OCM-DRW

• Author: Hancheng Yu,Haibao Qin, January 2018 .

Table of Contents

1. Introduction

2. Requirements
3. Usage of MEX Wrappers and Description of Files

4. Implementation steps 
5. Other notes


1. Introduction

~~~~~~~~~~~~~~~
This code repository aims at real-time texture-less object instance detection. 
This is an implementation of the method described in the paper:
"A Fast Approach to Texture-less Object Detection Based on Orientation Compressed Map and Discriminative Region Weight".
Authors: Haibao Qin,Hancheng Yu and Maoting Peng


2. Requirements
~~~~~~~~~~~~~~~
This is a C++ implementation, with an optional Matlab MEX wrapper.
OS Platform: Windows,Matlab 2014.Both 32 bit and 64 bit versions are available.

Note : If you want to run this program,you can refer to the implementation steps in the fourth part.
 
3. Usage of MEX Wrapper and Description of Files

First the MEX files have been compiled at the Matlab.

(1) [CMI,CMI_POPCNT,Edge] = S1_CMI(Test_image,C_radius);

Inputs: Test_image Greyscale image of type uint8. C_radius Compression radius.

Outputs: CMI CMI (the binarized orientation compressed map of input image I). CMI_POPCNT The number of quantized gradient orientations in each location of the CMI. Edge The edge map of the input image.

(2) S1_Match(CMI,CMT_Temp,CMI_POPCNT,CMT_Temp_C,S1_Scale,S1_Rotation,S1_Similarity);

Inputs: CMI The CMI. CMT_Temp The CMT (the binarized orientation compressed map of template T). CMI_POPCNT The number of quantized gradient orientations in each location of the CMI. CMT_Temp_C The number of quantized gradient orientations in each location of the CMT. S1_Scale,S1_Rotation Scales and Rotations. S1_Similarity The similarity scores for each location of the CMI.

(3) Object_candidate_loction(Test_image,S1_Similarity,S1_Scale,S1_Rotation,S1_Para);

Inputs: Test_image Greyscale image of type uint8. S1_Similarity The similarity scores for each location of the CMI. S1_Scale,S1_Rotation Scales and Rotations. S1_Para Threshold and other parameters.

(4) DRW_Im = S2_DRW(Test_image);

Inputs: Test_image Greyscale image of type uint8.

Outputs: DRW_Im The quantized gradient orientations map of the input image.

(5) S2_Match(DRW_Im,DRW_Temp,Mask_Crop,DRW_Temp_W,S2_Scale,S2_Rotation,S2_Similarity,S2_Para);

Inputs: DRW_Im The quantized gradient orientations map of the input image. DRW_Temp The spreading orientation map of the template T. Mask_Crop A Mask. DRW_Temp_W A Discriminative Region Weight.
S2_Scale,S2_Rotation Scales and Rotations. S2_Similarity The similarity scores for each location of the input image. S2_Para Other parameters.

(6) Draw_object_location(S3,S2_Similarity,S2_Scale,S2_Rotation,S3_Para); Be similar to (3).

4. Implementation steps

If you execute the project for the first time.Please refer to the following steps.

Firstly, in order to speed up computation,you will run this file ( mat_convert.m ),
converting another form of .mat file and generating four .mat format files in the directory of convert_mat.
If successful, 4 files will be generated in the directory of convert_mat. You can skip this step later.
In the following, you can execute test.m.
Finally,the detection results are achieved.

5. Other notes
~~~~~~~~~~~~~~
In the directory of offline_mat.

A matrix in CMT.mat represents a CMT. 
A matrix in CMT_POPCNT.mat is the number of quantized gradient orientations in each location of the CMT.
A matrix in DRW_Template.mat represents the spreading orientation map of the template.
A matrix in DRW_Template_Weight.mat represents the discriminative region weight of the template.