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This project aims to implement an efficient Linking Features Algorithm for detecting key parts of deformable objects, such as human and animal anatomy. Employing techniques like SIFT for image feature extraction, Generalized Hough Transform for part candidate generation, and voting for part orientations, the project aims to enhance the accuracy of part connectivity estimation. The methodology involves preprocessing the input image, grouping features, and inferring the most probable configuration of parts through a greedy MAP inference approach.
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AmmarKamalHattab ENGN2560 Final Project Proposal March 11, 2013 Using linking features in learning Non-parametric part models
Project Goal Nk Torso • Project Goal: implement Linking Features Algorithm to detect a set of parts of a deformable object. • Examples: • detect human parts: head, torso,upper/lower limbs • detect facial landmarks: eyes, nose, mouth outlines, etc. • detect animal parts • … tll trl bll brl
Linking Features Method • Step 1 Preprocessing : • Finding bounding box • Computing image features (SIFT descriptors on a regular grid) Preprocessing Input Image SIFTs
Linking Features Method • Step 2 Individual Parts: • SIFTs are grouped to generate part candidates for each part. • Each part candidate features vote for three orientations and one length estimate • Involves Generalized Hough Transform (GHT) • Using Training Images to help computeApproximate Nearest Neighbors featuresefficiently Preprocessing Individual parts Input Image SIFTs part candidates
Linking Features Method • Step 3: Pairwise Connectivity • Find ‘linking features’ for each pair part candidates using Training (to find nearest neighbors) • Find the correct choice of part candidates: • Score of a pair of part candidates is measured by the cumulative score of the linking features between them • This results in combined set of estimated connectivity parameters Preprocessing Individual parts Input Image Pairwise Connectivity connectivity parameters part candidates
Linking Features Method • Step 4: Finding the most likely global parts configuration • The most likely joint configuration of parts is inferred from the model. • the most consistent subset of candidate parts is computed by a greedy approximate MAP inference over the estimated model Preprocessing Individual parts Input Image Pairwise Connectivity Global Parts Configuration
Project Plan • Stage 1: Implement Preprocessing (and finding SIFTs) • Stage 2: Implement Training (to help compute Approximate Nearest Neighbors Features) • Stage 3: Implement Finding parts candidates (Involves Generalized Hough Transform (GHT) and clustering) • Stage 4: Finding Linking Features using Training Images • Stage 5: Finding the most likely global parts configuration using greedy approach Preprocessing Individual parts Input Image Pairwise Connectivity Global Parts Configuration Parts
References • Leonid Karlinsky, Shimon Ullman: Using Linking Features in Learning Non-parametric Part Models. ECCV (3) 2012: 326-339 • Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV (2005)
Training Phase • The training phase of the method receives a set of images with annotated parts (e.g. stick annotation in upper body experiments). • Enlarge them and find each part descriptor • Then building efficient data structures for similar descriptor search (Approximate Nearest Neighbors ANN) and memorizing a set of parameters for each feature, e.g. relative offset from different parts. • These data structures are later used in order to compute the model probabilities (features probabilities) for test images using Kernel Density Estimation (KDE) Training Images A test feature KDE Fi GHT=
