BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Africa/Cairo BEGIN:DAYLIGHT DTSTART:20260424T010000 TZOFFSETFROM:+0200 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=-1FR;BYMONTH=4 TZNAME:EEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261029T230000 TZOFFSETFROM:+0300 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1TH;BYMONTH=10 TZNAME:EET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261012T174310Z UID:623F2098-59CB-4675-ABFA-27233E387048 DTSTART;TZID=Africa/Cairo:20261014T190000 DTEND;TZID=Africa/Cairo:20261014T202000 DESCRIPTION:Computer vision is one of the most exciting fields in artificia l intelligence\, enabling computers and robots to interpret and understand visual information from the world around them. From detecting objects and recognizing faces to estimating depth and enabling autonomous navigation\ , computer vision bridges the gap between raw camera data and intelligent decision-making.\n\nThis webinar introduces participants to the fundamenta ls of computer vision through a practical\, hands-on-oriented exploration of how vision systems work. We will begin by examining how cameras capture light and convert it into digital image data\, including the role of imag e sensors\, pixels\, color channels\, and image frames. We will then explo re how software tools such as OpenCV can be used to process\, manipulate\, and extract useful information from these images.\n\nParticipants will be introduced to key computer vision techniques\, including color-based dete ction\, image features\, Histograms of Oriented Gradients (HOG)\, and basi c facial feature analysis. We will also explore the differences between mo nocular and stereo vision\, discussing how a single camera can be used to identify visual features and how two cameras can help estimate depth and u nderstand spatial relationships.\n\nThe session will then progress from tr aditional computer vision techniques to modern deep-learning-based approac hes\, introducing object detection models such as YOLO and RF-DETR and exp laining how these models identify\, classify\, and locate objects within i mages.\n\nFinally\, we will connect these concepts to real-world robotics through our experience with the Vulcan competition\, demonstrating how cam era-based perception can support autonomous robotic systems. We will explo re how a robot can use visual information\, including object recognition a nd color detection\, to interpret its environment and make navigation deci sions\, even when relying on a camera as its primary perception sensor.\n\ nWhether you are interested in artificial intelligence\, robotics\, autono mous systems\, or software development\, this webinar will provide a found ation for understanding computer vision and the tools and techniques used to build practical vision-based applications.\n\nNo prior experience in co mputer vision is required. A basic familiarity with programming is helpful but not essential.\n\nMeeting Link: https://meet.google.com/dmg-fypi-wqc\ n\nSpeaker(s): Allen\, \n\nAgenda: \nAGENDA: INTRODUCTION TO COMPUTER VISI ON\n\n1. Welcome and Introduction to Computer Vision\n\n-\nWhat is compute r vision?\n\n-\nComputer vision vs. image processing vs. artificial intell igence\n\n-\nReal-world applications of computer vision in robotics\, heal thcare\, security\, and autonomous systems\n\n2. How Cameras See the World \n\n-\nHow light is captured by image sensors and converted into digital i mages\n\n-\nUnderstanding pixels\, resolution\, RGB/BGR color channels\, a nd image frames\n\n-\nCamera data pipelines and frame rates\n\n-\nHow raw camera data becomes useful information\n\n3. OpenCV and Fundamentals of Im age Processing\n\n-\nIntroduction to OpenCV\n\n-\nReading\, displaying\, a nd processing images and video frames\n\n-\nImage resizing\, cropping\, fi ltering\, thresholding\, and drawing\n\n-\nUnderstanding color spaces\n\n- \nPractical demonstration: detecting colored objects using OpenCV\n\n4. Tr aditional Computer Vision Techniques\n\n-\nImage