Changes between Version 10 and Version 11 of Other/Summer/2015/cMF3

Timestamp:

Jun 16, 2015, 2:06:55 PM (9 years ago)

Author:

mrhohn

Comment:

Added more sections derived from Wuyang and Karthic by Zihong

Other/Summer/2015/cMF3

@@ -35 +35 @@
    * Test on Orbit MF topology
    * Run on top of MobilityFirst VN with ASR
+=== Image Processing ===
+We implement SURF algorithm to process object identify and match.
+
+The core idea of SURF algorithm can be summarized to the followings:
+
+1.Use Hessian Matrix and Scale Space to calculate key points  of an image[[BR]]
+2.Use SURFDescriptorExtractor to find feature vectors and to complete related calculations.[[BR]]
+3.Use BruteForce to match feature vectors within two images.[[BR]]
+4.Use drawMatches to draw matched key points.
+
+From the perspective of image processing each picture is consisted of n × m pixels which can obtain its corresponding determinant value of Hessian Matrix. Given a point p = (x,y) within an image [1] the Hessian matrix H(p, σ) at point and scale σ, is defined as follows:
+[[Image(formula1.png)]][[BR]]
+where [[Image(formula2.png)]] etc. are the second-order derivatives of the grayscale image.
+
+After the calculations of determinant value of each pixels a scale-space that contains repeatedly smoothed images through a Gaussian filter will be constructed. A scale-space can be regarded as an image pyramid built with multiple layers of an image with distinct scales. Determinant value of each pixel will be compared with that of other surrounding 26 pixels consisting of 9 upward ones, 9 downward ones and 8 encompassing ones. The maximum one will become a key point. From a circular region around the key point, a square region will be built to find its orientation and extract the SURF descriptor in order to construct its feature vector. After the calculations of all feature vectors of two images by following the above method, BruteForce algorithm matches feature vectors by calculating the shortest Euler distance of each of two vectors in all vectors. Each two feature vectors with the shortest distance is a pair of matched vector. At last we use drawMatches to draw matched key points.
+
+=== Weekly Summary ===
+Week 1
+
+ Familiarizing with Mobility First.
+
+ Completing Orbit Tutorials.
+
+Week 2
+
+ Explore Object recognition algorithms
+
+ Development of Image processing and camera sampling apps with basic functionality
+
+Week 3*
+
+ Development of Image processing and camera sampling apps with basic functionality
+
+* To be updated
+
+Future Goals
+
+ Incorporation with Google glass for live object recognition and information display
+
+ Facial Recognition
+
+ Client side low resolution object verification for coherent information display
 === Team ===
 
@@ -49 +91 @@
 <i>Wuyang Zhang</i>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<i>Zihong Zheng</i>
 }}}
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