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Powerpoint Matching Pairs Game Template

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Template Matching

Transcript: X Object Identification Assume white background, black sketch Convert to a monochrome image Threshold and erode to get relevant values only erode? erosion? Represent the shape by means of a skeletonized image that highlights significant features. Feature Extraction why do that? Feature Vector Template Matching 3 O O O O O O O O 4 1 O O ? O O O O O O O O O O O O O O Template Based Approach Instead O O Feature Based Approach O X O O X O O O O O O The smaller the distance means that images are "similar" Object Identification Transform the input image into a set of features Use this instead of the full size input 2 O O O O O O O O O O O O O Extract features from the image Feature Based Approach Compare results with those from the template Euclidean Distance Erosion or Skeletonization Feature Extraction Process applied to eliminate the boundary points from an object. Results Simplify the amount of information required to describe data accurately __ Calculate the Orientation > Calculate the center of gravity (COG) Compare eliminating effects due to rotation, translation and scaling V ? Questions Composition of the Feature Vector Based on the COG and orientation take a measurement vector every 5 degrees For each measurement record length and angle COG Orientation Origin of the image shape relative to its position Defined as the angle of axis of least moment of inertia Eliminate Translation Effect Eliminate Rotation Effect Normalization Divide the length of each vector by the max length Eliminate Scaling Effect


Transcript: DESIGN GOAL OBJECTIVE PRINCESS MEMORY GAME INTRODUCTION INTRODUCTION Games that require players to match similar elements. As the name implies, participants need to find a match for a word, picture, or card. For example, students place 30 word cards; composed of 15 pairs, face down in random order. Each person turns over two cards at a time, with the goal of turning over a matching pair, by using their memory. This is also known as the Pelmanism principle, after Christopher Louis Pelman, a British psychologist of the first half of the 20th century. OBJECTIVE OBJ1 OBJECTIVE 1 • To train visual memory OBJECTIVE 2 • Show the users how good their visual memories are and suggest strategies for improving their memory skills. OBJ2 OBJECTIVE 3 • To Improve concentration OBJ3 OBJECTIVE 4 • To increase short term memory OBJ4 OBJ5 OBJECTIVE 5 • To increase attention to detail • To improve the ability to find similarities and differences in objects OBJECTIVE 6 OBJ6 • To help to classify objects that are grouped by similar traits OBJECTIVE 7 OBJ7 TARGET USER The main target is for children from the age of 4 to 7 years old. However, for any age you can play this game. TARGET USER/PLAYER DESIGN GOAL The design goal of this game is to develop a game that can train users memory hence to boost their visual memory storage capacity in a playful way. When using this game , the users memory can memorize the location of different cards. Interface Memory Game Sketch the story board Interface 1 Interface 2 QUESTIONNAIRE Result of interview or questionnaire Respondent 1 Respondent 2 Respondent 3 Respondent 4 Respondent 5 CONCLUSION CONCLUSION In conclusion, this game is quite entertaining to fill in the free time. With a simple way to play and attractive appearance, this game can be played by all ages. The only disturbance is the presence of advertisements in the form of videos that cannot be skipped, so players must see the ad videos with an average duration of 15 or 30 seconds. In addition, matching Images are available for the Android and iOS operating systems.


Transcript: object location detection in the images . robot path registration. [1] To distinguish the matching territory, we need to look at the template image against the source image by sliding it. By sliding means, by moving the patch's one pixel at once left to right and top to bottom. Matching result cross correlation Load the template image and source image. Matching result Detected point This technique matches a template respect to its mean against the image with respect to its mean. so a flawless match will be 1 and an immaculate mismatch will be –1. a value of 0 just implies that there is no connection or irregular arrangements. Figure 2.3(b) Cross correlation normed 11008320 Sum of squared differences Images By Dheeraj Kumar Yadav Mandli Under the guidance of Prof. Christof Jonietz ALGORITHM ccoeff_normed ccorr_normed Contd.. Figure 1.7 Figure 2.2(b) RESULTS For every pixel position in the overlay, the template pixel value will be multiplied by the source image pixel value. Aggregate every one of the items together to get a "score" for the overlay. the various techniques to match the images are as follows: Figure 1.5 what is it? Figure 1.4 Template matching is a technique which matches the sample template with the source image . THANK YOU one of 6 template matching techniques in cv::matchTemplate(OpenCV function) is used .* For every area of T over I, metric is put away in the resultant matrix (R). Every area (x,y) in R contains the match metric . Utilizing the cv::minMaxLoc function in the OpenCV to find the most astounding worth (or lower, depending of the sort of matching method) in the R matrix . Confine the area with higher matching likelihood ,Draw a black color rectangle around the region relating to the most elevated match. [1] [1] [1] Figure 1.9 SUM OF SQUARED DIFFERENCES Square differences normed Cross correlation Coefficient normed Detected point This method will match the squared difference between the template and the source image. so a exact match will be '0' and poor matches will be large. Sum of Squared differences(sq_diff) Cross correlation(ccorr) Cross correlation Coefficient(ccoeff) Normalization -Sqdiff -ccorr -ccoeff figure 1.1 -, figures 1.2-2.4- results from the opencv program of template matching. Matching result Detected point Normalization Figure 1.2 Matching methods Introduction Sq_diff_normed [1] TEMPLATE MATCHING Figure 2.0 Figure 1.8 Cross correlation Figure 2.1 cross correlation Coefficient Figure 2.4(a) [1] [1] Figure 2.4(b) Figure 2.2(a) REFERENCES Cross correlation coefficient Figure 1.3 Figure 1.6 Figure 1.1 [1] Gary Bradski and Adrian Kaehler, “Learning OpenCV,” O’Reilly Media,September 2008: First Edition [2] Richard Szeliski,”Computer Vision:Algorithms and applications”,Sep3-2010 draft. [3] [4] NORMALIZATION Figure 2.3(a) why we need it?

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