metal parts with irregular geometry. In this study, the feasibility of ECPT of detecting surface cracks on the tungsten carbide matrix of PDC bits was investigated. A successive scanning detection mode is proposed to detect surface cracks by using ECPT with a low power heating excitation unit and small-size coils. The influence of excitation duration on the detection result was also investigated. In addition, principal component analysis (PCA) was employed to process the acquired IR image sequences to improve detection sensitivity. Finally, the whole shape of a crack was restored with processed images containing varied cracks segments. Based on the experimental results, we conclude that the surface cracks on the tungsten carbide matrix of PDC bit can be detected effectively and conveniently by ECPT in scanning mode with the aid of PCA.

Moving Target Tracking in Infrared Image Sequences Based on Differential Kernel Covariance Descriptor

Forward looking infrared (FLIR) imaging has been used in many areas of research and everyday life, but it has been mostly employed in military and security domains. In these fields, remote infrared target tracking is a crucial element for surveillance. However, long-range captured IR image sequences generally have poor contrast, variable illumination, and high background clutter. These challenges make target tracking difficult. This paper suggests a technique for target tracking in different ranges in challenging FLIR image sequences, based on Differential Kernel Covariance Descriptor (DKCD). This new method diminishes rotation and illumination variation effects. The proposed technique calculates the differential kernel matrix of reference target by using various statistical and spatial features such as first and second derivatives, location information, and the intensity value of pixels. Later, the differential covariance matrix is constructed by using different pixel features and applying the appropriate kernel function to the matrix. Thanks to the kernel functions, the algorithm redefines the target's differential spatial features in Hilbert space. This process makes the descriptor non-linear. The predicted position of the target is calculated with the nearest neighbor algorithm in the candidate regions in the sub-frame. The performance of the suggested single target tracking system is then tested on challenging real-life video sequences.

Homography estimation for low-contrast IR image sequences utilizing GPS control points

Abstract Estimating the homographies for IR sequences of approaching objects in the infrared spectrum is a special problem. Due to the approaching movement of a helicopter towards measurement items, the objects are represented with few pixels in the beginning of the sequence and they are represented with a lot more pixels when reaching the destination area. This fact results in an assignment problem of finding point correspondences. This difficulty has led to drifting phenomena when using sophisticated methods for estimating the homographies. In this article a method is described which eliminates both the problem of finding point correspondences as well as the drifting issue. The presented procedure exploits information gathered by additional sensors (i. e., a GPS receiver, an inertial measurement unit (IMU), and a compass) for the computation of camera projections for each frame. The camera mappings are used to project GPS control points into the frames. The use of the same GPS points for each frame results in naturally given point correspondences. Using these correspondences the homographies can be estimated. The results are compared to the state of the art.

时空域非线性滤波红外序列图像非均匀性校正

时空域非线性滤波红外序列图像非均匀性校正

해양환경에서 강건한 물표 추적 알고리즘

본 논문에서는 해양환경에서 취득한 열상 영상에서 물표를 강건하게 탐지하고 추적하는 알고리즘을 제안한다. 먼저 2-D 이산 Harr 웨이블렛 변환(DHWT) 기법을 이용하여 수평, 수직에지를 얻은 다음 수직 및 수평 에지들을 서로 곱하여 하나의 영상으로 결합해 돌출지도를 생성한다. 그런 다음 돌출지도를 이진화하여 물표를 추출한다. 물표를 추적하기 위하여 인접한 프레임에 존재하는 물체간의 가중치가 부여된 유클리디언 거리를 정합척도로 사용하였으며, 정합결과에 대해 물체의 나타남, 사라짐, 잘못된 물체 추출 등을 고려한 궤적관리를 통하여 최종적인 물체 궤적을 얻는다. 실험결과는 제안하는 알고리즘이 물체를 강건하게 추적함을 보인다. In this paper, we proposed a robust object extraction and tracking algorithm in the IR image sequence acquired in the sea environment. In order to extract size-invariant object, we detect horizontal and vertical edges by using DWT and combine it to generate saliency map. To extract object region, binarization technique is applied to saliency map. The correspondences between objects in consecutive frames are defined by the calculating minimum weighted Euclidean distance as a matching measure. Finally, object trajectories are determined by considering false correspondences such as entering object, vanishing objects and false object and so on. The proposed algorithm can find trajectories robustly, which has shown by experimental results.

