2,381 research outputs found
Video Upright Adjustment and Stabilization
Upright adjustment, Video stabilization, Camera pathWe propose a novel video upright adjustment method that can reliably correct slanted video contents that are often found in casual videos. Our approach combines deep learning and Bayesian inference to estimate accurate rotation angles from video frames. We train a convolutional neural network to obtain initial estimates of the rotation angles of input video frames. The initial estimates from the network are temporally inconsistent and inaccurate. To resolve this, we use Bayesian inference. We analyze estimation errors of the network, and derive an error model. We then use the error model to formulate video upright adjustment as a maximum a posteriori problem where we estimate consistent rotation angles from the initial estimates, while respecting relative rotations between consecutive frames. Finally, we propose a joint approach to video stabilization and upright adjustment, which minimizes information loss caused by separately handling stabilization and upright adjustment. Experimental results show that our video upright adjustment method can effectively correct slanted video contents, and its combination with video stabilization can achieve visually pleasing results from shaky and slanted videos.openI. INTRODUCTION
1.1. Related work
II. ROTATION ESTIMATION NETWORK
III. ERROR ANALYSIS
IV. VIDEO UPRIGHT ADJUSTMENT
4.1. Initial angle estimation
4.2. Robust angle estimation
4.3. Optimization
4.4. Warping
V. JOINT UPRIGHT ADJUSTMENT AND STABILIZATION
5.1. Bundled camera paths for video stabilization
5.2. Joint approach
VI. EXPERIMENTS
VII. CONCLUSION
ReferencesCNN)์ ํ๋ จ์ํจ๋ค. ์ ๊ฒฝ๋ง์ ์ด๊ธฐ ์ถ์ ์น๋ ์์ ํ ์ ํํ์ง ์์ผ๋ฉฐ ์๊ฐ์ ์ผ๋ก๋ ์ผ๊ด๋์ง ์๋๋ค. ์ด๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ๋ฒ ์ด์ง์ ์ธํผ๋ฐ์ค๋ฅผ ์ฌ์ฉํ๋ค. ๋ณธ ๋
ผ๋ฌธ์ ์ ๊ฒฝ๋ง์ ์ถ์ ์ค๋ฅ๋ฅผ ๋ถ์ํ๊ณ ์ค๋ฅ ๋ชจ๋ธ์ ๋์ถํ๋ค. ๊ทธ๋ฐ ๋ค์ ์ค๋ฅ ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ์ฐ์ ํ๋ ์ ๊ฐ์ ์๋ ํ์ ๊ฐ๋(Relative rotation angle)๋ฅผ ๋ฐ์ํ๋ฉด์ ์ด๊ธฐ ์ถ์ ์น๋ก๋ถํฐ ์๊ฐ์ ์ผ๋ก ์ผ๊ด๋ ํ์ ๊ฐ๋๋ฅผ ์ถ์ ํ๋ ์ต๋ ์ฌํ ๋ฌธ์ (Maximum a posteriori problem)๋ก ๋์์ ์ํ ๋ณด์ ์ ๊ณต์ํํ๋ค. ๋ง์ง๋ง์ผ๋ก, ๋์์ ์ํ ๋ณด์ ๋ฐ ๋์์ ์์ ํ(Video stabilization)์ ๋ํ ๋์ ์ ๊ทผ ๋ฐฉ๋ฒ์ ์ ์ํ์ฌ ์ํ ๋ณด์ ๊ณผ ์์ ํ๋ฅผ ๋ณ๋๋ก ์ํํ ๋ ๋ฐ์ํ๋ ๊ณต๊ฐ ์ ๋ณด ์์ค๊ณผ ์ฐ์ฐ๋์ ์ต์ํํ๋ฉฐ ์์ ํ์ ์ฑ๋ฅ์ ์ต๋ํํ๋ค. ์คํ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด ๋์์ ์ํ ๋ณด์ ์ผ๋ก ๊ธฐ์ธ์ด์ง ๋์์์ ํจ๊ณผ์ ์ผ๋ก ๋ณด์ ํ ์ ์์ผ๋ฉฐ ๋์์ ์์ ํ ๋ฐฉ๋ฒ๊ณผ ๊ฒฐํฉํ์ฌ ํ๋ค๋ฆฌ๊ณ ๊ธฐ์ธ์ด์ง ๋์์์ผ๋ก๋ถํฐ ์๊ฐ์ ์ผ๋ก ๋ง์กฑ์ค๋ฌ์ด ์๋ก์ด ๋์์์ ํ๋ํ ์ ์๋ค.๋ณธ ๋
ผ๋ฌธ์ ์ผ๋ฐ์ธ๋ค์ด ์ดฌ์ํ ๋์์์์ ํํ ๋ฐ์ํ๋ ๋ฌธ์ ์ธ ๊ธฐ์ธ์ด์ง์ ์ ๊ฑฐํ์ฌ ์ํ์ด ์ฌ๋ฐ๋ฅธ ๋์์์ ํ๋ํ ์ ์๊ฒ ํ๋ ๋์์ ์ํ ๋ณด์ (Video upright adjustment) ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ๋ณธ ๋
ผ๋ฌธ์ ์ ๊ทผ ๋ฐฉ์์ ๋ฅ ๋ฌ๋(Deep learning)๊ณผ ๋ฒ ์ด์ง์ ์ธํผ๋ฐ์ค(Bayesian inference)๋ฅผ ๊ฒฐํฉํ์ฌ ๋์์ ํ๋ ์(Frame)์์ ์ ํํ ๊ฐ๋๋ฅผ ์ถ์ ํ๋ค. ๋จผ์ ์
๋ ฅ ๋์์ ํ๋ ์์ ํ์ ๊ฐ๋์ ์ด๊ธฐ ์ถ์ ์น๋ฅผ ์ป๊ธฐ ์ํด ํ์ ์ ๊ฒฝ๋ง(Convolutional neural networkMasterdCollectio
Video Acceleration Magnification
The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this work we propose a method to cope with large motions while
still magnifying small changes. We make the following two observations: i)
large motions are linear on the temporal scale of the small changes; ii) small
changes deviate from this linearity. We ignore linear motion and propose to
magnify acceleration. Our method is pure Eulerian and does not require any
optical flow, temporal alignment or region annotations. We link temporal
