I return today from a week-long trip to Anchorage, Alaska. I spent the week enjoying the beautiful mountains, and the exciting science being presentented at the Conference for Computer Vision and Pattern Recognition (CVPR 2008) [here are some links to lots of papers from the conference]. This was my first trip to this conference, and I must say that I was impressed with the quality of the work presented. Below, I list some of my favorite papers and give a (very) brief overview:
Using Contours to Detect and Localize Junctions in Natural
Images. Michael Maire, Pablo ArbelÃ¡ez, Charless Fowlkes and Jitendra Malik. CVPR, 2008. [paper]
Maire et al. Make significant progress in edge detection and junction detection by using very clever insights into the way humans perceive and draw images. They also demonstrate using convincing metrics that their methods are the state of the art and rapidly approaching human-level of accuracy.
Stereo and 3D
I was impressed with the amount of focus on 3D reconstruction from either stereo cameras or monocular video cameras. This is something that I have been interested in for some time. Due to the apparent focus on this I may pursue this further in the coming months and try to add myself to this list!
Recovering Consistent Video Depth Maps via Bundle Optimization. Guofeng Zhang, Jiaya Jia, Tien-Tsin Wong and Hujun Bao. CVPR, 2008. [project website]
This was one of the first talks I saw and I was floored by the results. The authors use a single video stream to compute beautiful 3D models of the scene. The algorithm makes use of the temporal consistency of video as well as visual similarities between subsequent frames.
Stereoscopic Inpainting: Joint Color and Depth Completion from Stereo Images. Liang Wang, Hailin Jin, Ruigang Yang and Minglun Gong. CVPR, 2008.
Here, authors combine the ideas of in-painting (filling missing areas in an image automatically) and stereo depth estimation to dramatically improve both disciplines. By using in-painting methods, occlusions in stereo pairs can be accurately completed, and by supplementing visual in-painting with depth estimates, better results can be obtained.
3D Point Clouds
The analysis of shapes and 3D objects often relies on the use of a cloud of points on the surface of an object. Although this wasn’t as popular as stereo at CVPR, it was still a noteworthy discipline… and one that is likely to grow.
Three-Dimensional Point Cloud Recognition via Distributions of Geometric Distances. Mona Mahmoudi and Guillermo Sapiro. CVPR, 2008. [google cache of paper]
Shape recognition is a very complicated problem. Here, Mahmoudi and Sapiro take a very refreshing new approach. By computing histograms of features such as pair-wise distances, and curvature they create a system capable of matching shapes very well to ones in large databases. Their method simplifies this hard task and yields excellent results.
Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. Romeil Sandhu, Samuel Dambreville and Allen Tannenbaum. CVPR, 2008.
Cry nepotism if you like, but this is excellent paper from my colleague Rome Sandhu. Lining up point clouds is a task that crops up all over. This technique allows registration of small bits of data onto known models and even bits of data onto other bits of data. The results are simply incredible considering it would be a challenge to do this even for a human!
Summarizing Visual Data Using Bidirectional Similarity. Denis Simakov, Yaron Caspi. Eli Shechtman and Michal Irani. CVPR, 2008. [project website]
I was amazed last year by a technique called Seam Carving used to re-size images without losing or shrinking important information. This technique accomplishes the same goal, and sometimes does a far better job. The approach is two-fold: 1) make sure the smaller image has as much data as possible from the original, 2) make sure the smaller image doesn’t have any *new* data. The second constraint ensures that no artifacts develop.
Geometric Modeling of Tubular Structure. Huseyin Tek and M. Akif Gulsun. MMBIA(CVPR), 2008.
I am currently working on vessel analysis myself, and have been reviewing different methods of construction a “skeleton” or stick-like model of a vessel structure. Tek and Gulsun of Siemens Corporate Research have a very clever solution wherein they find the best line through a structure by measuring the “roundness” of the surrounding data.
Localized Statistics for DW-MRI Fiber Bundle Segmentation. Shawn Lankton, John Melonakos, James Malcolm, Samuel Dambreville and Allen Tannenbaum. MMBIA(CVPR), 2008. [pdf paper]
Just in case you were wondering, this is the paper that brought me to Alaska to take part in this conference. I use a method that looks locally at image differences in brain scans to find the shape of neuron bundles that run through the brain.