Дудкин А. А.; Инютин А.В.; Ваткин М. Е. Алгоритм обработки цветных изображений зараженного пол

Doudkin, A. A.

Inyutin, A. V.

Vatkin, M. E.

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                                Раздел II. Распознавание образов и анализ изображений  26 РАЗДЕЛ II. РАСПОЗНАВАНИЕ ОБРАЗОВ И АНАЛИЗ ИЗОБРАЖЕНИЙ  THE COLOR IMAGES OF INFECTED CROP FIELD PROCESSING ALGORITHM  Doudkin A.A., Inyutin A.V., Vatkin M.E., United Institute of Informatics Problem, National Academy of Science of Belarus, Minsk, Belarus,  Introduction In this work we consider the problem of segmenting agricultural landed-fields in remote sensing data [1-3]. Our approach of agricultural images analysis contain following steps: 1. Blurring the image to avoid influence of noise and JPEG coding artifacts. 2. Color segmentation to detect area of field with different infection levels. 3. Improving segmented images. 4. Calculation of infection map of field.  1. Color Image Segmentation Algorithm Lets consider two-stage method of image segmentation. At the first stage we find sets of pixels which color have small deviation inside of each set and the number of pixels in each set is significant regarding to amount of all pixels. For that purpose we use a clusterization algorithm of pixels in image color space. At the second stage we use some heuristics to make threshold separation of obtained set of clusters into two groups. To the first group belong clusters that include pixels of sought ob-jects and to the second clusters that include pixels of background. Algorithm of image color space clusterization. 1. Let source set of clusters С = ∅. 2. Chose pixel xj from pixels set of X.  X = X \ {xj}. 3. If C is equal ∅ Then: 3.1. C = C ∪ {xj}. Else: 3.2. Chose cluster ci such as || ci – xj || → min. 3.3. If || ci – xj ||<ε Then 3.3.1. Find new value of cluster center  ( ) ( )1++⋅= ijiii nxncc  where ni is the number of pixels added to the cluster ci. 3.3.2. Find new number added pixels to the cluster ci  ni = ni +1. Else 3.3.3. Add new cluster C = C ∪ {xj} 4. If set X is not equal to ∅ then repeat from 2.  Algorithm for finding the sought cluster center for segmentation. 1. Chose cluster сi such as || ci – h || → min where h is color of objects set by user. Set new index i = 1 for сi. 2. Sorting the set С according to rule  ∀ сj , j < N –1, || cj – ci || takes minimal value if i = j +1where j< i ≤ N. Репозиторий БрГТУ                                Раздел II. Распознавание образов и анализ изображений  273. Find dmax = || c1 – cN || 4. Find allowed deviation radius of objects color 100/maxPdR ⋅=  where P is allowed devia-tion percent from chosen object color. User sets value of P. 5. Join сi such as || c1 –ci || < R according to equation i i ii ih c n n = ⋅  ∑ ∑  After applying of mentioned above algorithms we perform threshold segmentation of im-age. If || h –xi || < R, then xi is object pixel else xi is background pixel.  2. Algorithm for improving the image after segmentation on the basis of soft mor-phology operations The task of noise removal from the raster image in mathematical morphology usually is solved with the help of open and close operations, or their sequences. The open destroys nar-row necks and smoothes ledges on the binary image, and the close swallows small hollows and closes gaps. Both open and close have property of idempotency. Hence, repeated using of function with same structuring element will not change the image. If it is required to improve filter outcome the set of structuring elements with a growing size is used. However increasing of a structuring element size reduces a resolving power of the filter. Thus, a shortage of clas-sical morphological filters is low ability of the filter solving that leads to significant distortion of the initial image during filtering.  For noise removal on the image the modification of the soft morphological filter is offered [4]. For which the subset of a structuring element kernel is equal to an empty subset. Define operations of soft erosion and a dilatation in the following way:  >+=≤+==bobontnantnaBASoftErode,1,0),( ,     (3)  >+=≤+==bobontnantnaBASoftDilate,0,1),( ,     (4)  where no is a number of segment pixels that are same to appropriate pixel of the structuring element in current position of the structuring element window, nb is a number of background pixels that are same to appropriate pixel, t is a filter threshold. Thus, the soft open and close operations we can define as:  SotfOpen (A, B) = SoftDilate (SoftErode (A, B), B),     (5)  SotfClose (A, B) = SoftErode (SoftDilate (A, B), B),     (6)  Use of a threshold in the soft morphological filter allows operating the filter outputs. For exam-ple, in open and close operation it is possible to use different thresholds for erosion and dilatations. The soft morphological filter works as follows - makes scanning the image by the structur-ing element. In a window of the structuring element the amount pixel the object (background), concurrent with nonzero pixel of structuring element is calculated. Depending on a function, according to formulas (3) - (4) the pixel value is appropriated.  