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• CLASSIFIER OF IMAGES OF AGRICULTURAL CROPS SEEDS USING A CONVOLUTION NEURAL NETWORK

  V. A. Derkachev, V. V. Bakhchevnikov, A. N. Bakumenko

  2020-11-22

  Abstract ▼

  This article discusses the creation of a convolutional neural network architecture that classifies
  images of crops (in particular wheat) for subsequent use in an optical seed separator (photo
  separator). Interest in the design of neural networks for classifying images has recently increased
  significantly, which is associated both with the development of the theory of deep neural networks
  and the increased computing power of desktop computers, as well as the transfer of computing to
  graphic processors. The aim of the article is to develop the architecture of a neural network that
  allows the separation of the input flow of wheat seeds into two classes: “good” seeds and “bad”
  (with defects in shape and color) seeds. The architecture of the resulting neural network is convolutional,
  because, unlike a fully connected one, this class of neural networks is within certain limits
  immune to changes in the scale and angle of rotation of objects in the input data. In the work,
  for the formation of training, validation and test samples, seed images obtained using a household
  camera were used, which negatively affected the results of training and testing the neural network
  regarding the possible result of application in a real photo separator. The architecture of the developed
  neural network is preliminarily optimized for use on FPGAs, however, in the considered
  case, the transition from the values of weighting factors from the data type from a floating point to
  an integer type has not been made, which can lead to a decrease in the accuracy of the neural
  network, while significantly reducing the amount of resources FPGA. Application of the proposed
  architecture allows one to obtain a fairly accurate estimate of classified wheat seeds from verification
  and test data sets.

• 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.