Build Image Recognition App

Explore Scratch programming, a visual, block-based language that lets you drag and drop blocks to create interactive games without writing code, online via version 3.0.

Explore the Scratch programming environment, navigate the home page and projects, and learn to drag-and-drop blocks—motion, looks, and sound—to control a sprite on the stage and customize costumes and backdrops.

Learn to program basic movements in scratch by using motion blocks to move sprites 50 steps forward and backward, rotate 15–30 degrees, and insert wait times to reveal intermediate positions.

Learn how loops work in Scratch, using repeat, until, and forever to repeat actions, such as drawing a square and creating a rectangle with move and wait blocks.

Program a sprite to follow a fixed path by setting a starting point, uploading a backdrop, and using move, turn, and go forward blocks with timing.

Explore conditional statements and if-then logic in a Scratch-like program by asking for input, testing numbers, and moving a sprite when conditions are met.

MIT App Inventor is a web-based, open-source integrated development environment for building Android apps using a graphical, block-based scratch-like interface with no code typing.

Discover registering and using MIT App Inventor, a web-based tool for building apps. Learn to create a Google account, log in, and access tutorials and forums.

Explore the MIT App Inventor interface to build apps, switch between design and blocks views, manage projects and screens, drag-and-drop UI components, and customize properties like fonts, images, and text.

Explore the software development lifecycle (sdlc) and the building blocks of app structure, from planning and requirement analysis to design, development, testing, deployment, and maintenance.

Design a MIT App Inventor counter app with a centered UI, background image, and two buttons; increment the counter on press and reset to zero using labeled heading and display.

Explore block programming with a scratch-like blocks palette to build app logic. Attach blocks to a counter to increment on button press and reset on launch.

Learn to connect the phone to the MIT App Inventor companion, install and run the app, scan a QR code, and test output view with a counter and reset functionality.

Set up and run an AI emulator on Windows, Mac, or Linux, install the emulator, connect the companion app, and test the app output with live taps and resets.

Learn an alternate method to view output by downloading the APK to your computer, transferring it to your phone via USB or email, and installing to test the app.

Explore the software development lifecycle for building a calculator app with MIT App Inventor, covering requirements for basic operations, UI design, coding, testing, deployment, and iterative enhancements.

Design a calculator user interface by creating a new project, placing a display and 16 buttons, and arranging labels, images, and background via a table layout for a clean interface.

Learn to program a calculator interface using blocks, with global current, previous, and operator variables, and a reusable number-button procedure for entering digits.

Build a calculator workflow by creating an operator button click procedure, managing global previous and current, and performing add, subtract, multiply, and divide with AC and display updates.

Test the app output on mobile by scanning a QR code to connect the companion, then review the arithmetic results as the simulated output loads and displays combined calculations.

Define requirements and design a 3x3 tic tac toe UI with Xs, Os, and a reset button; build, test, deploy, and monitor using MIT App Inventor blocks.

Design a tic tac toe UI by centering the layout, uploading a background, and creating a 3x3 grid of invisible buttons with X/O icons and a reset notifier.

Program the tic tac toe app initialization by creating global variables, a new game procedure, and wiring nine buttons to reset and start states, updating the display text.

Program the tic tac toe game logic by implementing win conditions and a has won procedure, handling button clicks to update global variables and display X and O images.

Test the app output by connecting a companion device, scanning the QR code, and loading the game to verify play flow, reset functionality, win conditions, and celebratory fireworks.

Explore how vision systems identify objects, places, and faces by tracing the image formation process, light exposure, rods and cones, and brain processing.

This lecture explains steps in machine image recognition: capture images with sensing device, process them to extract features, and compare with a database to recognize oranges, apples, bananas, and grapes.

Define artificial intelligence as machines that replicate human or rational thinking, with machine learning and deep learning as its branches, plus natural language processing, robotics, planning.

Train an artificial vision system by gathering and labeling a training dataset, preprocessing data, training the model, and predicting new images on a client device.

Train the model with three labels: disk, box, and green slime, by collecting and uploading 60 labeled images per label, using webcam or uploads, and avoiding mix-ups.

Upload labeled images, leave default model settings, and click train model to train the image recognition system; training time varies with image count and quality.

Learn how to collect and upload testing data for an image recognition app, labeling and organizing 60 test images across three categories, and review how the model predicts these images.

Validate the image recognition model by running predictions to see how well it classifies images, noting misclassifications from grayscale or unclear images.

Design the user interface for an image recognition mobile app by uploading background images, aligning elements, and adding a scan button with navigation to the recognition screen.

Develop and wire the image recognition app by programming button-driven navigation between screens, capturing images with the camera, and using a personal image classifier to classify images and display results.

Demonstrates output view simulation and testing for image recognition, including screen transitions, companion-based setup, QR code scanning, image capture, and interpreting object-detection percentages.

Review mobile platforms and tools, learn Scratch basics with loops and conditional statements, and build a counter, Tic Tac Toe, and an image recognition app with MIT App Inventor.