3. Algorithms of segments description The algorithms of segments description are used to find segments geometrical features. After finding geometrical features of segments we can apply mechanical treatment of field au-tomatically. It is necessary to mention that description of field can be applied to all segments together (statistical approach) or to each segment separately.  Репозиторий БрГТУ                                Раздел II. Распознавание образов и анализ изображений  28 Statistically map of field infection distribution can be evaluate as   ∑∑ ⋅=iiiiiCKCf ,       (7) where Ki is coefficient of infection for i-th level of infection, Ci is a number of pixels that belong to i-th level of infection in window of given size. Thus finding value of f in different position of scanning window we can obtain a map of in-fection distribution. To find features of segments individually we apply following algorithms. Algorithm of segment description using it raster edge pixels. All pixels of image are checked. If pixel p(x,y) is the object pixel and one of neighbor pixels is the background pixel then the pixel is edge pixel. All the edge pixels are sorted correspond-ing to their segments. Corresponding array of edge pixels is chosen by criterion if in the array is the pixel with coordinates that differ from verifiable pixel by 1. If such pixels are found in several edge pixels arrays, that arrays are joint in one array. The image is increased three times to exclude the special processing of exclusive pixels that are members of two different edge pixels arrays. After sorting all edge pixels the coordinates of pixels are set to the original image scale.  Chord segment description algorithm and segments feature calculation.  Chord segment description is represented by two-dimension array of horizontal chords of segment. Chords are ordered by coordinates y and x. Chord segment description can be ob-tained from edge segment description using the following algorithm. 1. Arrange edge pixels by coordinates y and x into two-dimension array. 2. Define to left or right chord end the each pixel belongs. 3. Pick out pare of left and right edge pixels in the increasing order of coordinates x and y. If edge pixel is left and right at the same time then that pixel is repeated two times. Selected pares are stored in new array of segment chords. It is easy to the find following segment features using segments chord description. 1. Segment area ( )∑=−=N1iii xrxlS, where xli , xri are left and right x coordinates of left and right ends of i-th chord, N is number of chords. 2. Geometric center of the segment  MxxM1iic∑== , MyyM1iic∑== , where (xi , yi) are coordinates of i-th edge pixel, M is the number of segment edge pixels.  4. Tests Test was performed using airplane-photo of potato field (fig. 1). Test shows good image segmentation results that allow high quality further processing. Репозиторий БрГТУ                                Раздел II. Распознавание образов и анализ изображений  29  a) b) c) d)  Fig.1 a) source image of field, b) the areas of field that are strongly infected, c) the areas of field that have average infection, d) the areas of field that are healthy  References 1. Margarita Torre, Petia Radeva. "Agricultural-Field Extraction on Aerial Images by Region Competition Algorithm," icpr, vol. 01,  no. 1,  p. 1313,  15th  2000. 2. G. A. Carpenter, S. Gopal, and C. E. Woodcock, "A neural network method for efficient vegetation mapping," Remote Sensing Environment, vol. 70, no. 3, pp. 326--338, Dec. 1999. 3. Yang, C.C.; Prasher, S.O.; Landry, J.A.; Perrett, J., Ramaswamy, H.S. 2000. Recognition of Weeds with Image Processing and their use with Fuzzy Logic for Precision Farming. Cana-dian Agricultural Engineering. 42(4) : 195-200 4. A. Gasteratos and I. Andreadis, Soft Mathematical Morphology: Extensions, Algorithms and Implementations, http://utopia.duth.gr/~agaster/papers/FuzzySoftMorph.pdf The research is partially supported by the Belarussian Republican Foundation of Fundamental Research, grant T04-219.  АЛГОРИТМ УСТРАНЕНИЯ ЭФФЕКТА ПЕРЕСЕГМЕНТАЦИИ С ИСПОЛЬЗОВАНИЕМ ОПЕРАЦИИ ПОЛУТОНОВОГО ПСЕВДОСКЕЛЕТА  Инютин А.В., Объединенный институт проблем информатики НАН Беларуси, Минск  Операция сегментации проводится для нахождения границ однородных областей на изображении. В разных задачах требуется проводить сегментацию по различным призна-кам - по яркости, по текстуре, по цвету. Для сегментации полутонового изображения по яр-кости можно использовать морфологической операции трансформации водораздела [1-4]. Отличительной особенностью данной операции является высокая точность сегментации, относительно невысокая вычислительная сложность и отказ от использования порогов. На вход операции трансформации водораздела подается градиент изображения. Результатом будет являться граница однородных по яркости областей. Толщина грани-цы равна одному пикселю. Связность элементов границы будет зависеть от выбора структурирующего элемента для операции трансформации водораздела. Однако недостаток данного метода - это пересегментация изображения. Под пере-сегментацией понимается наличие большое количество сегментов малого размера на Репозиторий БрГТУ

The color images of infected crop field processing algorithm

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The color images of infected crop field processing algorithm

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