Drawing by dots for kids of lines, shapes and animals. Draw by dots to develop writing skills.
A beautiful underline and successful learning to write depends on the correct possession of a pencil, skillful pressure and the ability to draw lines of various shapes. Start by learning dotted lines and shapes, and then have your child dotted animals and color them.
Draw by dots, developing skills gradually
Drawing lines with a pencil or pen is a great practice to help teach your hand to write, develop small muscles, and teach your baby to hold something tightly.
The dotted line serves as a guide and helps the child, because at any time you can slow down the drawing speed, increase or decrease the pressure on the pencil, without spoiling the picture, and therefore without losing interest.
As soon as the child learns to draw lines, straight lines and all kinds of waves, go to the figures, and then to the animals. The curves of the dotted lines will develop enough drawing skill to start learning the spelling of letters and numbers.
When offering a child a printed material with a picture on which you want to draw something point by point, first ask the child to circle the lines with the index finger of his right hand (or left, if the child is left-handed). Then ask him to draw with his finger not on the sheet, but as if in the air above the picture. Repeat the exercise several times, and then complete the task with a pencil.
When the child learns to draw with dots with a pencil, offer him a pen or marker.
Pay attention to drawing on the points of animals, without taking your hand off the paper.
How else to develop fine motor skills, besides drawing by dots?
If for some reason your child is not interested in dot-to-dot materials, you can have fun developing fine motor skills in other ways.
- String large beads together on strings or sort through beads;
- Glue a large sheet of paper or old wallpaper to the wall and have your child draw their own pictures on the sheet. Drawing on a vertical surface requires more effort and pens train faster;
- As soon as your child is already strong enough to hold small things in his hands and does not let go of them if you pull slightly, start teaching him how to tie shoelaces or weave pigtails from any ribbons or ropes;
- If you read newspapers or magazines, give your child a marker and have him circle all the headlines with it;
- A good thumb-forefinger grip is most easily developed by transferring beans or even peas from one bowl to another using only two fingers, not the whole palm.
- Frosty windows or misted bathroom mirrors are a great place to learn how to draw with your index finger.
If you wish, you can use each of the ways to develop your child's fine motor skills in everyday life, this will help him learn to write faster in the future.
Single color, outline image
First letter "s"
Second letter "and"
Third letter "l"
The last beech is the letter "t"
Answer for the clue "One-color, outline image", 6 letters:
silhouette
Alternative questions in crossword puzzles for the word silhouette
face contour
m. french shot from the shadow, from the side outline of the face
M. Lermontov's poem
Image, outline
Cut out the outline of the object
Word definitions for silhouette in dictionaries
Explanatory dictionary of the Russian language. D.N. Ushakov
The meaning of the word in the dictionary Explanatory dictionary of the Russian language. D.N. Ushakov
silhouette, m. One-color contour image of a person of an object against a background of a different color, drawn or cut out. trans. The vague external outlines of something, visible in the darkness, fog. Here flashed lights, silhouettes of huts. Chekhov. From time to time...
Wikipedia
The meaning of the word in the Wikipedia dictionary
Silhouette - one of the islands of the Seychelles archipelago. Located in the Indian Ocean, belongs to the state of the Seychelles.
Explanatory Dictionary of the Living Great Russian Language, Vladimir Dal
The meaning of the word in the dictionary Explanatory Dictionary of the Living Great Russian Language, Vladimir Dal
m. french shot from the shadow, from the side outline of the face.
Explanatory dictionary of the Russian language. S.I. Ozhegov, N.Yu. Shvedova.
The meaning of the word in the dictionary Explanatory dictionary of the Russian language. S.I. Ozhegov, N.Yu. Shvedova.
-a, m. One-color planar image of an object against a background of a different color. C. face in profile. trans. The outlines of something, visible in the darkness, fog. S. mountain range. Lines, outline of clothes. Modny s. clothes. adj. silhouette, th, th.
Examples of the use of the word silhouette in the literature.
Fighters began to more clearly interact with anti-aircraft artillery, they operated at heights inaccessible to artillery, used a light background above the target, created by luminous bombs, tracked against this background silhouettes of our aircraft, gave a signal to the anti-aircraft gunners to cease fire and went on the attack.
In the direction of Anapa, against the background of clouds, we could already see silhouettes heavy aircraft.
An arrow whistled just above his ear, the crossbowman unloaded his weapon into the silhouette- the magician already raised his hands, preparing to send a spell.
Senior Lieutenant Arseniev looked up from the periscope and rubbed his eyes; silhouettes ships, but he was immediately convinced of the mistake.
The creatures disembarking from ships surpassed all imagination in their silhouettes, similar to coils of a spiral or blooming flowers of arum, with purple bodies and heads resembling starfish.
In this article, you will learn how to paint with a brush based on the created paths.
Let's create the document first, I didn't use a fill or gradient, as you can do it yourself (I hope).
With a tool Feather (Pen) create a line. Then, by right-clicking, we call up an additional menu, where we select Stroke Path.
