This paper presents a one-dimensional scanning algorithm that allows improving the quality of images that were using aerial photography to monitor minerals and the state of the subsurface. Also, a software module was developed that implements the presented algorithm, the main advantages of which are the simplicity of the mathematical apparatus, the required number of low-resolution images, equal to two, to obtain a high-resolution image, and resistance to interference and noise.
The problem of detection and trajectory tracking of a group of small objects by a passive vision system is solved. The system consists of several space-oriented optical receivers that monitor the viewing area is considered. We propose an approach based on the distribution of optical receivers over stereo pairs, taking into account the orthogonality of the lines of sight on objects, and the redistribution of stereo pairs in the case of receiver failures to solve this problem. An appropriate algorithm has been developed to improve the reliability of the system, the probability of detecting all objects, and the accuracy of determining spatial coordinates. The results of the experimental examination of the developed algorithm are presented.
This paper describes algorithms for detection and evaluation of trajectory parameters of small, spatially moving objects by passive optical and radio thermal vision system. The algorithms are based on spatial and temporal image processing. During spatial processing, a system of equations representing a sufficient condition for the conjugation of direction vectors to objects in stereo pairs is solved. During temporal processing vectors of the directions on accessory to objects in a sequence of the periods of supervision are distributed. The results of the algorithms theoretical and experimental examination are given. They are showing the advantage of the joint application of the two approaches.
This paper presents a one-dimensional scanning algorithm that allows improving the quality of images that were using aerial photography to monitor minerals and the state of the subsurface. Also, a software module was developed that implements the presented algorithm, the main advantages of which are the simplicity of the mathematical apparatus, the required number of low-resolution images, equal to two, to obtain a high-resolution image, and resistance to interference and noise.
KEYWORDS: Detection and tracking algorithms, Video, Stereo vision systems, Cameras, Distance measurement, Switching, Field programmable gate arrays, Digital signal processing, Imaging systems, Computing systems
This paper describes the complex object distance measuring algorithm for the stereoscopic real-time onboard vision system. This complex algorithm includes two correlation-based algorithms with the different performance complexity and accuracy. The most accurate basic algorithm is a two-dimensional template matching procedure. The other basic algorithm is one-dimensional matching algorithm that used cumulative images as an input. The switching between the algorithms is based on the proposed algorithm performance indicator. This indicator is based on the mean object and background brightness comparison. The experimental research was performed using a set of artificial and natural video sequences. The proposed complex estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Based on the averaging principle, an algorithm for subpixel processing of an aerospace image is developed to increase spatial resolution. With it, a high-resolution image can be gotten by processing several original shots with a lower resolution. Input information is a series of aerospace images that are offset relative to one another by half a pixel. There is one generated image with increased resolution appears as the result of the processing.
Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information
about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic
aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be
acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image
processing can be used to solve many problems arising in this field of research. This paper discusses some of them
including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several
specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps:
image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm
consists of the following main steps: prescreening, land masking, image segmentation combined with parameter
measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is
based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is
concluded that the proposed approaches can be used in maritime applications.
The present paper has suggested a technology for search, indexing, cataloging and distribution of aerospace images on the basis of geo-information approach, cluster and spectral analysis. It has considered information and algorithmic support of the system. Functional circuit of the system and structure of the geographical data base have been developed on the basis of the geographical online portal technology. Taking into account heterogeneity of information obtained from various sources it is reasonable to apply a geoinformation platform that allows analyzing space location of objects and territories and executing complex processing of information. Geoinformation platform is based on cartographic fundamentals with the uniform coordinate system, the geographical data base, a set of algorithms and program modules for execution of various tasks. The technology for adding by particular users and companies of images taken by means of professional and amateur devices and also processed by various software tools to the array system has been suggested. Complex usage of visual and instrumental approaches allows significantly expanding an application area of Earth remote sensing data. Development and implementation of new algorithms based on the complex usage of new methods for processing of structured and unstructured data of high volumes will increase periodicity and rate of data updating. The paper has shown that application of original algorithms for search, indexing and cataloging of aerospace images will provide an easy access to information spread by hundreds of suppliers and allow increasing an access rate to aerospace images up to 5 times in comparison with current analogues.
