VLFeat – cross-platform open source collection of vision algorithms for GNU Octave – Yup bagi kalian yang suka berbau-bau opensource daripada menggunakan Matlab yang harganya cukup lumayan. Kalian bisa menggunakan GNU Octave. Penulis banyak koq menggunakan GNU Octave untuk melakukan beragam project. Tidak kalau jauh koq dari Matlab karena banyak tersedianya package.
Nah salah satu package yang kalian butuhkan ketika bekerja dengan computer vision, maka penulis rekomendasikan VLFeat
VLFeat is a cross-platform open source collection of vision algorithms with a special focus on visual features (for instance SIFT and MSER) and clustering (k-means, hierarchical k-means, agglomerative information bottleneck)
VLFeat yang bersifat portable bisa kalian gunakan di GNU Octave https://www.vlfeat.org/install-octave.html Mengenai lisensi http://VLFeat is distributed under the BSD license:
Beberapa algoritma yang didukung yaitu
Visual features
- Local features: the concept of frames (keypoints). An overview of the concept of feature frame used as geometric reference in feature detection.
- Covariant detectors. An introduction to computing co-variant features like Harris-Affine.
- Histogram of Oriented Gradients (HOG). Getting started with this ubiquitous representation for object recognition and detection.
- Scale Invariant Feature Transform (SIFT). An introduction to SIFT keypoint and descriptor extraction and matching.
- Dense SIFT (DSIFT) and PHOW. Extracting dense SIFT features for image classification.
- Local Intensity Order Pattern (LIOP). Getting started with the LIOP descriptor as an alternative to SIFT in keypoint matching.
- Maximally Stable Extremal Regions (MSER). Extracting MSERs from an image as an alternative covariant feature detector.
- Image distance transform. Compute the image distance transform for fast part models and edge matching.
- Fisher vector and VLAD encodings. Compute global image encodings by pooling local image features with Fisher vectors and VLAD.
Statistical methods
- GMM. Learn Gaussian Mixture Models using the Expectation Maximization algorithm.
- k-means. Cluster features with k-means.
- Agglomerative Information Bottleneck (AIB). Cluster discrete data based on the mutual information between the data and class labels.
- Quick shift. An introduction which shows how to create superpixels using this quick mode seeking method.
- SLIC. An introduction to SLIC superpixels.
- Support Vector Machine (SVM). Learn a binary classifier and check its convergence by plotting various statistical information.
- Forests of kd-trees. Approximate nearest neighbour queries in high dimensions using an optimized forest of kd-trees.
- Plotting functions for rank evaluation. Learn how to plot ROC, DET, and precision-recall curves.
- MATLAB Utilities. A list of useful MATLAB functions bundled with VLFeat.
Obsolete tutorials
- Integer optimized k-means (IKM). VLFeat integeger-otpimized k-means implementation (obsolete).
- Hierarchical k-means (HIKM). Create a fast k-means tree for integer data (obsolete).