Underfitting — Overfitting, Vanishing — Exploding Gradient Problems and their possible solutions with experiment results and scratch codes — After learning the basics of CNN and its usage for Image Classification, you may discover that the topic is not over and we should know more advanced topics to improve a convolutional neural network model. During my research, I came across some advice mostly to overcome overfitting problem, but when…

A comprehensive guide to convolution and convolutional neural networks for image classification, from implementation with Python and TensorFlow to optimization and transfer learning techniques — We learned Neural Network Fundamentals in my previous post Neural Networks Fundamentals A quick intro for Neural Networks, Unsupervised and Supervised Learning, the difference between Regression and…towardsdatascience.com

A quick intro for Neural Networks, Unsupervised and Supervised Learning, the difference between Regression and Classification and their simple modeling with neural networks, example implementation from scratch with Python — This is the first part of an end-to-end flow about Neural Networks and their usage for different tasks. The contents of the whole flow are as below: Neural Network Fundamentals

Epipolar Geometry basics like Essential Matrix, Fundamental Matrix, Triangulation, Feature Matching are explained with their MATLAB code implementations — We learned how to use a projective camera and how to calibrate it. In this post, we will learn Epipolar Geometry, which is the intrinsic projective geometry between two views. Two views mean now we will learn how to work with more than 1 camera to perform different tasks!

Camera Calibration using DLT and Zhang’s Method from theoretical math explanation to MATLAB code application — We learned what Camera Calibration Matrix is in the previous post of this series. A general issue in Visual Perception is that usually, we need to calibrate our cameras before using them. …

A user interface implemented with QT Creator, Opencv, and C++ to make the user able for realizing what they learned during this tutorial like Camera Calibration, Fundamental-Essential Matrix Estimation, Triangulation — The GUI consists of 3 parts as Calibration, Feature Matching, and Epipolar Geometry. Let’s examine it part by part Calibration In this part, you can calibrate your camera either using your already taken images or using your camera connected to your PC. …

A series about Visual Perception Fundamentals like Camera Calibration, Epipolar Geometry, and their mathematical implementations from scratch — Visual perception is the human ability to interpret the surrounding environment. [1] In this series, we will learn the Visual Perception fundamentals for robotics without using any Artificial Visual Perception method but using classical approaches step by step.

A brief explanation of these 3 terms that form the basis of relation— similarity analysis for different subjects like Region Growing, an algorithm used in Image Segmentation — Neighborhood The neighborhood of a pixel is simply the pixels that touch it. We examine neighborhood in 3 categories: 4- Neighborhood Here, we talk about the pixels touching in 4 directions top, right, bottom, and left. d- Neighborhood

Classical Computer Vision-Based Image Segmentation methods like Thresholding, Region-Based, Edge Detection Based and Morphological Segmentation are explained in 1 post to make a quick start with this domain — Image segmentation is the process of subdivision a digital image into multiple segments (objects). The goal of segmentation is to change the representation of an image into something more meaningful and easier to analyze. We can assemble different segmentation methods as shown below.