second-order derivative filtering to spatial acceleration magnification. We
apply our method to moving objects where we show motion magnification and color
magnification. We provide quantitative as well as qualitative evidence for our
method while comparing to the state-of-the-art.Comment: Accepted paper at CVPR 2017. Project webpage:
http://acceleration-magnification.github.io
Fast Full-frame Video Stabilization with Iterative Optimization
Video stabilization refers to the problem of transforming a shaky video into
a visually pleasing one. The question of how to strike a good trade-off between
visual quality and computational speed has remained one of the open challenges
in video stabilization. Inspired by the analogy between wobbly frames and
jigsaw puzzles, we propose an iterative optimization-based learning approach
using synthetic datasets for video stabilization, which consists of two
interacting submodules: motion trajectory smoothing and full-frame outpainting.
First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on
the probabilistic flow field. The confidence map associated with the estimated
optical flow is exploited to guide the search for shared regions through
backpropagation. Second, we take a divide-and-conquer approach and propose a
novel multiframe fusion strategy to render full-frame stabilized views. An
important new insight brought about by our iterative optimization approach is
that the target video can be interpreted as the fixed point of nonlinear
mapping for video stabilization. We formulate video stabilization as a problem
of minimizing the amount of jerkiness in motion trajectories, which guarantees
convergence with the help of fixed-point theory. Extensive experimental results
are reported to demonstrate the superiority of the proposed approach in terms
of computational speed and visual quality. The code will be available on
GitHub.Comment: Accepted by ICCV202
A Primer for Folklore Videographers
This is an orientation for novices beginning to shoot interviews and events. The cost of making and distributing videos on the Internet continues to drop and now the primary barrier to using video is acquiring the storytelling and technical skills to make movies that people will watch
Exploring Hollywood Cinema: A Look at Cinematic Techniques and the Classical Hollywood Ideology
The focus of this thesis is to explore the ideas behind Hollywood Cinema and techniques used to craft camera shots of todayโs cinema. The movie industry is one of the United Statesโ biggest export profits; because of this, American movies standout above the rest. I will be exploring and demonstrating the researched techniques of American cinema
Autonomous Execution of Cinematographic Shots with Multiple Drones
This paper presents a system for the execution of autonomous cinematography
missions with a team of drones. The system allows media directors to design
missions involving different types of shots with one or multiple cameras,
running sequentially or concurrently. We introduce the complete architecture,
which includes components for mission design, planning and execution. Then, we
focus on the components related to autonomous mission execution. First, we
propose a novel parametric description for shots, considering different types
of camera motion and tracked targets; and we use it to implement a set of
canonical shots. Second, for multi-drone shot execution, we propose distributed
schedulers that activate different shot controllers on board the drones.
