Segmentation and Detection

A comparison between edge-based segmentation, Felzenszwalb's method and morphological segmentation. – Jul 16, 2021

Lead image of the content 'Segmentation and Detection'

Following the pattern of my previous posts, this one is based on an assignment submitted to the class of 2021/1 of course Introduction to Image Processing (MO443) at Universidade Estadual de Campinas. Its goal is to apply segmentation algorithms over images and extract characteristics of the objects using Python programming language and assess its results. We compare strategies such as edge-based segmentation, Felzenszwalb's method and morphological segmentation.

Computer Vision Segmentation

The 2D Fourier Transform

Applying the Fourier Transform over images using TensorFlow. – Jun 9, 2021

Lead image of the content 'The 2D Fourier Transform'

Similarly to my previous post, I decided to publish here an assignment that was submitted to a Computer Vision class. Its goal is to apply the Discrete Fourier Transform over images using Python programming language and assess its results. Firstly, I implement low, high and band-pass circular filters in the frequency domain for 2D signals. Secondly, I present two examples: (a) a visualization of the frequency spectrum the when the original signal is rotated; and (b) an image compression strategy based on Fourier Transform.

Computer Vision Fourier TensorFlow

A Vectorized Implementation of The Convolution Operation

Implementing a few numeric operations over images using Python programming language. – Jun 8, 2021

Lead image of the content 'A Vectorized Implementation of The Convolution Operation'

In this post, I thought I shared an assignment I have recently done on a Computer Vision class. Although results in Computer Vision are easily represented and interpreted, the implementation of even the most basic operations can be quite challenging. Even when the idea behind some code is trivial, implementations on GitHub and other websites can be quite difficult to understand. A few reasons come to mind, but I believe one to be of paramount importance: vectorization.

Computer Vision TensorFlow

Introdução ao aprendizado de máquina, pt. 4

Convoluções, o início de deep-learning – Dec 24, 2017

Lead image of the content 'Introdução ao aprendizado de máquina, pt. 4'

Na parte 3, eu mostrei alguns modelos não lineares e como eles lidam com a tarefa de classificação. No geral, redes densas possuem duas ou três camadas. Isso acontece pois observa-se empiricamente que o ganho em validation loss não segue linearmente com a adição de mais camadas. Além disso, este pequeno ganho também pode ser alcançado ao simplesmente aumentar o número de unidades nas camadas já presentes na rede densa. Por quê precisamos da ideia de deep-learning e deep-models, então?

ML Computer Vision Portuguese