|Digital Signal Processing||
Fusion of hyperspectral images of the Earth, acquired in different spectral ranges
The fusion of hypercubes obtained from various OEC to the image with single spatial resolution has not researched enough. So we propose a task to obtain a combined hypercube having a maximum possible resolution. Prerequisite of such task statement is a fact that a set of initial hypercubes has a hypercube with the best resolution and it can be used for calculation of the compensating filter.
The present paper suggests using a linear filtering of images in order to solve this task. The filter parameters are estimated by processing of images using two possible approaches: in the spatial area – by means of the least squares method and in the spectral area – on the basis of the ratio of results of the Fourier transform of the hyperspectral image channels.
As a result of experimental researches of the proposed approaches using HSI from the spacecraft “Resource-P” the following has been determined:
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6. Àíòîíóøêèíà Ñ.Â., Eremeev V.V., Makarenkov A.A., Moskvitin A.E. “Specifics of analysis and processing of information from satellite hyperspectral Earth imaging systems” // Digital Signal Processing. ¹4. 2010. PP. 38-43.
7. Eremeev V.V., Makarenkov A.A., Moskvitin A.E., Yudakov A.A. “ Improving Object Readability on Hyperspectral Imagery of the Earth’s Surface” // Digital Signal Processing. ¹3. 2012. PP. 35 – 40.
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Image processing algorithm for combined vision system of aircraft
Keywords: combined vision system, image registration, edge detection, fuzzy clusterization, geometric transformations, Fourier transform.
The algorithm of combined image synthesis includes the following steps:
The algorithm of edge detection in the real images is developed. This algorithm based on the fuzzy C-means clustering method and includes the following procedures: preprocessing, clusterization, morphological analysis of binary image, parametric analysis of detected contours. We use ROI (region of interest)-based image processing in this step. The size and position of the ROI can be defined in accordance with information about the object derived from the DTM.
Mismatch of images can occur due to factors such as errors in measuring the position of the aircraft as a material point in space (geographic coordinates X, Y and height H), and errors in measuring of angles of the aircraft's orientation relative to its center (yaw Cr, pitch Tn and roll Kr). Brute force search of orientation angles can be replaced by estimation of the Euclidean transformations, namely offset (α, β) and rotation angle φ. We propose to use the method based on properties of the Fourier transform for estimation of geometric transformation parameters of real and synthetic contour images.
Experimental research of the developed algorithm of combined image synthesis is performed using real videos obtained during the observation from the aircraft board. The proposed algorithm showed its efficiency. So for the whole image synthesis average time is reduced in 20 times in case of rough estimation of geometric transformation parameters. If the estimation accuracy close to the maximum available for the image resolution, the performance increases a thousand times in comparison with brute force search and a three times in comparison with the correlation approach.
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Flying at low altitude are extremely dangerous because it often occur collisions with obstacles (bridges, high-rise building, pillar). To reduce the number of collisions is necessary to use assistance systems that warn of approaching to high-rise building. Collision avoidance systems may use different types of sensors that give information about the surrounding area. The paper describes the algorithm for detection of ground high-rise structures. For information around the aircraft uses only one video sensor. Analyses of the images sequence and of the aircraft coordinates allow detect high-rise buildings. Next we estimate the distance to the obstacle and time to the collision. Information about time to the collision allow the system warn the pilot about the threat of a collision.
Obstacle detection and estimation of their parameters is made by analyzing in the video sequence of coordinates of points of observed objects. To compare the object points in a video sequence is necessary that their images had distinguishing features, points must be features. There are various detectors of feature points. In most cases, the image of obstacles have clear boundaries, therefore comprise corner points. Consequently, to detect obstacles we use corner detectors (Harris corner detector). Analyse of the points on the edges of the image allow to evaluate the geometrical parameters of the obstacles.
Video sensor must move in space to detect obstacles before aircraft. As a result, we obtain images of objects (obstacles) from different angles. Having coordinates of the aircraft in space and calculating the coordinates of feature points in the image sequence can find the coordinates of an object in space. Among all points by analyzing the coordinate of feature points seek the points that are located in the space above the surface of the Earth. These points may belong to an image of obstacles. Obtained the points are combined to the group of feature points. For each object are calculated coordinates, range, dimensions, height. Subjects with high altitude are considered obstacles. Knowing information about the altitude of the aircraft system decides about the danger to collisions. If the obstacle is a threat, then system informs the pilot of the approaching danger.
The developed algorithm calculates the distance to the obstacle, their height above the ground. The accuracy of the algorithm depends on the speed of the aircraft, the distance to obstacles.
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Nowadays there are many indoor positioning systems, but there is not universal solution, as in global navigation satellite system (NAVSTAR, GLONASS, BeiDou, Galileo, etc.). The reason is the complexity of the accurate indoor positioning using radio channel. Therefore, this paper proposes the visible range of the electromagnetic spectrum for indoor orientation. A simple and inexpensive color camera is enough for work using this range.
