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The opencv_nonfree module provides very useful features such as two prominent image features SIFT and SURF (including the CUDA’s implementation). In this tutorial, I demonstrate how to install this module into OpenCV 3 on Ubuntu. Prerequisites Remove the old version which has been installed in the system. Do not install opencv via apt-get Install cmake : sudo apt-get install cmake Installation Download opencv git clone Do not download the source from homepage.


Transfer MAT objects from Android to NDK The main idea is to use the address of an MAT object in order to manipulate the data. Basically, we have a function playing as a bridge between Java APIs and NDK: public native void function_name(long matAddress); To call the function, we use Mat’s address by calling getNativeObjAddr(). All computations in NDK will affect the content of MAT in both Java and NDK layers.


In this tutorial, I will demonstrate how to configurate the renowned computer vision libary, OpenCV, with the current Android Studio version (3.0.1). Let get started. The compiled version of OpenCV which supports Android is available at OpenCV Homepage. Download and extract it. Please note that to test the application properly on the mobile devices, the OpenCV Manager has to be installed. In another tutorial, I will talk about how to compile our own OpenCV library and put it to Android Studio since the pre-compiled library misses some interesting and important components, e.


Programing Pearls - Jon Bentley Excellent. Until now, there is no such book which makes me shout out like this after receiving from Amazon. Let brieftly review some favoriate books: CLRS: theoretically, this really is an “introduction” textbook about algorithms. Nonetheless, it still is an extensive reference and full of details. However, I am not impressed by the writing style and the pseudo-code. Algorithms, Sedgewick: one of the best textbooks ever.


The content of this note mostly belongs to the first section of TAoCP, Vol4A. Definition Given a square matrix of size $M$ and each element is $\{0, 1, \cdots, M-1 \}$, we construct a matrix such that for the element $i$ in the set $\{0, 1, \cdots, M-1\}$ only appears exactly 1 time on every row and column. Examples We have 16 cards which comprises a combination of 4 ranks, i.



Hashing in content-based image retrieval

My main research at SUTD

Urban-Area Scene-Based Localization

The on-device large-scale image based localization integrating compact image retrieval and 2D-3D matching.

Selected Publications

Apply a discrete optimization method to a convolutional deep neural network in order to solve the MIP problem.

We propose a novel deep retrieval framework which includes several masking schemes and embedding and aggregating methods to achieve competitive retrieval performance
ACM Multimedia 2017

We propose a novel framework where feature aggregating and hashing are designed simultaneously and optimized jointly
CVPR 2017

We propose an embedding method, namely Gaussian Mixture Model embedding, to enhance the discriminative property of image features before passing them into hashing.

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