For a deeper understanding, the pen tool is not a drawing, but if we draw a line with a brush, this is actually equivalent to a line drawn with a brush. Just drawing a beautiful line at once with a brush is quite difficult, which is why we used the pen. So, the stroke menu.
Now choose Brush, i.e. what we want to circle our line.
Check mark "Simulate pressure" (Simulate pressure) controls the line thickness. If you select this option, then with my brush settings, the line will be thinner at first, then thicken towards the middle, and thin again towards the end. If you do not use this option, then the line will be of the same thickness, equal to the diameter of the previously specified brush.
So, here's what I got. Since we will no longer need the curve created by the pen, we will delete it - right-click, call an additional menu, where we select "Delete contour" (Delete pass).
Finally, we can create a brush from the resulting drawing. Holding down a key ctrl, click on the layer in the layers panel, thus loading the selection.
See you in the next lesson!
Institute of Electronic and Information Systems of NovSU, [email protected]
Contour analysis methods are considered, which are optimally used in real-time systems to highlight the contours of objects in a video sequence.
Keywords: contour, image processing, contour analysis, video surveillance system
Introduction
Image segmentation based on contouring is considered for solving this class of problems due to the fact that changing the parameters of the position, rotation and scale of the image has little effect on the amount of calculations. In addition, the contours entirely determine the shape of the image, weakly depend on color and brightness, and contain the necessary information for further classification of the object. This approach makes it possible not to consider the internal points of the image and thereby significantly reduce the amount of processed information due to the transition from the analysis of a function of two variables to a function of one variable. The consequence of this is the possibility of ensuring the operation of the processing system on a time scale closer to real time.
Basic concepts
Under the image contour we will understand a spatially extended gap, drop or abrupt change in brightness values.
An ideal drop has the properties of the model shown in fig. In reality, optical limitations, discretization, etc. lead to blurry brightness differences. As a result, they are more accurately modeled by an inclined profile similar to that shown in Fig. 1b. In such a model, the brightness drop point is any point lying on the inclined section of the profile, and the drop itself is a connected set formed by all such points.
Figure 1 Model of ideal (a) and oblique (b) brightness differences
The difference in brightness is considered a contour if its height and angle of inclination exceed some threshold values .
We note a number of problems that arise during the selection of the contour:
Contour breaks in places where the brightness does not change quickly enough;
False contours, due to the presence of noise in the image;
Unnecessarily wide contour lines due to blurring, noise, or due to the shortcomings of the algorithm used;
Inaccurate positioning due to line outlines having a width of one rather than zero.
Differential Methods
One of the most obvious and simple ways to detect edges is to differentiate the brightness, considered as a function of spatial coordinates.
Detection of contours for an image with brightness values f(x1,x2) perpendicular to the x1 axis ensures taking the partial derivative df/dx1, and those perpendicular to the x2 axis - the partial derivative df/dx2. These derivatives characterize the rates of brightness change in the x1 and x2 directions, respectively. To calculate the derivative in an arbitrary direction, you can use the brightness gradient:
grad f (x1, x2) = f (x1, x2).
Gradient - a vector in two-dimensional space, oriented in the direction of the most rapid increase of the function f (x1, x2) and having a length proportional to this maximum speed. The modulus of the gradient is calculated by the formula
Figure 2 Graphical representation of the gradient
To highlight the contour of an arbitrary direction, we will use the brightness field gradients module. For images, we take discrete differences instead of derivatives.
Roberts operator
One option for computing a discrete gradient is the Roberts operator. Since differences in any two mutually perpendicular directions can be used to calculate the gradient modulus, diagonal differences are taken in the Roberts operator:
The difference definition is formed by two finite impulse response (FIR) filters whose impulse responses correspond to 2x2 masks
The disadvantages of this operator include high sensitivity to noise and orientation of the boundaries of the regions, the possibility of discontinuities in the contour, and the absence of a pronounced center element. And he has one advantage - low resource consumption.
Sobel and Prewitt operators
In practice, it is more convenient to use the Sobel and Prewitt operators to calculate discrete gradients. For the Sobel operator, the influence of the noise of corner elements is somewhat less than for the Prewitt operator, which is essential when working with derivatives. For each of the masks, the sum of the coefficients is equal to zero, i.e. these operators will give zero response on areas of constant brightness.
FIR filters are 3x3 masks.
Sobel operator masks:
Prewitt operator masks:
The Sobel operator uses a weighting factor of 2 for the middle elements. This increased value is used to reduce the effect of smoothing by giving more weight to the midpoints.
To address the issue of rotational invariance, so-called diagonal masks are used to detect discontinuities in diagonal directions.
Diagonal masks of the Sobel operator:
Diagonal Prewitt operator masks:
In the presence of a central element and low resource consumption, this operator is characterized by high sensitivity to noise and orientation of the boundaries of regions, as well as the possibility of discontinuities in the contour.
