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In this work, we have considered industrial objects and livestock enterprises, which are located in an area of Smax. Some conditions and connections between them in the mathematical model are formulated newly, and the optimal equilibrium ratio states for the long-term existence of these objects are theoretically determined. Also, based on the mathematical models of the two objects, the optimal area ratio and values of the model were found
This study investigates the creation of large-scale images through mosaic construction and evaluates the outcomes thereof. The initial phase of the study involves a comprehensive comparison of algorithms commonly employed in mosaic creation, focusing on their respective strengths and weaknesses in terms of precision and computational efficiency. The experimentation is conducted on a dataset comprising 150 images obtained from SenseFly's aerial surveys. Results reveal that the utilization of the SURF algorithm for mosaic creation yields the highest precision, with a matching value of 30.6381 and a processing time of 605.5 seconds, surpassing other algorithms. However, employing the SURF algorithm for processing entire images poses challenges in terms of computational complexity, processing time, and memory usage. To address this, a methodology is proposed to selectively apply algorithms based on segment characteristics, enhancing precision and reducing processing time. Experimental results demonstrate that employing this approach reduces processing time to 120.2 seconds and minimizes error, resulting in superior outcomes when utilizing the SURF algorithm for the entire dataset.
This paper presents a comparative study between two robust estimation approaches: homography matrix-based RANSAC and fundamental matrix-based RANSAC, for outlier elimination in various computer vision applications. The study focuses on the critical task of reliably estimating correspondences across two-view images. The Random Sample Consensus (RANSAC) algorithm is employed to estimate accurate homography and fundamental matrices robustly, even in the presence of outliers. Image datasets are utilized for experimental analysis, including rotations and translations of object. The performance of both methods is compared in terms of accuracy, robustness based on their geometric properties with the different test dataset. Experimental results demonstrate that the homography matrix-based RANSAC method works well with planar movements of the objects, while the fundamental matrix-based RANSAC method performs better with 3D movements of the objects. The paper concludes by discussing the implications of these findings and highlighting the suitability of each approach.
Accuracy of the motion estimations is highly depending on inlier corresponding points which is computed from RANSAC (Random Sample Consensus) based methods. In this study, we compared four RANSAC based methods to estimate exact corresponding points such as essential matrix based RANSAC method, homography matrix based RANSAC method, relative orientation based RANSAC method, and homography based relative orientation based RANSAC method. Firstly, we captured image sequences of a moving object. Secondly, to track motion of the object, we estimate corresponding points between object image template at t 1 time and object image frames at t 2 time. The corresponding points are computed from the four RANSAC based methods. Finally, we analyzed robustness and accuracy of the methods. We showed that homography matrix based RANSAC and homography based relative orientation based RANSAC methods are produced more accurate results for planar object sequences. Essential matrix based RANSAC and relative orientation based RANSAC methods are produced more accurate results for 3D object sequences.
This study presents the estimations of the 3D motion of a moving object in an image sequence taken from a monocular camera through linear and non-linear equations and determines the differences between linear and non-linear algorithms in terms of theoretical level and estimation accuracy with noisy point correspondences. Firstly, we investigated linear and non-linear algorithms for determining 3D motion at the theoretical level. Second, we estimated the 3D motion of the moving object in an image frame at two different instants of time with feature point correspondences in real-time. Finally, we implemented an accuracy analysis of the results from the linear and non-linear estimations. We showed that the non-linear approach produced more accurate results than the linear approach from noisy point correspondences.
Spatial tracking technologies are necessary to design usable 3D (three-dimensional) user interfaces. Traditionally, 6-DOF (degree of freedom) trackers are widely used in 3D user interfaces by detecting the 3D location and orientation of the object. Conventionally, 3D user interfaces extracted from image sequences require additional devices for their 3D input to sense 3D motion. In this regard, our approach to converting motion parameters into 3D input using a monocular camera is unique. In this study, we introduce the development of 3D input through 6-DOF motion estimations. Finally, 3D input could be provided through motion estimations using a monocular camera. To improve existing motion estimations, they could be applied in real-time to provide 3D input of 3D interaction. They were robust to provide 3D input. Keywords: 3D input, 3D user interface, motion estimation, 6-DOF
Last decades, selecting for optimal location of a new organization has been an essential component in all kinds of businesses, including manufacturing and service organizations. The most significant difficulty to address the location selection problems is finding how to measure the considered criteria. Multicriteria decision-making (MCDM) methods are most suitable for solving the facility location selection problems for these reasons. In this work, we applied AHP, PROMETHEE, and TOPSIS methods of the MCDM technique. When we are employing these methods to solve location selection problems of one organization, these methods prove their applicability to deal with such types of decision-making problems with multiple criteria and facility alternatives.