features\, edges\, shapes \, and contours\n\n-\nColor detection and image segmentation\n\n-\nIntrodu ction to Histograms of Oriented Gradients (HOG)\n\n-\nBasic facial feature analysis and face detection\n\n-\nTraditional computer vision vs. machine -learning-based approaches\n\n5. Monocular vs. Stereo Vision\n\n-\nUnderst anding single-camera (monocular) vision\n\n-\nHow stereo cameras estimate depth\n\n-\nCamera calibration\, disparity\, and triangulation\n\n-\nAppli cations in robotics\, distance estimation\, and obstacle awareness\n\n6. M odern Computer Vision: YOLO and RF-DETR\n\n-\nIntroduction to deep-learnin g-based computer vision\n\n-\nUnderstanding object detection\, classificat ion\, confidence scores\, and bounding boxes\n\n-\nIntroduction to YOLO an d RF-DETR object detection models\n\n-\nComparing traditional computer vis ion techniques with deep-learning approaches\n\n-\nIntegrating object dete ction models into camera-based applications and robotic systems\n\n7. Comp uter Vision in Action: Lessons from the Vulcan Competition\n\n-\nOverview of our experience implementing autonomous behavior using a camera\n\n-\nTh e camera-to-action pipeline: image capture\, preprocessing\, perception\, decision-making\, and control\n\n-\nApplying object recognition and color detection to robotic autonomy\n\n-\nChallenges involving lighting\, perspe ctive\, processing latency\, and detection reliability\n\n-\nLessons learn ed from building a camera-based autonomous system\n\n8. Questions\, Discus sion\, and Closing\n\n-\nSummary of key concepts\n\n-\nAudience questions and discussion\n\n-\nRecommended tools\, resources\, and next steps for le arning computer vision\n\nVirtual: https://events.vtools.ieee.org/m/584643 LOCATION:Virtual: https://events.vtools.ieee.org/m/584643 ORGANIZER:allenkizitowachio@gmail.com SEQUENCE:38 SUMMARY:Introduction to Computer Vision URL;VALUE=URI:https://events.vtools.ieee.org/m/584643 X-ALT-DESC:Description: <br /><p>Computer vision is one of the most excitin g fields in artificial intelligence\, enabling computers and robots to int erpret and understand visual information from the world around them. From detecting objects and recognizing faces to estimating depth and enabling a utonomous navigation\, computer vision bridges the gap between raw camera data and intelligent decision-making.</p>\n<p>This webinar introduces part icipants to the fundamentals of computer vision through a practical\, hand s-on-oriented exploration of how vision systems work. We will begin by exa mining how cameras capture light and convert it into digital image data\, including the role of image sensors\, pixels\, color channels\, and image frames. We will then explore how software tools such as OpenCV can be used to process\, manipulate\, and extract useful information from these image s.</p>\n<p>Participants will be introduced to key computer vision techniqu es\, including color-based detection\, image features\, Histograms of Orie nted Gradients (HOG)\, and basic facial feature analysis. We will also exp lore the differences between monocular and stereo vision\, discussing how a single camera can be used to identify visual features and how two camera s can help estimate depth and understand spatial relationships.</p>\n<p>Th e session will then progress from traditional computer vision techniques t o modern deep-learning-based approaches\, introducing object detection mod els such as YOLO and RF-DETR and explaining how these models identify\, cl assify\, and locate objects within images.</p>\n<p>Finally\, we will conne ct these concepts to real-world robotics through our experience with the V ulcan competition\, demonstrating how camera-based perception can support autonomous robotic systems. We will explore how a robot can use visual inf ormation\, including object recognition and color detection\, to interpret its environment and make navigation decisions\, even when relying on a ca mera as its primary perception sensor.