Movie Maker Tool For High Resoultion Stills Computer Science Essay

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Current techniques for generating animated scenes involve either videos (whose resolution is limited) or a single image (which requires a significant amount of user interaction). In this project, we describe a system that allows the user to quickly and easily produce a compelling-looking animation from a small collection of high resolution stills. Our system has two unique features. First, it applies an automatic partial temporal order recovery algorithm to the stills in order to approximate the original scene dynamics. The output sequence is subsequently extracted using a second-order Markov Chain model. Second, a region with large motion variation can be automatically decomposed into semiautonomous regions such that their temporal orderings are softly constrained. This is to ensure motion smoothness throughout the original region. The final animation is obtained by frame interpolation and feathering. Our system also provides a simple-to-use interface to help the user to fine-tune the motion of the animated scene. Using our system, an animated scene can be generated in minutes. We show results for a variety of scenes.

1.0 Introduction

A single picture conveys a lot of information about the scene, but it rarely conveys the scene's true dynamic nature. A video effectively does both but is limited in resolution. Off-the-shelf camcorders can capture videos with a resolution of 720 _ 480 at 30 fps, but this resolution pales in comparison to those for consumer digital cameras, whose resolution can be as high as 16 MPixels. What if we wish to produce a high resolution animated scene that reasonably reflects the true dynamic nature of the scene? Video textures are the perfect solution for producing arbitrarily long video sequences. Our system is capable of generating compelling-looking animated scenes, but there is a major drawback: Their system requires a considerable amount of manual input. Furthermore, since the animation is specified completely manually, it might not reflect the true scene dynamics. We use a different tack that bridges video textures and system: We use as input a small collection of high resolution stills that (under-)samples the dynamic scene. This collection has both the benefit of the high resolution and some indication of the dynamic nature of the scene (assuming that the scene has some degree of regularity in motion). We are also motivated by a need for a more practical solution that allows the user to easily generate the animated scene. In this paper, we describe a scene animation system that can easily generate a video or video texture from a small collection of stills (typically, 10 to 20 stills are captured within 1 to 2 minutes, depending on the complexity of the scene motion). Our system first builds a graph that links similar images. It then recovers partial temporal orders among the input images and uses a second-order Markov Chain model to generate an image sequence of the video or video texture.

REQUIREMENT SPECIFICATION

Our system is designed to allow the user to easily fine-tune the animation. For example, the user has the option to manually specify regions where animation occurs independently (which we term independent animated regions (IAR)) so that different time instances of each IAR can be used independently. An IAR with large motion variation can further be automatically decomposed into semi-independent animated regions (SIARs) in order to make the motion appear more natural. The user also has the option to modify the dynamics (e.g., speed up or slow down the motion, or choose different motion parameters) through a simple interface.

Finally, all regions are frame interpolated and feathered at their boundaries to produce the final animation. The user needs only a few minutes of interaction to finish the whole process. In our work, we limit our scope to quasi-periodic motion, i.e., dynamic textures. There are two key features of our system. One is the automatic partial temporal order recovery. This recovery algorithm is critical because the original capture order typically does not reflect the true dynamics due to temporal under sampling.

As a result, the input images would typically have to be sorted. The recovery algorithm automatically suggests orders for subsets of stills. These recovered partial orders provide reference dynamics to the animation. The other feature is its ability to automatically decompose an IAR into SIARs when the user requests and treat the interdependence among the SIARs. IAR decomposition can greatly reduce the dependence among the temporal orderings of local samples if the IAR has significant motion variation that results in unsatisfactory animation. Our system then finds the optimal processing order among the SIARs and imposes soft constraints to maintain motion smoothness among the SIARs.

HARDWARE AND SOFTWARE REQUIREMENT

OPERATING SYSTEM

Windows XP Professional

FRONT END

Microsoft Visual Studio .NET 2003

CODING LANGUAGE

Visual C#.NET

SYSTEM

Pentium III 700 MHz

HARD DISK

320 GB

RAM

1 GB

Existing System:

The existing system has garnered a lot of attention is video texture, which reuses frames to generate a seamless video of arbitrary length.

Video textures work by figuring out frames in the original video that are temporally apart but visually close enough, so that jumping between such frames appears seamless.

This work was extended to produce video sprites, which permit high-level control of moving objects in the synthesized video. Unlike videos, the ordering of our input stills may not be 1D. Thus, we can only use partial orders as reference dynamics.

Drawbacks:

Fully manual in traction.

Each and every process should be done manually.

Ex. Flash player.

Proposed System:

The proposed system is a scene animation system that can easily generate a video or video texture from a small collection of stills.

Our system first builds a graph that links similar images. It then recovers partial temporal orders among the input images and uses a second-order Markov Chain model to generate an image sequence of the video or video texture. Our system is designed to allow the user to easily fine-tune the animation.

Merits:

Reduce the user interaction

Time complexity

User can give only collections of jpeg images. Based on the images we will get movie file for 1 or more than 2 minutes.

Simply conversion of Jpeg to Mpeg/AVI

System Analysis

Produce a compelling-looking animation scene from a small collection of high-resolution stills. This system has two unique features.

First, it applies an automatic partial temporal order recovery algorithm to the stills in order to approximate the original scene dynamics.

The output sequence is subsequently extracted using a second-order Markov Chain model.

A region with large motion variation can be automatically decomposed into semiautonomous regions such that their temporal orderings are softly constrained.

