Phase 1: Problem assessment – Determine the problem’s characteristics.
What is an intelligent system?
The process of building Intelligent knowledge-based system has been called knowledge engineering since the 80s. It usually contains six phases: 1. Problem assessment; 2. Data and knowledge acquisition; 3. Development of a prototype system; 4. Development of a complete system; 5. Evaluation and revision of the system; 6. Integration and maintenance of the system .
Continue reading “How to Build an Artificial Intelligent System (I)”
Road from Above is partly based on my Australia Postgraduate Intern Projects (Computer Vision and Machine Learning for Feature Extraction) within Aureon Group in Melbourne.
Aurecon’s experts, across Cape Town, Melbourne and Auckland offices, have been teamed up to develop and test approaches that capture and validate new and existing measurements of the metropolitan road network. Due to the confidentiality, we reduced the resolutions of the aerial images and only opened limited results on the public domain at https://roadsfromabove.netlify.com/. Thanks to Greg More, the design of this website got the best feedback from the workshop (Visualization for AI Explainability) of the IEEE VIS 2018 conference in Berlin, Germany
Visualization for AI Explainability: Projections and Dimensionality Reduction. The goal of this workshop is to initiate a call for “explainable” that explain how AI techniques work using visualizations. We believe the VIS community can leverage their expertise in creating visual narratives to bring new insight into the often obfuscated complexity of AI systems.
Continue reading “Roads from Above: Augmenting Civil Engineering & Geospatial Workflows with Machine Learning”
This project is forked from zbar library, I added some modifications, so the webcam can be used as an image reader to detect QR and Barcodes.
Continue reading “QR Code Detector with Webcam (Python / OpenCV / Pyzbar)”
Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.
The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.
More details about the algorithm could be found in the following papers:
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution ;
- Instance Normalization: The Missing Ingredient for Fast Stylization.
If you could not download the papers, here are the Papers.
Continue reading “Fast Neural Style Transfer by PyTorch (Mac OS)”
This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.
The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].
Moreover, I strongly recommend the formal paper Image Style Transfer Using Convolutional Neural Networks by the same authors published on CVPR-2016. This paper gives more details about how this method works. In short, the following two figures cover the idea behind the magic.
Continue reading “Image Style Transfer Using ConvNets by TensorFlow (Windows)”