Gil Shapira's Active Appearance Model slides
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wolfThe document presents Active Appearance Models, which use principal component analysis to create a statistical model that captures appearance variations in images. It discusses how PCA is used to model shape and texture independently, then combined into a single model. The model can generate synthetic images and interpret new images by iteratively adjusting parameters to minimize differences between the input and generated images. The presenter shows the model can successfully converge and interpret images if initial parameter estimates are reasonable.
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![Active Appearance Models Presenter: Gil Shapira Instructor: Lior Wolf Date: November 6 , 2006 [4]](https://image.slidesharecdn.com/active-appearance-models-27192/85/Gil-Shapira-s-Active-Appearance-Model-slides-1-320.jpg)
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Lecture17aam
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MNIST and machine learning - presentation
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Report
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The document discusses structure from motion reconstruction from multiple images. It provides an overview of the steps to:
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2. Reconstruct structure and motion for two datasets - a public dataset and a personal dataset acquired by the student.
3. The key steps are feature detection, matching, estimating the fundamental matrix, triangulating 3D points, identifying the plane at infinity to upgrade from projective to affine reconstruction, and further refinement to metric reconstruction if possible.
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Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
Comparative Study and Analysis of Image Inpainting Techniques
Abstract: Image inpainting is a technique to fill missing region or reconstruct damage area from an image.It
removes an undesirable object from an image in visually plausible way.For filling the part of image, it use
information from the neighboring area. In this dissertation work, we present a Examplar based method for
filling in the missing information in an image, which takes structure synthesis and texture sysnthesis together.
In exemplar based approach it used local information from an image to patch propagation.We have also
implement Nonlocal Mean approach for exemplar based image inpainting.In Nonlocal mean approach it find
multiple samples of best exemplar patches for patch propagation and weight their contribution according to
their similarity to the neighborhood under evaluation. We have further extended this algorithm by considering
collaborative filtering method to synthesize and propagate with multiple samples of best exemplar patches. We
have to preformed experiment on many images and found that our algorithm successfully inpaint the target
region.We have tested the accuracy of our algorithm by finding parameter like PSNR and compared PSNR
value for all three different approaches.
Keywords: Texture Synthesis, Structure Synthesis, Patch Propagation ,imageinpainting ,nonlocal approach,
collabrative filtering.
Pca ankita dubey
Objective of PCA is to perform dimensionality reduction while preserving as much of the randomness in the high-dimensional space as possible.
Viii sem
This document compares two image inpainting algorithms: the Fast Marching Method (FMM) and exemplar-based image inpainting. FMM uses structural consistency to fill damaged regions, while exemplar-based uses both structural and textural consistency. FMM is faster but does not preserve edges as well as exemplar-based. Exemplar-based works for both small and large regions but is slower. Both algorithms were tested on photos for tasks like removing objects or adding effects. Exemplar-based was better for large regions and edge preservation, while FMM was better for speed and small regions.
Similar to Gil Shapira's Active Appearance Model slides (20)
3D Reconstruction from Multiple uncalibrated 2D Images of an Object
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Ala Stolpnik's Standard Model talk
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- 1. Active Appearance Models Presenter: Gil Shapira Instructor: Lior Wolf Date: November 6 , 2006 [4]
- 26. Results [2]
- 32. Thank You!