Tag Archives: OpenCV

Performance Evaluation of Binary Descriptors – Introducing the LATCH descriptor

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In the last few posts we introduced the concept of local image descriptors and specifically binary image descriptors. We surveyed notable example of binary descriptors, namely BRIEF[1], ORB[2], BRISK [3] and FREAK[4]. Here, we will both introduce a novel binary descriptor that we have developed and give a full evaluation of several binary and floating point descriptors. We will show that our proposed descriptor – the LATCH descriptor[5] – outperforms the alternatives with similar running times. We will also demonstrate its performance in the real world application of 3D reconstruction from multiple images.

Given an image patch centered around a keypoint, LATCH compares the intensity of three pixel patches in order to produce a single bit in the final binary string representing the patch. Example triplets are drawn over the patch in green and blue

Given an image patch centered around a keypoint, LATCH compares the intensity of three pixel patches in order to produce a single bit in the final binary string representing the patch. Example triplets are drawn over the patch in green and blue

Our proposed LATCH descriptor was presented in the following paper:

Gil Levi and Tal Hassner, LATCH: Learned Arrangements of Three Patch Codes, IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, March, 2016

Here is a short video of me presenting LATCH at the WACV 16 conference (I apologize for the technical problems in the video).

Our LATCH descriptor  has already been officially integrated into OpenCV3.0 and has even won the CVPR 2015, OpenCV State of the Art Vision Challenge, in the Image Registration category !

Also, see CUDA (GPU) implementation of the LATCH descriptor and a cool visual odometry demo, both by Christopher Parker.

 

For more information, please see LATCH project page.

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Adding rotation invariance to the BRIEF descriptor

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In this post I will explain how to add a simple rotation invariance mechanism to the BRIEF[1] descriptor, I will present evaluation results showing the rotation invariant BRIEF significantly outperforms regular BRIEF where visual geometric changes are present and finally I will post a C++ implementation integrated into OpenCV3.

Just as a reminder, we had a general post on local image descriptors, an introductory post to binary descriptors and a post presenting the BRIEF descriptor. We also had posts on other binary descriptors:  ORB[2], BRISK[3] and FREAK[4].

We’ll start by a visual example, displaying the correct matches between a pair of images of the same scene, taken from different angles – once with the original version of BRIEF (first image pair) and one with the proposed rotation invariant version of BRIEF (second image pair):

Correct matches when using the BRIEF descriptor

Correct matches when using the BRIEF descriptor

Correct matches when using the rotation invariant BRIEF descriptor

Correct matches when using the rotation invariant BRIEF descriptor

It can be seen that there are much more correct matches when using the proposed rotation invariant of the BRIEF descriptor.

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Installing OpenCV and building an example application

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Hi,

I’ve searched for tutorials explaining how to install and configure OpenCV 2.4.9 with Cmake, using Visual Studio 2013, but I haven’t found any good ones. As a result, I’ve decided to create my own tutorial, where I explain how to build the OpenCV solution using Cmake and how to create applications in Visual Studio 2013 that use OpenCV. Note that my laptop is running Windwos 8.1.

Here is the tutorial:

The tutorial summarizes the following steps:

  1. Downloading and extracting OpenCV.
  2. Downloading and installing Cmake.
  3. Building OpenCV using Cmake.
  4. Creating applications in Visual Studio 2013 that use OpenCV.

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