FAME: Face Association through Model Evolution

We attack the problem of learning face models for public faces from weakly-labelled images collected from web through querying a name. The data is very noisy even after face detection, with several irrelevant faces corresponding to other people. We propose a novel method, Face Association through Model Evolution (FAME), that is able to prune the data in an iterative way, for the face models associated to a name to evolve. The idea is based on capturing discriminativeness and representativeness of each instance and eliminating the outliers. The final models are used to classify faces on novel datasets with possibly different characteristics. On benchmark datasets, our results are comparable to or better than state-of-the-art studies for the task of face identification.Comment: Draft version of the stud

2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy.

Lensfree in-line holographic microscopy offers sub-micron resolution over a large field-of-view (e.g., ~24 mm2) with a cost-effective and compact design suitable for field use. However, it is limited to relatively low-density samples. To mitigate this limitation, we demonstrate an on-chip imaging approach based on pixel super-resolution and phase recovery, which iterates among multiple lensfree intensity measurements, each having a slightly different sample-to-sensor distance. By digitally aligning and registering these lensfree intensity measurements, phase and amplitude images of dense and connected specimens can be iteratively reconstructed over a large field-of-view of ~24 mm2 without the use of any spatial masks. We demonstrate the success of this multi-height in-line holographic approach by imaging dense Papanicolaou smears (i.e., Pap smears) and blood samples

Particle displacement tracking for PIV

A new Particle Imaging Velocimetry (PIV) data acquisition and analysis system, which is an order of magnitude faster than any previously proposed system has been constructed and tested. The new Particle Displacement Tracing (PDT) system is an all electronic technique employing a video camera and a large memory buffer frame-grabber board. Using a simple encoding scheme, a time sequence of single exposure images are time coded into a single image and then processed to track particle displacements and determine velocity vectors. Application of the PDT technique to a counter-rotating vortex flow produced over 1100 velocity vectors in 110 seconds when processed on an 80386 PC