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Run Hundreds of Experiments with OpenCV and Hydra

Feature Matching Problem

Image matching is an important task in computer vision. Real-world objects may be captured on different photos from any angle with any lightning conditions and may be occluded. But while images contain the same objects they must be categorized accordingly. For this purpose, computer vision gives us invariant feature extractors that help to match objects on different images

Detectors, Descriptors, and Matchers

Image matching is a three-step algorithm. Fortunately, they are all covered in the OpenCV library

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Yolo-FastestV2: Faster and Lighter

Yolo-Fastest focuses on single-core, real-time inference performance and low CPU usage under real-time conditions. Not only can it realize real-time performance on mobile phones, but also on RK3399, Raspberry Pi 4, and various Cortex-A53 while remaining low-cost. The power consumption device meets certain real-time performance standards. After all, these embedded devices are much weaker than mobile phones, but they are more widely used and cheaper.

Let me talk about the original intention of Yolo-Fastest. Most people used Mobilenet-SSD in early lightweight object detection. In actual tests, it is difficult to achieve real-time on commonly used ARM devices; it can only be done on some high-end mobile phones. All big cores can barely reach real-time when booting, not to mention the "powerful" RK3399 and other commonly used ARM CPUs in the industry. Real-time is basically impossible.

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How AI-Powered Computer Vision Is Transforming Healthcare

AI-Powered Computer Vision

The impact of AI on human lives can be felt the most in the healthcare industry. AI-powered computer vision technology can help bring affordable healthcare to millions of people. Computer vision practices are already in place for sorting and finding images in blogs and retail websites. It also has applications in medicine.

You may be interested in:  Computer Vision: Overview of a Cutting Edge AI Technology

Medical diagnosis depends on medical images such as CAT scans, MRI images, X-rays, sonograms, and other images.

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Object Detection and Augmentation in Modern Web Development

I’ve been playing around a lot with the Shape Detection API in Chrome and I really like the potential it has. For example, a very simple QRCode detector I wrote a long time ago has a JS polyfill but uses new BarcodeDetector() API if it is available.

You can see some of the other demo’s I’ve built here: https://paul.kinlan.me/face-detection/, https://paul.kinlan.me/barcode-detection/ and https://paul.kinlan.me/detecting-text-in-an-image/

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Object Detection Tutorial in TensorFlow: Real-Time Object Detection

Creating accurate machine learning models that are capable of identifying and localizing multiple objects in a single image has remained a core challenge in computer vision. But with recent advancements in deep learning, object detection applications are easier to develop than ever before. TensorFlow’s object detection API is an open-source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. 

You can go through this real-time object detection video lecture where our deep learning training expert discusses how to detect an object in real time using TensorFlow.