A single performance characteristic for the evaluation of seeker tracking algorithms

This paper presents a single numerical performance characteristic for the evaluation of seeker tracking algorithms. It concentrates on ship IR seeker tracking algorithms. Assessing the threat from guided missiles needs a sound evaluation of their performance. The main goal is to introduce a characteristic which is able to assess the threat for ships depending on various scenario parameters. It is shown that for these applications such a single characteristic is sufficient. In order to achieve this five popular tracking algorithms are used. Synthetic IR image sequences are generated to simulate a large set of attack approaches and assemble sufficient statistics on the behavior of the algorithms. The introduced characteristic can also be used for investigations on algorithms themselves, e.g. for sensitivity analyses and parameter optimization of a single algorithm, and for comparison of different algorithms.

A multistage target tracker in IR image sequences

In this paper, we propose a robust approach for tracking target in forward looking infrared imagery taken from an airborne moving platform. The tracker is proposed in a two stage algorithm with a coarse to fine manner. First, we introduce a coarse but quick algorithm to roughly locate the target, which based on a model matching procedure by computing the SIFT vector of the target. The raw position and the size of the target are then used to initialize the multistage tracking algorithm. Second, the target’s position is refined through a dedicated localization algorithm, which based on a HOG feature tracking procedure to overcome the rapid size and angel changes of the dim target in the IR image sequence. Finally, the target’s model can automatically been updated and utilized in the first stage. As confirmed by experimental results on a variety of IR image sequences, the proposed approach efficiently and robustly tracks target under challenging environmental conditions. Moreover, the speed of the multistage algorithm is also convincing in the tested IR imagery.

Application of L-K Optical Flow Algorithm Mixed with Pyramid on Calculating Water Surface Flow Field

A classical Lucas-Kanade optical flow algorithm was used to analysis the IR Image sequence of the wind-driven surface in this paper. Gaussian pyramid representation was introduced to retain both detail components and veracity for velocity field when considering the aperture problem and robustness. Three layers of pyramid for L-K optical flow is the best comparing with other layers (from one to four) in property. L-K optical flow algorithm mixed with pyramid representation shown an qualified power on calculating water surface flow field, demonstrated by optical flow fields on different wind speeds ( from 3m/s to 6m/s).

New Approach in Processing of the Infrared Image Sequence for Moving Dim Point Targets Detection

The development of an efficient moving target detection algorithm in IR-image sequence is considered one of the most critical research fields in modern IRST (Infrared Search and Track) systems, especially when dealing with moving dim point targets. In this paper we propose a new approach in processing of the Infrared image sequence for moving dim point targets detection built on the transformation of the IR-image sequence into 4-vectors for each frame in the sequence. The results of testing the proposed approach on a set of frames having a simple single pixel target performing a different motion patterns show the validity of the approach for detecting the motion, with simplicity in calculation and low time consumption.

An IR and visible image sequence automatic registration method based on optical flow

IR---visible camera registration is required for multi-sensor fusion and cooperative processing. Image sequences can provide motion information, which is useful for sequence registration. The existing methods mainly focus on registration using moving objects which are observed by both cameras. However, accurate motion feature extraction for a whole moving object is difficult, because of the complex environment and different imaging mechanism of two sensors. To overcome this problem, we use motion features associated with single pixels in the two image sequences to carry out automatic registration. A normalized optical flow time sequence for each image pixel is constructed. The matching of pixels between the IR image and the visible light image is carried out using a fast similarity measurement and a three stage correspondence selection method. Finally cascaded random sample consensus is adopted to remove outlying matches, and least-square method and Levenberg---Marquardt method are used to estimate the transformation from the IR image to the visible image. The effectiveness of our method is demonstrated using several real datasets and simulated datasets.

Line based Matching of Uncertain 3D Building Models with IR Image Sequences for Precise Texture Extraction

Line based Matching of Uncertain 3D Building Models with IR Image Sequences for Precise Texture Extraction

Dim moving target detection in multispectral IR image sequence based on GLRT

The scope of this paper addressed the problem of detecting a dim moving target from a sequence of multispectral IR cubes.The detection problem was formulated in a general framework,assuming unknown target amplitude,position and velocity.This composite hypothesis testing problem was approached by means of the generalized likelihood ratio test(GLRT) theory.The detector structure and its actual implementation based on velocity filters were discussed in detail.Approximated expressions of the false alarm and detection probabilities were obtained and validated by means of simulation.To test the effectiveness of the detection algorithm,the detection results obtained on a set of synthetic multispectral IR image sequences were presented and discussed.These results indicate that the algorithm proposed can obtain a good performance on dim target detection with low SNR.