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• IMAGE MATCHING SYSTEM WITH USING INTUITIONISTIC FUZZY SETS

  К.I. Morev

  286-298

  2026-04-29

  Abstract ▼

  This paper presents a fully learnable system for solving the problem of matching two images. All the main elements of the system are trainable, i.e. their final form corresponds to the target dataset on which the training was carried out. The fact that the system is trainable, the methods used in training and the architecture of the system allow using the system to solve a large number of various computer vision problems. The system consists of a convolutional neural network that serves both to extract key points and their descriptors, as well as a trainable matcher of the extracted key points based on their description and mutual arrangement in the observed scene. The used convolutional neural network processes full-size images and calculates both the location of interest points with pixel accuracy and the descriptors associated with them in a single forward pass. Matching key points is a separate step and is performed after the forward pass of the neural network. In the process of training the model for calculating the positions of key points and their descriptors, a method for forming a training sample is used, called homographic adaptation - an approach that helps to increase the repeatability and accuracy of detecting key points. The process of training the feature point detection model consists of obtaining new weights in the process of additional training of the base detector, which represents the initialization weights of the model. The final feature point detection model, trained on the universal MS-COCO image set using homographic adaptation, repeatedly outperforms the original base detector in terms of the number, reliability and repeatability of feature points, and also outperforms any other traditional corner detector based on classical approaches

• THE METRICS FOR TRACKING ALGORITHMS EVALUATION

  А. Е. Shchelkunov, V.V. Kovalev, K.I. Morev, I.V. Sidko

  2020-07-10

  Abstract ▼

  The work is devoted to a review of existing metrics for assessing the quality of the task of tracking objects on video with various algorithms. When evaluating tracking algorithms for their subsequent com-parison, it is not enough to use one metric, and algorithms should be evaluated using a set of different independent estimates. To this end, a study was conducted of existing metrics for evaluating algorithms, the results of which are given in the article. The review involves many different approaches to evaluating algorithms. For example, approaches based on the assessment of the definition of the center of the track-ing object, which are one of the first and still popular metrics for evaluating tracking algorithms. The main disadvantages of such approaches include the difficulty of determining the true center of the object, as well as the interpretation of estimates for various sizes of the object. To eliminate these shortcomings, anew metric is introduced in the article: an unbiased (window) error in determining the center of an object, which takes into account the constant component of the error in determining the center. Other approach-es include metrics based on the analysis of the intersection over union. Also, the article considers ap-proaches based on the analysis of tracking failures, which take into account the tracking length and fail-ure rate. A new method is proposed for evaluating algorithms in case of loss of visual contact with an tracking object, taking into account the number of frames in which visual contact with the object was lost. During the study, approaches to evaluating algorithms for simultaneous tracking of several objects were considered. Integral metrics were proposed whose task is to obtain a comprehensive assessment of the tracking algorithm. For the formation of a comprehensive assessment, it is desirable to use various un-correlated metrics. Complex estimates provide the ability to compare algorithms with each other. As a comprehensive assessment, the article proposes the use of a metric combining the accuracy and robust-ness of the algorithm. As a rule, the intersection over union is used as the accuracy metric, however, for problems where the accuracy of tracking the center of the object is fundamental, the authors propose using an unbiased error in determining the center as the accuracy metric.

• IMAGE MATCHING USING DIFFERENT KEYPOINTS TYPES

  K. I. Morev , A.V. Bozhenyuk

  2020-10-11

  Abstract ▼

  The work is devoted to experiments with various methods of selecting special points on images,
  followed by their description with a binary descriptor and comparison by a full search method.
  This paper actively uses the method of describing the neighborhood of singular points, based
  on the construction of a binary string that characterizes changes in the brightness of pixels in the
  described neighborhood. The resulting string is obtained by comparing the brightness of pixels
  according to a specific template. Today, the use of special points when working with images allows
  you to develop applied methods in various areas of computer vision with increased requirements
  for working time and resistance to sudden changes in scenes. The paper presents the results
  of experiments with special points of various classes, the classification is given in section 1. During
  the experiments, methods implemented in the OpenCV library were used. The paper provides
  brief descriptions of the methods used in experiments. Section 1 of the paper offers a classification
  of modern types of singular points of images and provides a brief description of popular methods
  for detecting the described types of singular points. In section 2, the authors give a General description
  of methods for working with special image points. Section 3 describes the experiments
  that are being carried out with the comparison of special points of different types described by a
  single descriptor, and reveals their results. The experiments performed allow us to identify the
  strengths and weaknesses of bundles of different types of singular points when comparing them.

• EXPERIMENTAL ESTIMATION OF ERRORS IN RECONSTRUCTING THE STRUCTURE OF THE OBSERVED SCENE FROM A SERIES OF IMAGES BY VARIOUS CAMERAS

  К.I. Morev, P.А. Lederer

  2024-04-16

  Abstract ▼

  The article is devoted to the study of the influence of using various mathematical models of cameras,
  and therefore models of scene image formation, when restoring the 3-D structure of a scene from a
  set of 2-D images during camera movement (restoring the structure from motion, hereinafter referred to
  as LEDs). A comparative assessment is carried out for two camera models: the classic central projection
  camera model and the relatively new omnidirectional camera model. The article provides a brief
  description of the mathematical model of an omnidirectional camera, the described model is used during
  experiments, and also describes ways to represent images from omnidirectional cameras. Additionally,
  a description of the mathematical model of the classical camera of the central projection is given.
  The described model is also used during experiments. The analytical calculations used in solving the
  problem of restoring structure from motion are briefly mentioned in the article. An algorithm for obtaining
  3-D coordinates of the points of the observed scene from a sequence of images in motion is also
  described. The experiments carried out as part of the study are described in detail in this article. The
  process of setting visual landmarks and determining their true 3-D coordinates is revealed. The steps for
  the formation of data sets for obtaining comparative estimates are described. At the end of the work, an
  analysis of the experimental results is given, models are identified that reduce the errors in restoring the
  3-D coordinates of the observed visual landmarks