# Category: Image Processing

## Summary of Projective Geometry

In this article will emphasize on some important characteristics and invariants with respect to transformations in projective geometry. Some properties in projective geometry The following properties are important to know if you want to work on stereo image processing, photogrammetry computer vision which contains 3D reconstruction, depth estimation and so on. For different dimensions in

## Projective Geometry

As described in last article, projective space can be considered as a tool to mathematically describe the perspective projection. So in this article, we want to go through some basics, but important elements of projective space. After reading this article you will get to understand the followings topics: Homogeneous coordinates and its properties Homography matrix

## Local Feature and Harris Corner Detection

In the last three posts, we have learned some important basics in computer vision and in this post, I want to talk about the concept “Feature” which plays an important role in different levels of image processing. Feature (local Feature) In image processing, feature (local feature) is basically a piece of image (patch) which can

## Considerations while working with (industrial) cameras

In this post, I’m going to describe basic and important parameters for camera and lenses. After reading this post you should have the following knowledge: Clearly know what is Focal Length Aperture Depth of View Clear understanding of different types of lenses Field of View Sensor size Shutter Speed ISO Maximum Transmission Unit (MTU) Ground

## Self-driving cars and Localization

In this article, I’m going to describe a possible scenario for the precise positioning of autonomous cars in the streets and intersections. Overview Before describing the localization of autonomous cars, I want to describe some important components which may be used in the next generation of autos. Basically, there are two vital elements for driving

## Edge Detections

So far we have discussed some basics and we know how convolution in image processing works. It is time to talk more about applications of convolution (kernel convolution). So in this post, we will go through Edge Detection algorithms and after reading this post you should be able to understand edge detection concept and its

## Convolution, Cross-Correlation and Gaussian

In this post, we will continue to learn more about low-level computer vision which we’ve started in last post. After reading this post you will learn Convolution as well as literally most of the computer vision because it appears somehow in all functions that we will use in the CV. Let’s get started. Convolution and

## Computer Vision Basics

As you may know, demand on Computer Vision Applications is increasing and so I decided to make post series about that and dive into my favorite topic “Computer Vision” and share my knowledge with you to help you to get deep understanding of different topics in this field. These series divided into three parts each