</p>\n<p>Whether you are interested in artificial intelligence\, robotics\, autonomous systems\, or software d evelopment\, this webinar will provide a foundation for understanding comp uter vision and the tools and techniques used to build practical vision-ba sed applications.</p>\n<p>No prior experience in computer vision is requir ed. A basic familiarity with programming is helpful but not essential.</p> \n<p><strong>Meeting Link:</strong> <a href="https://meet.google.com/dmg-f ypi-wqc">https://meet.google.com/dmg-fypi-wqc</a></p><br /><br />Agenda: < br /><p><strong>AGENDA: INTRODUCTION TO COMPUTER VISION</strong></p>\n<p>< strong>1. Welcome and Introduction to Computer Vision</strong></p>\n<ul>\n <li>\n<p>What is computer vision?</p>\n</li>\n<li>\n<p>Computer vision vs. image processing vs. artificial intelligence</p>\n</li>\n<li>\n<p>Real-wo rld applications of computer vision in robotics\, healthcare\, security\, and autonomous systems</p>\n</li>\n</ul>\n<p><strong>2. How Cameras See th e World</strong></p>\n<ul>\n<li>\n<p>How light is captured by image sensor s and converted into digital images</p>\n</li>\n<li>\n<p>Understanding pix els\, resolution\, RGB/BGR color channels\, and image frames</p>\n</li>\n< li>\n<p>Camera data pipelines and frame rates</p>\n</li>\n<li>\n<p>How raw camera data becomes useful information</p>\n</li>\n</ul>\n<p><strong>3. O penCV and Fundamentals of Image Processing</strong></p>\n<ul>\n<li>\n<p>In troduction to OpenCV</p>\n</li>\n<li>\n<p>Reading\, displaying\, and proce ssing images and video frames</p>\n</li>\n<li>\n<p>Image resizing\, croppi ng\, filtering\, thresholding\, and drawing</p>\n</li>\n<li>\n<p>Understan ding color spaces</p>\n</li>\n<li>\n<p>Practical demonstration: detecting colored objects using OpenCV</p>\n</li>\n</ul>\n<p><strong>4. Traditional Computer Vision Techniques</strong></p>\n<ul>\n<li>\n<p>Image features\, e dges\, shapes\, and contours</p>\n</li>\n<li>\n<p>Color detection and imag e segmentation</p>\n</li>\n<li>\n<p>Introduction to Histograms of Oriented Gradients (HOG)</p>\n</li>\n<li>\n<p>Basic facial feature analysis and fa ce detection</p>\n</li>\n<li>\n<p>Traditional computer vision vs. machine- learning-based approaches</p>\n</li>\n</ul>\n<p><strong>5. Monocular vs. S tereo Vision</strong></p>\n<ul>\n<li>\n<p>Understanding single-camera (mon ocular) vision</p>\n</li>\n<li>\n<p>How stereo cameras estimate depth</p>\ n</li>\n<li>\n<p>Camera calibration\, disparity\, and triangulation</p>\n< /li>\n<li>\n<p>Applications in robotics\, distance estimation\, and obstac le awareness</p>\n</li>\n</ul>\n<p><strong>6. Modern Computer Vision: YOLO and RF-DETR</strong></p>\n<ul>\n<li>\n<p>Introduction to deep-learning-ba sed computer vision</p>\n</li>\n<li>\n<p>Understanding object detection\, classification\, confidence scores\, and bounding boxes</p>\n</li>\n<li>\n <p>Introduction to YOLO and RF-DETR object detection models</p>\n</li>\n<l i>\n<p>Comparing traditional computer vision techniques with deep-learning approaches</p>\n</li>\n<li>\n<p>Integrating object detection models into camera-based applications and robotic systems</p>\n</li>\n</ul>\n<p><stron g>7. Computer Vision in Action: Lessons from the Vulcan Competition&nbsp\; </strong></p>\n<ul>\n<li>\n<p>Overview of our experience implementing auto nomous behavior using a camera</p>\n</li>\n<li>\n<p>The camera-to-action p ipeline: image capture\, preprocessing\, perception\, decision-making\, an d control</p>\n</li>\n<li>\n<p>Applying object recognition and color detec tion to robotic autonomy</p>\n</li>\n<li>\n<p>Challenges involving lightin g\, perspective\, processing latency\, and detection reliability</p>\n</li >\n<li>\n<p>Lessons learned from building a camera-based autonomous system </p>\n</li>\n</ul>\n<p><strong>8. Questions\, Discussion\, and Closing&nbs p\;</strong></p>\n<ul>\n<li>\n<p>Summary of key concepts</p>\n</li>\n<li>\ n<p>Audience questions and discussion</p>\n</li>\n<li>\n<p>Recommended too ls\, resources\, and next steps for learning computer vision</p>\n</li>\n< /ul> END:VEVENT END:VCALENDAR