Time is a limited resource

Some of my recent clients are interested in image classification using limited learning data. A major use case is in detecting defective products on the manufacturing line. Defective products are normally caught by human operators watching the production line. It requires constant concentration and effort so if this can be automated it will bring a lot of productivity boost to manufacturers. One method to overcome this issue is by creating a network to compare input images with the training dataset. For defective products, the images will look different from the passing products. Experiments For this experiment, I used a Siamese Network to generate embeddings for images of Pokemon. The Pokemon has 4 classes, Pikachu, Squirtle, Bulbasaur and Charmander. The images are scraped from the internet using the Bing API. The images in the training dataset is trained to find the set of weights that will clearly separate the 4 classes. If a new image which is not included in the 4 classes is used...

In this post I'd like to share my experiments in calibrating a projector using a camera with known intrinsic and extrinsic parameters. No math, just concepts. The final result is a projected image that follows a marker's position and orientation, and adjusts the image. The calibration process enables us to automatically setup the relationship between the projector and the camera that detects the marker. In short, these are the steps performed Calibrate camera and projector relative position and orientation Detect the marker using the camera Adjust the image position and orientation based on the marker position and orientation Experiment setup The following videos show the results of the experiments.   As you can see, the projected dots follow the position and orientation of the chessboard marker. This setup will be very useful for projection mapping installations. Of course, at the current setup it will be useful only on small sizes but by using a larger marker we can theoretic...