In this work, we tested the global thresholding method, the well-known image segmentation method for separating objects and background from the image on its refined histogram using the distinction neighborhood metric. If the original histogram of image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement. We refined the histogram to overcome the big bin problem in which sub-bins are created from big-bins based on distinction metric. Median and Otsu thresholding methods are used in our work, and experimental results show that they work more effectively on the refined histograms.
Сүүлийн жилүүдэд төрийн болон хувийн бүх төрлийн бизнес, тэр дундаа үйлдвэрлэл, үйлчилгээний байгууллагуудын хувьд байршлыг зохистой сонгох хэрэгцээ шаардлага маш их болсон байна. Байгууламжийн байршлын оновчтой шийдвэр гаргахын тулд олон шалгуурт шийдвэр гаргах (Multi Criteria Decision making-MCDM) техникийн төрлүүд болох AHP, PROMETHEE, TOPSIS аргуудыг дэлхийн улс орнуудад хот болон бүс нутагтаа хэрэглэж байгааг олон судалгааны ажлуудад харуулжээ. Энэхүү судалгааны ажлаар бид эдгээр аргуудыг ашиглан нэгэн байгууллагын хувьд хэрэгцээт байгууламжаа өөрийн сонгосон хувилбаруудаас оновчтой сонгоход тусалсан үр дүнг гаргаж, аргуудын харьцуулалт хийж харуулсан болно.
With the 3D mouse, professional users can interact with virtual space more intuitively by positioning their 3D model or view and providing access to their favorite applications than with a 2D mouse. Furthermore, while working with a 3D mouse, the user can rotate the 3D model around each axis and translate it along each axis. Users can also use a 2D mouse for a more efficient combination of input devices. To contribute its importance, we present an approach for providing 3D input of 3D mouse by determining 6-DOF (degree of freedom) motion of a moving object in image sequences taken by a single camera. To estimate 3D rotation and 3D translation parameters, firstly, we computed the homography matrix by using corresponding points. Secondly, we estimated 3D motion parameters from the homography matrix. Finally, the estimated motion parameters were integrated into a simple 3D mouse application as its 3D input for manipulating the 3D model in a virtual space, controlling the mouse cursor, and triggering mouse events. These procedures have been successfully implemented in real-time processing.
This study presents Kalman filtering based motion correction. Motion of a moving object in image sequences is estimated from phase correlation that is applied to sub-images. To smooth noisy motion, characterized as translation, rotation and scale, Kalman filtering was applied after phase correlation. Experimentally, we observed that Kalman filtered rotation and scale were comparatively lower than originally estimated values. Also, Kalman filtered rotation and scale parameters considerably improved the visual appearance of tracking in the real scene.
We aimed a tracking algorithm which works robustly in the presence of large scale, rotation, and translation of a moving object. To recover 2D rotation and scale between an object in a template and an object in next frames of the video sequence, we applied the phase correlation method as frequency matching. Since the changes in scale and rotation in the Cartesian coordinates are transformed into translations in log-polar coordinate system, we applied frequency matching in log-polar domain. Translation motion is acquired through template matching technique by dynamically updating template for each frame based on feature matching. An original method for motion estimation in frequency domain was mostly introduced in image registration task [1-5]. We introduced our algorithm different from previous applications. The original method had limitations in that a sub-image was selected for every position in the target image and was compared against the sub-image from template. We overcome this problem by combining feature matching into frequency matching. The proposed method finds the feature correspondences in the template and subsequence frames. Feature points are used to reduce the computational time. Results, experiments, and test datasets to check accuracy of the estimated motion parameters from the proposed method is presented.
While working with a three dimensional (3D) user interface, professional users can interact with virtual 3D space in more intuitive way by positioning their 3D model or viewing, and providing access to their favorite applications than with a 2D mouse. To contribute its importance, we review approaches for providing input of 3D user interface by determining motion of a moving object in image sequences taken by a single camera without using additional devices to sense 3D motion. We also summarized technical and application difference between estimation approaches of the object motion in frequency and spatial domain. In frequency domain, we proposed a method for providing inputs of 3D interaction between physical and virtual 3D space based on log-polar transformation and phase correlation using two dimensional (2D) to 2D point correspondences. The estimated motion parameters were providing four degrees of freedom (4-DOF) input to the interaction system. In spatial domain, firstly, the relative pose estimation methods developed in computer vision and photogrammetric field was carried out. We implemented approaches for 3D motion estimation in both fields by using 2D to 2D point correspondences, and performed an analysis for their results. The estimated motion parameters were providing up to 6-DOF input to the interaction system. In spatial domain, secondly, we present an approach for providing input of 3D interaction by determining 3D motion of an object based on 2D to 3D point correspondences. To estimate motion parameters as characterized by 3D rotation and 3D translation, non-linear equations based on rigid transformation have been formulated in least square. Good initial values of this non-linear system were provided from para-perspective projection model to overcome ill-conditioned convergence problem of the equations. The estimated motion parameters were providing 6-DOF input to the interaction system.