In such system, the mobile robotic platform (MRP) orientates by special color-coded landmarks. Thus, MRP with installed camera can orients indoors. The landmarks are located indoor on fixed positions with a priory coordinates and with the known size. The advantage of such landmarks in comparison with other types is easy-to-manufacture, economical and do not require a power source, which allows them to remain operable for a long time.
The landmarks have following restrictions that define the algorithm working efficiency:
The vertical landmark position is also preferable as the distance between color areas will not change while horizontal movement of the MRP.
The article describes technique of positioning system operation: the algorithm of color-coded landmarks recognition and the positioning algorithm. The article describes construction of “color mask” of television image.
Due to the possible impact of different types of noise on the positioning system, the research of the influence was provided. This research considers two methods of transformation from relative coordinate (the camera coordinate system) to absolute coordinate (plan of indoor space): the three-dimensional affine transformations usage (parallel transfer, turn, scaling) and gradient descent (to achieve a minimum standard deviation).
The studies found using the method of gradient descent the system is more stable. Besides, it shows low dispersion values of calculated coordinates in comparison with the results using the method of affine transformations.
During the studies, the system has shown the greatest resistance to the influence noise, when impact noise "salt and pepper".
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The paper has analyzed methods of color correction of panoramic images with small-size objects. Methods used for this task solution can be divided into parametric approaches with usage of models and nonparametric approaches without usage of models. Global methods operating in various color spaces (methods No.1  and No.2 ) and also local methods (methods No.3  and No.4 ) using various probabilistic characteristics have been chosen for analysis. Besides, method  based on the tensor voting had been analyzed but due to its low computational speed it was excluded from the following research. Sets of images have been created to evaluate an efficiency of methods. These images were synthetic with distinctive brightness characteristics at the boundary +6, +8, +10, +12 and natural. Also images in sets differ in scenes with various percentage of the sky area: 75%, 50%, 30%, 25%. Small-size objects (a plane) with different sizes in relation to area of the whole image were in the sets of images.
Criterion  containing two components: color similarity between the original image and transferred image and structural similarity between the resultant image and transferred image, was used as a criterion of efficiency in this paper.
Executed qualitative and quantitative analysis of the methods of color correlation of panoramic images with small-size objects has shown that local methods of color correction (No.3 and No.4) provide better results. In comparison with method No.1 operating in the uncorrelated LAB color space, method No.2 operates in the correlated space RGB that leads to degradation of the color correction effectiveness. Method No.3 cannot be recommended for images with small-size objects in spite of its high speed.
2. M. Brown and D.G. Lowe. Automatic panoramic image stitching using invariant features. IJCV, 74(1):59-73, 2007.
3. Îáèäèí Ã.È., Ñèëüâåñòðîâà Î.Â. Ñïîñîáû îöåíêè ýôôåêòèâíîñòè öâåòîâîé êîððåêöèè â ïàíîðàìíûõ èçîáðàæåíèÿõ ñ ìàëîðàçìåðíûìè îáúåêòàìè // Òðóäû 20-é Âñåðîññèéñêîé íàó÷íî-òåõíè÷åñêîé êîíôåðåíöèè «Ñîâðåìåííîå òåëåâèäåíèå», Ìîñêâà, ÔÃÓÏ ÌÊÁ «Ýëåêòðîí», 2012.
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15. Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 2004.
At first the paper considers factors leading to high complexity of adjacent CCD image displacements: high frequency disturbances of the satellite orientation and relief.
Known stitching methods divided into four groups have been analyzed:
Implementation of the stitching method proposed by authors that uses DEM is reviewed. This method is based on precise georeferencing with the rigorous sensor geometric model. “Stitched” image parameters are the same as parameters of the image obtained by a single virtual CCD-line with a swath equal to the total swath of all TDI CCD sensors. In this case it is possible to use a rough DEM because 300–1000 m errors of the elevation depending on a scanner lead to the stitching error < 1 pixel. The mathematical model of stitching is given including three-dimensional piecewise linear approximation of the geometric correspondence functions for high-speed processing. The paper includes an example of the stitched image with mountain landscape and stitching accuracy estimation in this case.
In conclusion the precise stitching technology based on the stitching method proposed by authors is reviewed. The technology provides a geodetic orientation procedure to reach high stitching precision in the case of significant georeferencing errors. The proposed technology is successfully exploited in the Research Center for Earth Operative Monitoring of the Russian Federal Space Agency for processing of images obtained by spacecraft “Resurs-P” No.1 and No.2.
2. Tang, X.; Hu, F.; Wang, M.; Pan, J.; Jin, S.; Lu, G. Inner FoV Stitching of Spaceborne TDI CCD Images Based on Sensor Geometry and Projection Plane in Object Space. Remote Sens. 2014, 6, 6386-6406.