KEYWORDS: Image processing, Object recognition, Detection and tracking algorithms, Systems modeling, 3D modeling, Field programmable gate arrays, Databases, Unmanned aerial vehicles, Neural networks, Computing systems
This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Task of processing and analysis of obtained Earth remote sensing data on ultra-small spacecraft board is actual taking into consideration significant expenditures of energy for data transfer and low productivity of computers. Thereby, there is an issue of effective and reliable storage of the general information flow obtained from onboard systems of information collection, including Earth remote sensing data, into a specialized data base. The paper has considered peculiarities of database management system operation with the multilevel memory structure. For storage of data in data base the format has been developed that describes a data base physical structure which contains required parameters for information loading. Such structure allows reducing a memory size occupied by data base because it is not necessary to store values of keys separately. The paper has shown architecture of the relational database management system oriented into embedment into the onboard ultra-small spacecraft software. Data base for storage of different information, including Earth remote sensing data, can be developed by means of such database management system for its following processing. Suggested database management system architecture has low requirements to power of the computer systems and memory resources on the ultra-small spacecraft board. Data integrity is ensured under input and change of the structured information.
The aim of this work was developing an algorithm eliminating the atmospheric distortion and improves image quality. The proposed algorithm is entirely software without using additional hardware photographic equipment. . This algorithm does not required preliminary calibration. It can work equally effectively with the images obtained at a distances from 1 to 500 meters. An algorithm for the open air images improve designed for Raspberry Pi model B on-board vision systems is proposed. The results of experimental examination are given.
KEYWORDS: 3D modeling, Image processing, 3D image processing, Field programmable gate arrays, Optical spheres, Binary data, Digital signal processing, Cameras, Detection and tracking algorithms, Error analysis
This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
In this paper image-based collision avoidance algorithm that provides detection of nearby aircraft and distance estimation is presented. The approach requires a vision system with a single moving camera and additional information about carrier’s speed and orientation from onboard sensors. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The proposed algorithm is able to detect small targets but unlike many other approaches is designed to work with large-scale objects as well. To localize aerial vehicle position the system of equations relating object coordinates in space and observed image is solved. The system solution gives the current position and speed of the detected object in space. Using this information distance and time to collision can be estimated. Experimental research on real video sequences and modeled data is performed. Video database contained different types of aerial vehicles: aircrafts, helicopters, and UAVs. The presented algorithm is able to detect aerial vehicles from several kilometers under regular daylight conditions.
This paper describes the implementation of the multiple targets tracking algorithm in FPGA-based vision system. The described algorithm was designed to process such situations as the object trajectories crossing and the temporary object screening by other objects. The source data for this algorithm is a list of the parameters of the previously extracted binary regions from each frame of the sequence. The main idea of this algorithm is to represent the source data as a bipartite graph and split it into insolated elementary graphs corresponding to five situations: object is moving or staying still, a new object detected, object is missed, the pair of the objects is merged into one and the region is divided. These graphs are used to form a new object list. The goal of this work was to implement the described algorithm in small-sized onboard vision system based on the single Xilinx FPGA using MicroBlaze soft processor block. In the proposed implementation of this algorithm recursive procedures were replaced with table-based procedures. The experimental research of the algorithm shows the increasing tracking performance 5 – 9 times on previously described hardware.
An approach to tracking objects changing their size from several pixels to whole image size is suggested in this paper. A
complex object tracking algorithm designed for a family of FPGA-based on-board vision systems is proposed. The
results of experimental examination are given.
In this paper an algorithm for image geometric transformation parameters estimation which deals with multispectral
video sequences is considered. An approach of optimal choosing of reference areas that allow minimizing error, caused
by additive noise presence, is proposed. The results of experimental examination are given.
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