Determining three-dimensional (3D) motion parameters from image sequences has been challenging task in a variety of applications for a long time. Motion parameters are determined through linear and non-linear algorithms by establishing point correspondences extracted from two or more views. Generally, non-linear algorithms solve problem of non-linear least squares iteratively. Iterative methods may converge to a local but not global minimum with a good initial guess, or may diverge at all. In a number of cases, linear algorithms have formulated with given eight or more correspondences. Linear algorithms solve linear equations that give the unique solution except in degenerate cases. In all practical situation, non-linear and linear algorithms fail to find the unique solution for many situations such as initialization, degenerate spatial configuration, noisy data, any relative motion between the camera and the scene. We present an analytical result on how estimation error depends on motion characteristics of the moving object in image sequences taken from single camera. First, non- linear and linear algorithms are described that determines motion parameters from point correspondences between perspective views. The relationship between errors and the motion of the moving object are analyzed. The results of the analysis show that when the algorithms are failed with which motion of the moving object. Simulations are demonstrated performance of the algorithms as well as the relationships between errors and motion of the object in the image sequences.
The techniques of measuring position and orientation of the object using image correspondences are based on the principles of epipolar geometry in photogrammetric field. In this study, we aim to evaluate the main differences of approaches for estimating motion parameters, characterized by 3D rotation and 3D translation. The estimation methods developed in photogrammetric field were carried out. This paper describes the mathematical motion model for the proposed approaches and comparison of their results, indicated the differences between the estimations in terms of accuracy and test dataset. We showed that relative orientation based approach produced accurate results for image sequences of 3D objects but homography based relative orientation approach produced accurate results for image sequences of planar objects.
This study investigates stereo 3D viewing for linear pushbroom satellite images using the Orbit-Attitude Model and using OpenGL graphic library in Digital Photogrammetry Workstation. 3D viewing is tested with KOMPSAT satellite stereo images, a large number of GCPs (Ground control points) collected by GPS surveying and orbit-attitude sensor model as a rigorous sensor model. Comparison is carried out by two accuracy measurements: the accuracy of orbit attitude modelling with bundle adjustment and accuracy analysis of errors in x and y parallaxes. This research result will help to understand the nature of 3D objects for high resolution satellite images, and we will be able to measure accurate 3D object space coordinates in virtual or real 3D environment.
We present a new algorithm for determining 3D motion of a moving rigid object in real-time image sequences relative to a single camera. In the case where features are two-dimensional (2D), they are obtained by projective transformations of the 3D features on the object surface under perspective model. The perspective model has formulated in nonlinear least square problem to determine 3D motions as characterized by rotation and translation iteratively. In practice, it is numerically ill-conditioned and may converge slowly or even fail to converge, if it starts with not good enough initial guess. However, since para-perspective projection model closely approximates perspective projection for recovering the 3D motion and shape of the object in Euclidean space, we used the results provided from para-perspective projection model as an initial value of nonlinear optimization refinement under perspective model equations.
Motion estimation from point correspondences is an important task in computer vision. Generally, computation of nonlinear equations in this field require good initial value, while computation of linear equations require additional constraint to choose correct solution for recovering motion parameters. In this work, we demonstrate main differences for computation of the linear and nonlinear equations in motion estimation. The main contribution of this study is that the result can be help to understand the computation technique on motion estimation from point correspondences.
Abstract: Most of the image processing techniques use edge detection function. Traditional edge detection operator computes the first order derivative, it produces thicker edges of image, and the second order derivative results more sensitive to noise. In this work, we discuss an image segmentation algorithm using global and local image fitting energy based on fractional derivative. This method can preserve more low frequency contour feature, keep high frequency marginal feature and also enhance medium frequency texture details. Furthermore, segmentation algorithm yields good visual effects for various images. We test our method on various images and compare them to other existing methods.
Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation–based approaches under noisy conditions.