3. Weican Meng, Shulong Zhu, Baoshan Zhu, Shaojun Bian The research of TDI-CCDs imagery stitching using information mending algorithm. Proc. SPIE 8908, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications, 89081C (August 21, 2013); doi:10.1117/12.2033285.
4. Modern technologies of Earth remote sensing data processing [in Russian]. Under the editorship of V.V. Eremeev Moscow: Fizmatlit, 2015, 460 p.
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Images obtained from the spacecraft «Landsat-8» and digital model of the SRTM relief were used as a source of reference data. Unfortunately, on the basis of separate images it is rather difficult to arrange a quick search of corresponding scenes on analyzable and reference images. So, a multiscale continuous bitmap coverage of the Earth surface reference has been developed by analogy with mapping services. For this purpose images obtained from the spacecraft «Landsat-8» were transformed into the Mercator projection (WGS84).
The pyramid of layers has been designed to organize a multiscale representation of the continuous reference image. In this pyramid resolution of each following layer is half resolution of the previous layer. Pyramid layers are represented as images of very big size. So, a tile-based mechanism of the data organization is used for operation with such bitmaps. Only tiles required at the present moment are used to extract patches of the continuous image that allows avoiding excess requests to the disk storage, decreasing a number of cache-misses and increasing velocity of the reference bitmap formation.
The paper has analyzed possible solutions for search of corresponding points on images being heterogeneous in time of the significant (tens and hundreds of gigabytes) size.
Some authors use a method of the correlation-extremal identification of corresponding fragments. Besides, search of corresponding objects is carried out along the pyramid of images to take significant mutual coordinate mismatches into consideration, and images are preliminarily reduced to the contour form in order to exclude influence of the image scene texture and results of the correlation matching are analyzed by means of a group of statistical checks of the correlation function form.
Unfortunately, time expenditures of the suggested approach are enough high and in some cases an operator finds corresponding points faster than the procedure of automated search.
Method of the corresponding point search SURF is more effective in terms of multithreaded realization. It is based on extraction of compact descriptions of distinctive patches – descriptors from comparing images and their following matching between each other. Search of blobs is carried out in the SURF algorithm to detect characteristic points because descriptors of such patch type can be matched with greater reliability.
Matching of all descriptors of one image with descriptors of another is carried out to determine corresponding points according to the original method. And proximity measure is a Euclidian distance calculated according to all components of descriptors. The paper suggests an optimized algorithm of the descriptor matching based on the preliminary decomposition of descriptors into groups where the Hessian matrix trace sign coincides among all elements and angle of the dominating direction differs not more than in 5o, with following matching of descriptors only of the same group.
The following results have been obtained according to results of the executed researches.
Reference bitmap information bank has been established in the territory of the Russian Federation and CIS countries. Bitmap coverage of this territory is organized in the form of a pyramid of different-scale layers with partition into tiles. Such representation of information allows realizing an access to any part of the coverage in real time.
High-performance and reliable mechanism for identification of coordinates of corresponding objects on the reference and analyzable images being heterogeneous in time and having texture differences and containing cloudy objects has been developed on the base of the SURF algorithm. Found coordinates of reference points and their heights are transmitted to the procedure for estimation of the georeferencing precision of imagery routes.
Developed technology of the automatic control of georeferencing has been adapted to information obtaining from the spacecraft “Canopus-V” and at present it is processed in the Research Center for Earth Operative Monitoring.
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4. Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, "Speeded Up Robust Features", ETH Zurich, Katholieke Universiteit Leuven.
Developed method of real time video scene detection, discriminating field game episodes from other types of content has six important features: a) detection is performed on a frame-by-frame basis preserving temporal smoothness within the same video segment; b) detection is based on video cues understandable by human; c) four new types of color detectors producing human-like judgments are proposed: yellow, green, white and bright and saturated color; d) four types of low level statistical features are proposed: mean gradient in green areas, histogram compactness for luminance channel in green areas, average luminance in green areas, average value of blue channel for green areas; e) classifier has the form of directed acyclic graph with 1-D and 2-D thresholding functions, linear classifiers and logical functions in the nodes; f) new type of scene change detector based on k-means color segmentation is proposed.
Presented solution is tested on ∼ 25 hours of sport and non-sport content of different quality (SD/HD, compressed by MPEG2, H264 and XViD). The classifier provides frame-by-frame detection results and utilizes small number of arithmetic operations - summations and shifts, allowing cheap hardware implementation and application in adaptive video enhancement pipeline of TV receiver. Submitted approach clears the way for a whole set of advanced algorithms for visual recognition and object categorization. It can also be used as pre-classifier in video classification solution.
