MULTI-CAMERA APPLICATION

SUCH AS MULTI-FOCUS OR MULTI-CUE FOR CAPTURE IMAGE

WEEKLY REPORT: NOV 8 2016

Professor: Pei-Jun LeeResearcher: Bui Trong An

OUTLINE

• Introduce topic• Issue• Progressing• In Progressing• To do

INTRODUCE RESEARCH TOPIC

Multi-cameras application, such as multi-focus or multi-cue for capture image.

ISSUE

• The limit of depth-of-focus if optical lenses in Charged Coupled Device (CCD) devices is possible to obtain an image that contains all of the relevant object in focus.

CCDs are sensors used in digital cameras and video cameras to record still and moving images. The CCD captures light and converts it to digital data that is recorded by the camera.

PROGRESSING Published: Information Fusion · May 2015

IN PROGRESS

• SIFT algorithm

• OpenCV for Mobile Environment

• Convert Original image to SIFT Image ( Step 1 in Progressing)

• Result ( SIFT Demo)

SIFT ALGORITHMThere are mainly four steps involved in SIFT algorithm:

* IDEA OF ALGORITHM

1. Scale-space Extrema Detection

2. Keypoint Localization

3. Orientation Assignment

4. Keypoint Descriptor

http://docs.opencv.org/3.1.0/da/df5/tutorial_py_sift_intro.html

IDEA

SCALE-SPACE EXTREMA DETECTION

Search over multiple scales and image locations.

Detail: SIFT algorithm uses Difference of Gaussians

KEYPOINT LOCALIZATION• Fit a model to detrmine location and scale.

• Select keypoints based on a measure of stability.

ORIENTATION ASSIGNMENT• Compute best orientation(s) for each key point region.

KEY POINT DESCRIPTION• Use local image gradients at selected scale and rotation to describe each keypoint region.

OPENCV FOR MOBILE APP (ANDROID)

FUNCTION CONVERT

SIFT DEMO

SIFT DEMO

TO DO

• RESEARCH P1 AND P2 IN A PROGRESSING

PROBLEM

• SIFT PUBLISHED MAY 2015

• Robust Multi-Focus Image Fusion Using Multi-Task Sparse Representation and Spatial Context (MAY 2016)

8.0 MP -> 3~5s

20.7 MP -> ~20s