DOC PREVIEW
CUNY CSC I6716 - Computer Vision Project Topics

This preview shows page 1-2-3-4-5 out of 15 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 15 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Computer Vision Project TopicsCSc I6716Spring2011Project Reports1. Introduction (problem –Why, with real‐world applications)2. Related Work (What has been done, with a few ref erences)3. Your Approach (How you are going to do it, with algorithms, equations, figures)4. Your Implementation and Analysis (What you do, with images, tables, and numbers)5. Your Conclusions (Itemized conclusions, observations and discussions)All Reports in hard copy due May 17, 2011 before class (hard deadline)Each student has 10 minutes (8 talk +2 QA) x 15 students = 2.5 hours2Rubik cube recognition• Team– Jay Junjie Yao– Felix Cen– Wahyu Jati Nugroho• Key Components– Edge detection: an initial detection of the cube object– Color detection: scan the color cells for each visible side– Feature extraction: det ect all small squares and cube features (3D localization and orientation)• Implementation– Java and OpenCV (JavaCV)– You need to specify contributions of each (3x)3Car recognition in images • Team– Nikolaos Markou• Key Components– object learning and recognition– Where is 3D and perspective geometry?• Implementation– Maybe car distance and orientation estimation?4A Robust Shape Model for Multi‐View Car Alignment Yan Li, Leon Gu, and Takeo KanadeThe IEEE International Conference on Computer Vision and Pattern Recognition, June, 2009.Image searching• Team– Jianfang Li – Could you team up with Nikolaos Markou?• Key Components– The project is to find a target image from bunch of images by scale‐invariant feature transform (SIFT)– This is too broad and vague, lack details– How does this have anything to do with 3D vision?– Are you dealing with perspective distortion?5Detection of possible cancer using computer vision• Team– Carlo Garcia– Razia Sultana • Key Ideas– A High Resolution picture of a histology slides and a pathologist uses some staining techniques undera microscope to determine the average intensity by sections of the high resolution image. – How can computer vision help?– Image registration of images before and after?6Dete ction of stairs• Team– Franqueli Mendez• Key Ideas– Line detection– Parallel lines– Stair models– 3D estimation• Comments– Obstacle and face detection are too common and hard7Door detection• Team– Hengyu Ji• Key Ideas– Features: • Lines, qualdrilaterials?– Perspective distortion:• Position and orientation?8Face orientation estimation• Team– Jose Maldonado• Project Ideas– 1. Face Recognition– 2. Bar code reading system• Implementations– Lack details– Detection face orientations ?9Many Faces of Hillary ClintonVision‐based smart board• Team– Omar Olivo• Project Ideas– interact with a projection screen using a laser pointer projected on the screen– use Kinect to create better interactivity with the screen when teachers are in front of the classrooms10Fast object recognition by standardized edge templates• Qiang Li– step1: make a standardized template library (all fruit, 51x51)step2: ex tract individual object from a image and generate a single line edge (contour???).step3: standardize the image(normalized, single line, 51x51).step4: use the central point as fix point, clockwise scan the two template images (contour projection?). step5: choose a tolerance value(3 or 5 pixels) to ev aluate the image with each template, and get a score (contour matching).step6: decide what kind of fruit it is by lowest score.• Next step– Relational template library11Renaissance Painting & Computer Vision• Monica CONCEPCION– chiaroscuro style paintings featuring intense darkness/brightness together • Tasks– identify objects in paintings that are done in the chiaroscuro style– Shape from shading?12Nativity at Night by Geertgen tot SintJans, c. 1490, after a composition by Hugo van der Goes of c. 1470. Sources of light are the infant Jesus, the shepherds' fire on the hill behind, and the angel who appears to them.Estimating Roulette game outcome based on multiple images• Team– Lior Baron• Key Ideas– Use 3 images of the system in motion, and an image of the roulette wheel to map out the section of the wheel.• Estimate the ball position speed.• Estimate the rotation speed of the roulette wheel• Estimate the section of the roulette wheel in which the ball will make contact first.• Implementation– use HD videos posted on youtube to get screen captures and test13Project Reports1. Introduction (problem –Why, with real‐world applications)2. Related Work (What has been done, with a few ref erences)3. Your Approach (How you are going to do it, with algorithms, equations, figures)4. Your Implementation and Analysis (What you do, with images, tables, and numbers)5. Your Conclusions (Itemized conclusions, observations and discussions)All Reports in hard copy due May 17, 2011 before class (hard deadline)Each student has 10 minutes (8 talk +2 QA) x 15 students = 2.5


View Full Document

CUNY CSC I6716 - Computer Vision Project Topics

Download Computer Vision Project Topics
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Computer Vision Project Topics and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Computer Vision Project Topics 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?