Moreover, an event-based mechanism is used to synchronize shot execution among
the drones and to account for inaccuracies during shot planning. Finally, we
showcase the system with field experiments filming sport activities, including
a real regatta event. We report on system integration and lessons learnt during
our experimental campaigns
Little Trees
I believe in the power of film as a medium of storytelling. My desire to head a year-long production as well as create a moving story that underlined my interest in sibling relationships motivated me to create a short film as my honors thesis. My film, Little Trees, emphasizes themes such as alienation and growth within a broken household. My ultimate goal is to become an artist who creates meaningful cinema by telling deeply personal stories, and creating Little Trees was a successful first step in that process
Style Talk: A collection of Interviews on Personal Style
My concept for this creative Capstone project is to see how much of a correlation there is between culture and fashion. I wanted to speak to people from a variety of backgrounds to see how much a personโs family and culture impacts the way one regards fashion and affects the way one dresses. In order to have a diverse set of interviews, I chose to interview three American female students and three non-American female students who attend Syracuse University.
In the past, I have noticed that many style shows focus on the current trends and do profiles on celebrities and fashion designers. Those stories are filled with entertainment news and are intended to feed us information about whatโs up and coming in the fashion and beauty world. There are television pieces I have seen online, in which interview centers on the designer and a couple models before a runway show. I appreciate interviews that take the time to discover the designerโs inspiration for a collection and the modelโs experience preparing for the show, because it seems more intimate than a typical promotional interview.
Jessica Simpsonโs The Price of Beauty, which aired on VH1 in March 2010, was a show aimed at discussing culture with fashion and beauty with local citizens around the world. It seemed coincidental that her show came out around the time I developed an idea for my Capstone. I knew that different cultures have varying opinions on fashion and beauty, but I wanted to localize that idea, since itโs out of my budget to fly around the world. Syracuse University has a diverse body of students and I wanted to focus on the individual who happens to be part of a specific culture. That way I can get to know that femaleโs personal story and not stereotype her cultureโs perception of fashion and beauty. It will solely be the views of someone who comes from a particular culture.
As a Television-Radio-Film major, I used video as my medium, but also incorporated still images and graphics into the videos, so it would be a multiplatform project. I did short interviews instead of one short twenty-minute movie, so the interviews would be easy to upload online and people can watch one of the interviews during a quick five-minute break. Through a handheld flipcamera, I recorded my interviews. The advantage of the flipcamera was the ability to easily upload my footage to any computer and edit in the specialized labs that contain the video editing programs.
After putting all the interviews together, I branded the project as Style Talk. Itโs a simple, self-explanatory title that fits the project perfectly. My interviews focused on individuals to talk about their personal style. Itโs a direct process where these young women tell their story to the camera and talk about their style, hence Style Talk. I noticed throughout the interviews that all the girls had a varied amount of interest in regards to fashion. Even the girl who would be classified as the โleast fashionableโ still puts effort into her look knowing that she doesnโt follow the high-end fashion magazines and works with pieces that she can afford and tailors them to suit her personal needs.
Overall, family and culture has had some effect on the way these girls view fashion. What theyโve observed with fashion among their family, friends, and the media, has given them a foundation of how they perceive fashion, in which they have developed their personal style over the years, so now they can make independent decisions in regards to what they wear on a daily basis
้ซ้ใใธใงใณใ็จใใใชใขใซใฟใคใ ใใใชใขใถใคใญใณใฐใจๅฎๅฎๅใซ้ขใใ็ ็ฉถ
ๅบๅณถๅคงๅญฆ(Hiroshima University)ๅๅฃซ(ๅทฅๅญฆ)Doctor of Engineeringdoctora
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