5670917
Improving the Robustness of Language Models - UIUC TREC 2003 Robust and Genomics Experiments
In this paper, we report our experiments in the TREC 2003 Genomics Track and the Robust Track. A common theme that we explored is the robustness of a basic language modeling retrieval approach. We examine several aspects of robustness, including robustness in handling different types of queries, different types of documents, and optimizing performance for difficult topics. Our basic retrieval method is the KL-divergence retrieval model with the two-stage smoothing method plus a mixture model feedback method. In the Genomics IR track, we propose a new method for modeling semi-structured queries using language models, which is shown to be more robust and effective than the regular query model in handling gene queries. In the Robust track, we experimented with two heuristic approaches to improve the robustness in using language models for pseudo feedback.
7037192
Interactive material replacement in photographs
Material replacement has wide application throughout the entertainment industry, particularly for post-production make-up application or wardrobe adjustment. More generally, any low-cost mock-up object can be processed to have the appearance of expensive, high-quality materials. We demonstrate a new system that allows fast, intuitive material replacement in photographs. We extend recent work in object selection and fast texture synthesis, as well as develop a novel approach to shape-from-shading capable of handling objects with albedo changes. Each component of our system runs with interactive speed, allowing for easy experimentation and refinement of results.
8559794
Randomly accessible procedural animation of physically approximate turbulent motion
Complex virtual environments can be simulated with physical or procedural motion. Physical motion is more realistic, but requires the integration of an ordinary differential equation from an initial state. Procedural motion has the advantage of being randomly accessible, though it is not physically based. We combine these techniques into a multiresolution model. A coarse-level simulation is sampled to yield keyframes. Then a physical motion between keyframes is calculated and distorted to meet its boundary conditions. We demonstrate these ideas with a simple virtual world consisting of wind blowing trees and leaves that can be entered and experienced efficiently at any point in time.
9299231
Language Models for Expert Finding--UIUC TREC 2006 Enterprise Track Experiments
In this paper, we report our experiments in the TREC 2006 Enterprise Track. Our focus is to study a language model for expert finding. We extend an existing language model for expert retrieval in three aspects. First, we model the document-expert association using a mixure model instead of name matching heuristics as in the existing work; such a mixture model allows us to put different weights on email matching and name matching. Second, we propose to model the prior of an expert based on the counts of email matches in the supporting documents instead of using uniform prior as in the previous work. Finally, we perform topic expansion and generalize the model from computing the likelihood to computing the cross entropy. Our experiments show that the first two extensions are more effective than the third extension, though when optimized, they all seem to be effective.
13897908
Detail preserving shape deformation in image editing
Shape deformation is a common practice in digital image editing, but can unrealistically stretch or compress texture detail. We propose an image editing system that decouples feature position from pixel color generation, by resynthesizing texture from the source image to preserve its detail and orientation around a new feature curve location. We introduce a new distortion to patch-based texture synthesis that aligns texture features with image features. A dense correspondence field between source and target images generated by the control curves then guides texture synthesis.
5098005
RotoTexture: Automated Tools for Texturing Raw Video
We propose a video editing system that allows a user to apply a time-coherent texture to a surface depicted in the raw video from a single uncalibrated camera, including the surface texture mapping of a texture image and the surface texture synthesis from a texture swatch. Our system avoids the construction of a 3D shape model and instead uses the recovered normal field to deform the texture so that it plausibly adheres to the undulations of the depicted surface. The texture mapping method uses the nonlinear least-squares optimization of a spring model to control the behavior of the texture image as it is deformed to match the evolving normal field through the video. The texture synthesis method uses a coarse optical flow to advect clusters of pixels corresponding to patches of similarly oriented surface points. These clusters are organized into a minimum advection tree to account for the dynamic visibility of clusters. We take a rather crude approach to normal recovering and optical flow estimation, yet the results are robust and plausible for nearly diffuse surfaces such as faces and t-shirts
11027312
An Implementation of Time and Frequency Synchronization for Carrier Interferometry of OFDM in an Underwater Acoustic Channel
Considering the features of Underwater Acoustic channel and the sensitivity versus synchronization for CI/OFDM system, A time and frequency synchronization method based on Constant Amplitude Zero Auto-Correlation(CAZAC) sequence is presented. The time offset estimation and carrier frequency offset estimation can be achieved simultaneously. The fractional part and integral part of carrier offset estimation can be respectively got by the correlation result of time offset estimation and cyclic correlation principle. The efficacy of the synchronization algorithms is demonstrated experimentally.
6933858
Region Growing with Automatic Seeding for Semantic Video Object Segmentation
As content-based multimedia applications become increasingly important, demand for technologies on semantic video object segmentation is growing, where the segmented objects are expected to be in line with human visual perception. Existing research is limited to semi-automatic approach, in which human intervene is often required. These include manual selection of seeds for region growing or manual classification of background edges etc. In this paper, we propose an automatic region growing algorithm for video object segmentation, which features in automatic selection of seeds and thus the entire segmentation does not require any action from human users. Experimental results show that the proposed algorithm performs well in terms of the effectiveness in video object segmentation.
18215845
A hybrid scheme for temporal video segmentation
Video temporal segmentation is normally the first and important step for content-based video applications. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries. In this paper, we propose a hybrid scheme to combine some features and techniques for detecting all sorts of shot cuts inside general videos. The hybrid scheme contains two processing modes, which are unified by a mode-selector to decide which mode the scheme should work on in order to achieve accurate temporal video segmentation. Via using the publicly test data set from Carleton University, we report extensive experimental results to evaluate the proposed algorithm. In comparison with existing algorithms, the experimental results support that the proposed algorithm outperforms the benchmark in terms of the precision-recall rates in detecting all types of shot cuts.
1612186
3D facial geometry recovery via group-wise optical flow
We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a minimum description length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.
17735204
3D Face Reconstruction Under Imperfect Tracking Circumstances Using Shape Model Constraints
We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the 3D face structure under noisy observation (tracking) circumstances. By minimizing two sets of errors, the reconstruction error generated by the linear transform of the shape models and projection error obtained by re-projecting the 3D shape to 2D positions, the 3D face shapes can be recovered optimally. Experimental results show that this algorithm accurately reconstructs the 3D shape of familiar and non-familiar faces from video sequences under circumstances of imperfect face tracking or noisy observations.