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In this paper we propose video matting methods’ comparison technique by spatial error and temporal coherence. The expected value of error of matting result composite with random background is used as spatial error measure. And variance of per frame spatial error is used as temporal error measure. We show comparison results of 12 matting methods. Additionally, we show how matting methods’ performance is affected by trimaps’ unknown area width.
To carry out experiments described above we prepared a set of test video sequences with ground truth transparency maps. To get ground-truth for our test data set we employed chroma keying and the following stop-motion capture procedure. The object with semitransparent edges is placed on the platform in front of an LCD monitor. The object rotates in small, discrete steps along a predefined 3D trajectory, controlled by two servos connected to a computer. After each step the digital camera in front of the setup captures the motionless object against a set of background images. At the end of this process, the object is removed and the camera again captures all of the background images. We paid special attention to avoiding reflections of the background screen in the foreground object. These reflections can lead to false transparency that is especially noticeable in nontransparent regions. To reduce the amount of reflection we used checkerboard background images instead of solid colors, thereby adjusting the mean color of the screen to be the same for each background. The new stop-motion procedure enabled us to get transparency maps with quality sufficiently exceeding results of chroma keying and stop-motion technique used in .
The results of all experiments were published at http://videomatting.com to enable interactive analysis and addition of new methods.
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The problem of accurately determining the position of eyes in the face image (eye localization) is important for a wide range of modern computer vision systems. Such as determining the direction of view and the angle of rotation of the head relative to the camera, the analysis of facial expressions and so on. In addition, the localization of the eye successfully used as a preliminary stage in the face recognition task. In this case, the coordinates of the eyes center can help to properly normalize face image after its detection. Studies show that the accuracy of the localization of the eyes has a significant impact on the quality of the face recognition system.
Over the past three decades, many different approaches to solving the problem of localization of the eye have been proposed. However, despite significant progress in this area, it is worth noting that this problem is still far from being solved.
Most modern eye localization methods can be divided into three categories:
Analysis of known eye localization algorithms shows that the existing techniques are error prone. Insufficient image quality or the presence of spectacles in the image leads to inaccurate localization.
In this paper we propose an iterative algorithm for the localization of the eye centers based on multi-block local binary patterns that adapts to the quality and complexity of the face image. For testing we used two well-known eye localization algorithms - gradient and Bayesian. Currently they are often used in practical applications, and demonstrate an acceptable accuracy of localization. Gradient algorithm of localization uses a priori information about the spatial structure of the face. Bayesian algorithm is based on statistical learning using available sample of eye images.
The proposed algorithm gives almost no rough localization error (err > 0.15). Eyes localization error exceeds 0.15 only for 1% of the images from the database FERET and 4% from the BioID database. Furthermore, this method exceed other algorithms almost an order of magnitude in terms of performance. This allows to localize eyes in the video stream in real time.
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The basis of the method is a prediction procedure. Each BI is approximated by a two-dimensional Markov chain with two states. This representation allows to implement the images statistical redundancy as much as possible. The prediction of BI elements is realized on the basis of the transitions probabilities matrix of two-dimensional Markov chain and only incorrectly predicted bits will be stored. Only logical comparison operations are used for the prediction implementation. The prediction procedure is most effective for high-order BI, which contain maximum redundancy. The presence of areas similar to white Gaussian noise (WGN) according to the statistical characteristics is typical of middle-order and low-order BI. Therefore, the removal of such areas for these BI takes place before the prediction procedure. These areas are filled up with samples WGN at restoration. Any coding can be carried out after the prediction procedure. The RLE and Huffman methods are used in this work.
The method effectiveness investigation was carried out by compressing the test panchromatic and color images by the known (JPEG, JPEG2000) and the proposed methods. The MSE, SSIM and speed of processing were used as parameters for an estimation of recovered images quality. The proposed method has a slight loss in the compression ratio but higher performance in comparison with analogs.
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Combine vision systems execute correlation combining of two images: real (RI) and virtual (VI), viz. comparison functional, particularly, cross-correlation function of VI and RI is calculated and extremum of comparison functional received is determined.
There are many methods of global extremum searching: genetic algorithm (GA), step-by-step scanning and so on.
The aim of this work is research of global optimization methods used for correlation images combining and finding ways to increase the performance of images combining algorithm.
We have found out that the most effective global optimization method is genetic algorithm. This algorithm has definite advantage over other methods. In particular image combination runtime using GA is order of magnitude less than using step-by-step scanning method. Thus it is necessary to receive GA optimum parameters values while it being used in combined vision systems has been carried out.
The ideas of GA were borrowed from nature. They are based on genetic processes of human organisms: biological populations develop over several generations, obeying the laws of natural selection according to “survival of the fittest” principle.
To increase the performance of images combining algorithm we offer to use extended of virtual terrain model angles. They are supposed to decrease the computational complexity of the correlation combining images algorithm. This approach considerably reduces the number of virtual images generated for combining.
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