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Computer Vision-Become an ace of Computer Vision, Detect Shapes and Create Apps using Python, OpenCV, TensorFlow, etc.
An excellent training about Data Science
Mastering Computer Vision-Theory & Projects in Python
Comprehensive Course Description: Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. In the CV process, real-world images and videos are captured, processed, and analyzed to allow machines to extract contextual, useful information from the physical world. Until recently, computer vision functioned in a limited capacity. But due to the recent innovations in artificial intelligence and deep learning, this field has made great leaps. Today, CV surpasses humans in most routine tasks connected with detecting and labeling objects. The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you to understand the digital imaging process and identify the key application areas of CV. The course is: Easy to understand. Descriptive. Comprehensive. Practical with live coding. Rich with state of the art and updated knowledge of this field. Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The Homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations. The two hands-on projects in the last sectionChange Detection in CCTV Cameras (Real-time) and Smart DVRs (Real-time)make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field. The course tutorials are divided into 320+ videos along with detailed code notebooks. The videos are available in HD, and the total runtime of the videos is 27 hours+.Now is the perfect time to learn computer vision. Get started with this best-in-class course without any further delay! Teaching is our passion: In this course, we apply the proven learning by doing methodology. We build the interest of learners first. We start from the basics and focus on helping you understand each concept clearly. The explanation of each theoretical concept is followed by practical implementation. We then encourage you to create something new out of your learning. Our aim is to help you master the basic concepts of CV before moving onward to advanced concepts. The course material includes online videos, course notes, hands-on exercises, project work, quizzes, and handouts. We also offer you learning support. You can approach our team in case of any queries, and we respond in quick time. Course Content: The comprehensive course consists of the following topics:1. Introductiona. Introi. What is computer vision?2. Image Transformationsa. Introduction to imagesi. Image data structureii. Color imagesiii. Grayscale imagesiv. Color spacesv. Color space transformations in OpenCVvi. Image segmentation using Color space transformationsb. 2D geometric transformationsi. Scalingii. Rotationiii. Sheariv. Reflectionv. Translationvi. Affine transformationvii. Projective geometryviii. Affine transformation as a matrixix. Application of SVD (Optional)x. Projective transformation (Homography)c. Geometric transformation estimationi. Estimating affine transformationii. Estimating Homographyiii. Direct linear transform (DLT)iv. Building panoramas with manual key-point selection3. Image Filtering and Morphologya. Image Filteringi. Low pass filterii. High pass filteriii. Band pass filteriv. Image smoothingv. Image sharpeningvi. Image gradientsvii. Gaussian filterviii. Derivative of Gaussiansb. Morphologyi. Image Binarizationii. Image Dilationiii. Image Erosioniv. Image Thinning and skeletonizationv. Image Opening and closing4. Shape Detectiona. Edge Detectioni. Definition of edgeii. Nave edge detectoriii. Canny edge detector1. Efficient gradient computations2. Non-maxima suppression using gradient directions3. Multilevel thresholding- hysteresis thresholdingb. Geometric Shape detectioni. RANSACii. Line detection through RANSACiii. Multiple lines detection through RANSACiv. Circle detection through RANSACv. Parametric shape detection through RANSACvi. Hough transformation (HT)vii. Line detection through HTviii. Multiple lines detection through HTix. Circle detection through HTx. Parametric shape detection through HTxi. Estimating affine transformation through RANSACxii. Non-parametric shapes and generalized Hough transformation5. Key Point Detection and Matchinga. Corner detection (Key point detection)i. Defining Cornerii. Nave corner detectoriii. Harris corner detector1. Continuous directions2. Tayler approximation3. Structure tensor4. Variance approximation5. Multi-scale detectionb. Project: Building automatic panoramasi. Automatic key point detectionii. Scale assignmentiii. R
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