We present a new algorithm for determining 3D motion of a moving rigid object in the image sequences relative to a single camera. In the case where features are two-dimensional (2D), they are obtained usually by projective transformations of the 3D features on the object surface under perspective model. The perspective model has formulated in non-linear least square problem to determine 3D motions as characterized by rotation and translation iteratively. In practice, it is numerically ill-conditioned and may converge slowly or even fail to converge, if it starts with not good enough initial guess. However, since paraperspective projection model closely approximates perspective projection for recovering the 3D motion and shape of the object in Euclidean space, we used the results provided from paraperspective projection model as an initial value of non-linear optimization refinement under perspective camera model equations.
We present a new algorithm for determining 3D motion of a moving rigid object in the image sequences relative to a single camera. In the case where features are two-dimensional (2D), they are obtained usually by projective transformations of the 3D features on the object surface under perspective model. The perspective model has formulated in non-linear least square problem to determine 3D motions as characterized by rotation and translation iteratively. In practice, it is numerically ill-conditioned and may converge slowly or even fail to converge, if it starts with not good enough initial guess. However, since paraperspective projection model closely approximates perspective projection for recovering the 3D motion and shape of the object in Euclidean space, we used the results provided from paraperspective projection model as an initial value of non-linear optimization refinement under perspective camera model equations.
Augmented reality (AR) combines real and virtual objects, be interactive in real time, register real and virtual objects, inserted in the scene. In other words, synthetic elements are rendered and aligned in the scene accurately in real time. The solution of this problem can be related to a pose estimation or, equivalently, camera localization process. On the other hand, this is a motion tracking issue. Many sensors have been considered for tracking issues, including mechanical devices, magnetic sensors, inertial devices, GPS, optical sensors. However, these are expensive devices that require special configurations. Instead, it is important to develop AR applications by using vision-based technology to determine pose estimation in image sequences from monocular camera. In this study, we will develop AR application through motion estimations for image sequences taken from single camera.
3D tracking plays a vital role in 3D projects by enhancing optimal control in 3D environments to reduce the time and cost of it. We present various real-time 3D motion estimation approaches developed in computer vision field and compare their performance. The methods developed in computer vision estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented two different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approach were more accurate than essential matrix based approach under noisy situations.
3D tracking plays a vital role in 3D projects by enhancing optimal control in 3D environments to reduce the time and cost of it. We present various real-time 3D motion estimation approaches developed in computer vision field and compare their performance. The methods developed in computer vision estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented two different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approach were more accurate than essential matrix based approach under noisy situations.
3D tracking plays a vital role in 3D applications by enhancing interaction between real and virtual world. We present various real-time 3D motion estimation approaches developed in photogrammetry and computer vision fields and compare their performance. The methods developed in both fields estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented four different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approaches in both fields were more accurate than relative orientation or essential matrix based approaches under noisy situations.
3D tracking plays a vital role in 3D applications by enhancing interaction between real and virtual world. We present various real-time 3D motion estimation approaches developed in photogrammetry and computer vision fields and compare their performance. The methods developed in both fields estimates 3D motion of a moving object relative to a camera or equivalently moving camera relative to the object in an image sequence when its corresponding features are known at different times. We reviewed 3D motion models formulated by different methods related to their geometric properties. We implemented four different methods and analyzed their performance results. Comparison from test datasets from image sequences demonstrated that homography based approaches in both fields were more accurate than relative orientation or essential matrix based approaches under noisy situations.
Загвар танилт нь зураг болон видеоноос хэрэгтэй мэдээллийг гаргаж авах хамгийн түгээмэл арга юм. Энэ арга нь зургийн дараалал дахь зураг тус бүрийн агуулгыг урьдчилан бэлдсэн загвартай харьцуулдаг. Энэхүү судалгааны ажлаар бодит хугацаанд гэрэл сүүдрийн өөрчлөлт болон халхлалттай хавтгай зургийн хувьд боловсруулалт хийж, зурган дараалал дээрхи загвар объектын хөдөлгөөнийг даган тодорхойлох алгоритм боловсруулсан юм.
Загвар танилт нь зураг болон видеоноос хэрэгтэй мэдээллийг гаргаж авах хамгийн түгээмэл арга юм. Энэ арга нь зургийн дараалал дахь зураг тус бүрийн агуулгыг урьдчилан бэлдсэн загвартай харьцуулдаг. Энэхүү судалгааны ажлаар бодит хугацаанд гэрэл сүүдрийн өөрчлөлт болон халхлалттай хавтгай зургийн хувьд боловсруулалт хийж, зурган дараалал дээрхи загвар объектын хөдөлгөөнийг даган тодорхойлох алгоритм боловсруулсан юм.