Figure 3. Edge detection by the Sobel operator: a) original image; b) the result of applying the Sobel operator
Laplacian
To solve the problem of highlighting brightness differences, you can apply differential operators of a higher order, for example, the Laplace operator:
In the discrete case, the Laplace operator can be implemented as a procedure for linear image processing with a 3x3 window. The second derivatives can be approximated by the second differences:
The Laplacian takes both positive and negative values, so in the edge detection operator, you need to take its absolute value. Thus, we obtain a boundary detection procedure that is insensitive to their orientation
The role of the Laplacian in segmentation problems is to use its zero-crossing property to localize the contour and find out whether the considered pixel is on the dark or light side of the contour.
The main disadvantage of the Laplacian is its very high sensitivity to noise. In addition, the appearance of gaps in the circuit, as well as their doubling, is possible. Its advantages include the fact that it is insensitive to the orientation of the boundaries of the regions, and low resource consumption.
Local processing
Ideally, edge detection methods should select only the pixels that lie on the edge in the image. In practice, this set of pixels rarely renders the contour accurately enough due to noise, broken contours due to non-uniform illumination, and the like. Therefore, contour detection algorithms are usually supplemented with linking procedures to form sets of contour points containing contours.
One way to link edge points is to analyze the characteristics of the pixels in a small neighborhood of each point in the image that has been marked as edge. All points that are similar according to some criteria are connected and form a path consisting of pixels that meet these criteria. This uses two main parameters to establish the similarity of the contour pixels: the magnitude of the response of the gradient operator, which determines the value of the contour pixels, and the direction of the gradient vector.
A pixel in a given neighborhood is combined with the central pixel (x, y) if the criteria for similarity in both magnitude and direction are met. This process is repeated at each point of the image with simultaneous memorization of the found associated pixels as the center of the neighborhood moves. A simple way to account for the data is to assign a different brightness value to each set of linked pixels in the path.
Canny border detector
The Canny edge detector is guided by three main criteria: good detection (increasing the signal-to-noise ratio); good localization (correct determination of the position of the border); the only response to one boundary.
From these criteria, an objective function of the cost of errors is constructed, minimizing which is found the optimal linear operator for convolution with the image.
To reduce the sensitivity of the algorithm to noise, the first Gaussian derivative is applied. After applying the filter, the image becomes slightly blurry. Here is what the Gaussian mask looks like:
After calculating the gradient of the smoothed image, only the maximum points of the image gradient are left in the border contour. Information about the direction of the boundary is used in order to remove points exactly near the boundary and not to break the boundary itself near the local maxima of the gradient .
The Sobel operator is used to determine the direction of the gradient. The resulting values of the directions are rounded up to one of four angles - 0, 45, 90 and 135 degrees.
Weak boundaries are then removed using two thresholds. The border fragment is treated as a whole. If the gradient value somewhere on the traced fragment exceeds the upper threshold, then this fragment also remains the “permissible” boundary in those places where the gradient value falls below this threshold, until it falls below the lower threshold. If there is not a single point on the entire fragment with a value above the upper threshold, then it is deleted. This hysteresis reduces the number of discontinuities in the output boundaries.
The inclusion of noise reduction in the algorithm increases the stability of the results, but increases the computational cost and leads to distortion and loss of edge detail. The algorithm rounds the corners of objects and destroys the boundaries at the connection points.
The disadvantages of this method are the complexity of implementation and very high resource consumption, as well as the fact that some rounding of the corners of the object is possible, which leads to a change in the parameters of the contour.
The advantages of the method include low sensitivity to noise and orientation of the boundaries of regions, the fact that it clearly highlights the contour and allows you to identify the internal contours of the object. In addition, it eliminates the erroneous detection of the contour where there are no objects.
Figure 4. Border selection by the Canny method: a) original image; b) after processing by the Canny algorithm
Analysis with Graph Theory
Representing it as a graph and searching the graph for the least cost paths that correspond to meaningful contours allows us to construct a method that works well in the presence of noise. Such a procedure is quite complicated and requires more processing time.
Figure 5. Path element located between pixels p and q
The contour element is the border between two pixels p and q that are neighbors. The contour elements are identified by the coordinates of the points p and q. The contour element in Fig. 5 is determined by the pairs (хр, yr)(хq, yq). A contour is a sequence of contour elements connected to each other.
The task of finding the minimum cost path on a graph is non-trivial in terms of computational complexity, and one has to sacrifice optimality in favor of computational speed.
The complexity of implementation and high resource consumption are the main disadvantages of such an analysis, the advantage of which is a low sensitivity to noise.
Conclusion
The methods presented in the paper describe the optimal approaches for contour detection in real-time systems. The methods allow solving a wide range of contouring tasks, which are used in many areas where image segmentation is necessary.
Literature
1. Gonzalez R., Woods R. Digital image processing. M.: Technosfera, 2005. S.812-850.
2. Yane B. Digital image processing. M.: Tekhnosfera, 2007. S.331-356.
3. Methods of computer image processing / Ed. V.A. Soifer. M.: Fizmatlit, 2003. S.192-203.
4. Pret W. Digital image processing. M.: Mir, 1982. S.499-512.
5 See: http://www.cs.berkeley.edu/~jfc/