This is to ensure motion smoothness throughout the original region. Our system also provides a simple-to-use interface to help the user to fine-tune the motion of the animated scene.

SYSTEM ARCHITECTURE

Our system first builds a graph that links similar images. It then recovers partial temporal orders among the input images and uses a second-order Markov Chain model to generate an image sequence of the video or video texture. Our system is designed to allow the user to easily fine-tune the animation.

class DIAGRAM

USE CASE DIAGRAM

SEQUENCE DIAGRAM

DATA FLOW DIAGRAM

System DesIGN:

Modules

Preprocessing

Building a Graph

Motion creation

Layer based approach

Scene making

Manual editing

DESIGN FORM - Insert Image

Preprocessing

Color Enhancement, Improve the image quality, Size corrections, and noise removal. Algorithm: Morphological Filters, Automatic Color Enhancement technique [ACE]

Input : Stills with low quality

Output : Quality stills.

1. Color Enhancement - improve the Contrast & Brightness, Improve the quality based on the saturation Adjustment In first module we first set same pixels for all images. Because it will compare the images for find the difference b/w the same objects. After that we will set the same brightness and contrast for all images. Because based on the brightness and contrast the output will be show with same bright n contrast.

Morphology - Removing Noise

If we improve the quality of an image it will affect the original pixels. Some pixels may be affect that time noise will occur. Her we are using noise removal process.

2. Convolution and correlation - Smoothing, Sharpness

The Pre processing option has the HSL color space option for zoom, flip etc. Levels, median and gamma correction options are available under the pre processing tab.

The median option shows the HSL ratio, RGB value, zoom percentage etc. The image below shows the respective values

The flip and mirror options are also available. Represented below are the mirror option and its corresponding flipped image.

Building a Graph

Image comparison.

Algorithm: Floyd's algorithm, Partial Temporal order recovery algorithm.

Input: Unordered stills Output: Ordered, distance computed.

1. Edge Detection - Detect the object Edges Using canny edge detection process we will detect the edges of the images. Here we will show layout of the objects. 2. Difference calculation - Calculate the Difference b/w the two images based on the pixel difference. 3. Make a Graph - Make a Graph based on difference

Motion Creation

Based on distance low resolution optical flow is created between two adjacent images. Sampling the graph

Algorithm: Statistical approach, Markov Chain Model.

Fine tuning

Input: Stills with no tuning

Output: Fine tuned video texture.

PROCESS

To obtain the output, the Process option is made use of. Under the option, four such sub options are available. Re-size, Ordering, Motion and Convert AVI. Once the convert AVI option is done then the output is obtained with the images converted into video.

Once the motion is created, the Convert AVI process is done. Once completed successfully, the output file can be saved to view the video.

Layer Based Approach

Feathering Techniques

Motion creation

Algorithm: Baysian Matting, Canny edge detection.

Input :Stills with no layer separation

Output: Animated Scenes after separation.

Scene Making

Video texture creations

AVI File conversion based on time sequence

Frame interpolation

Algorithm :AVI format

Input: Stills

Output: Video or video texture.

Manual Editing

Manual reorders

Editing the image

Motion smoothness

Measuring motion irregularity.

Techniques Used

Morphological filters,

Floyd's algorithm

Automatic color enhancement,

Statistical approach

Markov Chain model,

Baysian Matting.

Partial temporal order recovering algorithm.

Markov Chain Model

Ws,k = Dist(Img(s, k-1),fn-1)+Dist(Img(s,k),fn)

where Img(s,k) is the kth node (image) on the sth path. The value of Dist() can be directly taken from the precomputed distance lookup table.

The distribution of ws,k determines the probability of choosing Img(s, k + 1) as the next frame in the following way:

P(Img(s,k+1)|fn-1,fn)~exp(-ws,k/( ))

Where is a user controllable parameter and its default value is 0.1. This is a sampling scheme of a second-order Markov Chain. Intuitively, (1) and (2) jointly imply that if the (k- 1)th and the kth images of the sth path are close to fn-1 and fn, respectively, then it is likely to choose the(k +1)th images of the sth path as the next frame fn+1.

SYSTEM TESTING

Testing is done for each module. After testing all the modules, the modules are integrated and testing of the final system is done with the test data, specially designed to show that the system will operate successfully in all its aspects conditions. . The procedure level testing is made first.

UNIT TESTING: Unit testing verification efforts on the smallest unit of software design, module. This is known as "Module Testing". The modules are tested separately. This testing is carried out during programming stage itself.

INTEGRATION TESTING: Integration testing is a systematic technique for constructing tests to uncover error associated within the interface. In the project, all the modules are combined and then the entire programmer is tested as a whole. In the integration-testing step, all the error uncovered is corrected for the next testing steps.

Thus the system testing is a confirmation that all is correct and an opportunity to show the user that the system works. The final step involves Validation testing, which determines whether the software function as the user expected.

VALIDATION TESTING: To uncover functional errors, that is, to check whether functional characteristics confirm to specification or not specified.

SL NO

COMPONENTS

DESCRIPTION

COST IN SGD

1

Effort and Salary for staff

Prototype developers - 5, 2 HR/Admin, 1 Marketing Manager, VB.NET Programmer - 3

30,000

2

Infrastructure

PC, Servers, Networking equipments, security cameras …

200,000

3

Staff welfare

Transportation, refreshments, entertainment …

20,000

4

Training

Staff and client training

8,000

5

Support

Client support and version management

90,000

 

TOTAL

 

348,000

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