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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

MATCHING GAME

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.

Template matching

Transcript: Template Matching Pattern Recognition Bita Baroutian Narges Razizade Introduction Image Processing Vehicle tracking Robotics Medical imaging Object detection Source image Template image Source image Template image Approaches Template-based Feature-based Strong feature Template-based Template-based scale invariant orientation invariant Feature-based Feature-based Template-based Compare Feature-based Image pyramid What if we want to find larger or smaller eyes? Image pyramid Multi-angle matching Advanced method grayscale-based matching Grayscale-based matching Edge-based matching Edge-based matching Methodology Measures of matching Template matching measures Measure of match between two images is considered to be a metric that indicate the degree of similarity or dissimilarity between them. Measures of match Measures of match: 1.Based on optimal path searching: Representation: Representing template by a sequence of measurement vectors: Measures of match: A grid with I points (template) in horizontal and J points (test) in vertical. Each point (i,j) of the grid measures the distance between r(i) and t(j). Each path is associated with a cost: K : number of nodes across the path Measures of match: The optimal path is constructed by searching among all allowable paths. The optimal node correspondence, between the test and reference patterns, is unraveled by backtracking the optimal path. Measures of match: 2. Euclidean Distance: I : gray level image g : gray-value template of size n * m (r,c) : top left corner of template g Measures of match: 3. Edit Distance: Dealing with patterns that consist of sets of ordered symbols. Wrongly identified symbol ("befuty" instead of "beauty") Insertion error ( "bearuty") Deletion error ("beuty") Measures of match: 3. Edit Distance: The similarity between two patterns is based on the cost of converting one to another. if having the same length = number of different symbols D(A,B) = minimum total number of changes C, insertions I ,and deletions R required to change pattern A into pattern B. Measures of match: 3. Edit Distance: equality deletion change insertion Measures of match: 4. Measures based on correlation: to find whether a specific known reference pattern resides within a given block of data. target detection, robot vision, video coding. Steps: 1. Move the reference pattern to all possible positions and compute the similarity . 2. Computing the best matching value : Measures of mismatch: based on the pixel-by-pixel intensity differences between the two images f and g. Measures of mismatch Measures of mismatch Measures of mismatch: 1. Root mean square distance(RMS): 2. Sum of absolute differences (SAD): Diff(xs,ys,xt,yt) = |Is(xs,ys)-It(xt,yt)| Problems with template matching: problems with template matching problems Problems with template matching: 1. The template represents the object as we expect to find it in the image 2. The object can indeed be scaled or rotated 3. requires a separate template for each scale and orientation 4. too expensive, especially for large templates 5. Sensitive to: -noise -occlusions Template matching applications: Template matching applications 1. Template matching with various average face pyramid levels 2. 3D reconstruction 3. Motion detection 4. Object recognition 5. Panorama reconstruction

TEMPLATE MATCHING

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 - http://www.engineersonline.nl/wosimages/nieuws_26299_31883_item_original.jpg, 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] http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html [4] http://docs.opencv.org/3.1.0/d4/dc6/tutorial_py_template_matching.html NORMALIZATION Figure 2.3(a) why we need it?

Template Matching

Transcript: Assets Mary Wollstone Craft Elizabeth & Lashmi Comment Feature-based Matching Applications in Real-world Scenarios Statistical Matching Importance in Image Processing Template matching is critical in diverse sectors such as medical imaging, facial recognition, and automated inspection. For example, it is used in medical diagnostics to match patterns in MRI scans, improving accuracy in disease detection. Template matching serves as a reliable method for image recognition, facilitating tasks like object detection and tracking. Its straightforward approach enables quick implementation in real-time applications, making it invaluable in areas such as surveillance, robotics, and quality control. Statistical matching analyzes pixel values statistically to find similarities between the template and the target image. Methods like Mean Squared Error (MSE) or Normalized Cross-Correlation are used, providing a statistical framework for matching accuracy. Feature-based matching identifies distinct features within the template and the target image, such as corners or edges. This technique is robust to changes in orientation and scale, facilitating effective recognition in varied environments. Normalized Cross-Correlation (NCC) Definition of Template Matching Normalized Cross-Correlation (NCC) addresses the limitations of standard cross-correlation by normalizing intensity variations. This allows for more robust matching under differing lighting conditions, making it a preferred choice in various computer vision applications. Template matching is an image processing technique that locates and identifies a sub-image (template) within a larger image. It operates by sliding the template across the image and assessing similarity at each position, making it suitable for various visual recognition tasks. did he write a book? Edge-based Matching Cross-Correlation Full Template Matching Edge-based matching focuses on detecting edges within images, utilizing gradients to identify shapes and contours. This method enhances accuracy in identifying objects irrespective of texture or color variations, thus improving overall matching performance. Cross-correlation measures the similarity between a template and image by sliding the template over the image and calculating the correlation coefficient. Higher values indicate a better match, often used in location identification and object recognition tasks. In this book, Wollstonecraft argues that women are not naturally inferior to men, but that they have been denied the education and opportunities necessary to fully develop themselves.She advocates for gender equality, promoting the idea that women should have access to education and the ability to participate in public and political life. Sum of Squared Differences (SSD) Full template matching involves searching for a complete and exact match of a template image within a target image. This method excels when the object’s scale, rotation, and lighting are consistent, ensuring high accuracy during detection. The Sum of Squared Differences (SSD) calculates the sum of the squared differences between pixel intensities of the template and the image. This method is efficient but sensitive to noise and illumination changes, making it important to pre-process images before application. Methods of Template Matching Template matching employs various methodologies to accurately detect and match templates within images. This section outlines pivotal techniques including cross-correlation, SSD, NCC, and edge-based matching, highlighting their unique applications and strengths. Types of Template Matching Template matching encompasses various types, each tailored to specific image processing requirements. Understanding these types is crucial for selecting the most effective method for different applications. Real-time Processing Advances Enhanced Accuracy Solutions Advancements in computational speed allow for real-time template matching applications. These innovations are critical in fields like autonomous navigation and surveillance, where timely decisions are essential. Novel algorithms are being developed to mitigate common errors in template matching. Techniques like deep learning-based approaches provide significant improvements in identifying objects in complex scenes. Tools and Libraries for Template Matching Variability in Scale OpenCV Challenges in Template Matching Template matching can be efficiently implemented using several robust tools and libraries that provide various functionalities tailored for image processing. These tools help developers and researchers streamline their workflows and enhance accuracy in matching templates across images. Variability in scale is a significant challenge as objects may appear larger or smaller in images due to distance or zoom levels. This can lead to inaccurate matching unless templates are adapted for multiple scales or multi-scale approaches are employed. OpenCV (Open Source Computer Vision Library)

powerpoint template

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