Summarizes nine key deep learning papers that advanced convolutional neural networks (CNNs) and computer vision over five years.
Explains stride and padding parameters in Convolutional Neural Networks (CNNs), building on Part 1 of the beginner's guide.
A developer shares their experience using AForge.NET to implement a 'magic color' image processing feature, detailing the filter steps and results.
Explores Visual Question Answering (VQA) as an alternative Turing Test, detailing neural network approaches using Python and Keras.
A technical guide to implementing Monocular Visual Odometry using OpenCV and C++, covering feature detection, motion estimation, and algorithm details.
A technical walkthrough of implementing a human activity recognition system using Kinect's skeletal joint data and machine learning.
A beginner-friendly tutorial on implementing Visual Odometry for robotics, focusing on a stereo vision approach based on a classic research paper.
Explores using ASIFT algorithm to stitch close-range intra-oral dental images for a complete jaw view, overcoming perspective challenges.
Explores algorithms for segmenting individual teeth from dental images, comparing Active Contours and Watershed transforms.
A tutorial on using Python and OpenCV to detect and count books in an image, filtering out other objects.
A student's curated reading list for learning Visual Odometry, including tutorials and key papers on algorithms like the 5-point and 8-point methods.
An explanation of the RANSAC algorithm for outlier rejection, used in computer vision and data modeling.
