Thursday, July 29, 2010
Monday, July 26, 2010
Authors: Jonathan Taylor Allan D. Jepson Kiriakos N. Kutulakos
We introduce locally-rigid motion, a general framework for
solving the M-point, N-view structure-from-motion problem
for unknown bodies deforming under orthography. The
key idea is to first solve many local 3-point, N-view rigid
problems independently, providing a “soup” of specific,
plausibly rigid, 3D triangles. The main advantage here is
that the extraction of 3D triangles requires only very weak
assumptions: (1) deformations can be locally approximated
by near-rigid motion of three points (i.e., stretching not
dominant) and (2) local motions involve some generic rotation
in depth. Triangles from this soup are then grouped
into bodies, and their depth flips and instantaneous relative
depths are determined. Results on several sequences,
both our own and from related work, suggest these conditions
apply in diverse settings—including very challenging
ones (e.g., multiple deforming bodies). Our starting point
is a novel linear solution to 3-point structure from motion,
a problem for which no general algorithms currently exist.
Saturday, July 24, 2010
Lab Meeting July 20, 2010 (fish60): What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers
Here's the content:
What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers
Shuo Chen, Jianwen Zhang, Guangyun Chen, Changshui Zhang
State Key Laboratory on Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Automation, Tsinghua University, Beijing 100084, China
Learning from multiple teachers or sources
has received more attention of the researchers in the machine
learning area. In this setting, the learning system is dealing
with samples and labels provided by multiple teachers, who
in common cases, are non-expert. Their labeling styles and
behaviors are usually diverse, some of which are even detrimental
to the learning system. Thus, simply putting them
together and utilizing the algorithms designed for singleteacher
scenario would be not only improper, but also damaging.
Our work focuses on a case where the teachers are composed of good
ones and irresponsible ones. By irresponsible, we mean the
teacher who takes the labeling task not seriously and label
the sample at random without inspecting the sample itself.
If we do not take out their effects, our learning system would be ruined with no
doubt. In this paper, we propose a method for picking out the
good teachers with promising experimental results. It works
even when the irresponsible teachers are dominating in numbers.
Wednesday, July 21, 2010
換言之，不管個人興趣是否與指導教授研究領域相近，繼續跟定指導教授，不輕言放棄；且莫在博士資格考試〈PhD Qualifying Examination〉未通過前提出，以免造成輟學的嚴重後果。
經過長期溝通，最後A生接受指導教授建議，先休學、工作一段時間，再考慮是否繼續完成博士學位。B生則同時加入另一教授之研究團隊，不排除於畢業後往學術界發展；其後續已不再為研究課題而煩惱，並已在新覓研究領域之尖端會議中發表論文。由於處理得宜，目前這兩位高材生仍與原來指導教授保持良好關係。畢竟，恩師難覓，必須知福惜福；師生情難建，值得一生珍惜！ 〈王榮騰 臺大電機系與電子工程研究所客座教授;2010年6月6日〉
Sunday, July 18, 2010
Lab Meeting July 20, 2010 (Gary): Robust Unified Stereo-Based 3D Head Tracking and Its Application to Face Recognition (ICRA2010)
Robust Unified Stereo-Based 3D Head Tracking and Its Application
to Face Recognition
Authors: Kwang Ho An and Myung Jin Chung
This paper investigates the estimation of 3D head poses and its identity authentication with a partial ellipsoid model. To cope with large out-of-plane rotations and translation in-depth, we extend conventional head tracking with a single camera to a stereo-based framework. To achieve more robust motion estimation even under time-varying lighting conditions, we incorporate illumination correction into the aforementioned framework. We approximate the face image variations due to illumination changes as a linear combination of illumination bases. Also,��by computing the illumination bases online from the registered face images, after estimating the 3D head poses, user-specific illumination bases can be obtained, and therefore illumination-robust tracking without a prior learning process can be possible. Furthermore, our unified stereo-based tracking is approximated as a linear least-squares problem; a closed-form solution is then provided. After recovering the full-motions of the head, we can register face images with pose variations into stabilized-view images, which are suitable for pose-robust face recognition. To verify the feasibility and applicability of our approach, we performed extensive experiments with three sets of challenging image sequences.
Thursday, July 15, 2010
Lab Meeting July 20, 2010 (Jimmy): Group-Sensitive Multiple Kernel Learning for Object Categorization
Authors: Jingjing Yang, Yuanning Li, Yonghong Tian, Lingyu Duan, Wen Gao
In: ICCV 2009
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories. For each category, images within a group share a set of kernel weights while images from different groups may employ distinct sets of kernel weights. In GS-MKL, such group-sensitive kernel combinations together with the multi-kernels based classifier are optimized in a joint manner to seek a trade-off between capturing the diversity and keeping the invariance for each category. Extensive experiments show that our proposed GS-MKL method has achieved encouraging performance over three challenging datasets.
Monday, July 12, 2010
Lab Meeting July 13, 2010(ShaoChen):Rao-Blackwellized Particle Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communication(ICRA 2010)
Authors: Luca Carlone, Miguel Kaouk Ng, Jingjing Du, Basilio Bona, and Marina Indri
Multi robot systems are envisioned to play an important role in many robotic applications. A main prerequisite for a team deployed in a wide unknown area is the capability of autonomously navigate, exploiting the information acquired through the on-line estimation of both robot poses
and surrounding environment model, according to Simultaneous Localization And Mapping (SLAM) framework. As team coordination is improved, distributed techniques for filtering
are required in order to enhance autonomous exploration and large scale SLAM increasing both efficiency and robustness of operation. Although Rao-Blackwellized Particle Filters (RBPF) have been demonstrated to be an effective solution to the problem of single robot SLAM, few extensions to teams of robots exist, and these approaches are characterized by strict assumptions on both communication bandwidth and prior knowledge on relative poses of the teammates. In the present paper we address the problem of multi robot SLAM in the case of limited communication and unknown relative initial poses. Starting from the well established single robot RBPFSLAM, we propose a simple technique which jointly estimates SLAM posterior of the robots by fusing the prioceptive and the eteroceptive information acquired by each teammate. The approach intrinsically reduces the amount of data to be exchanged among the robots, while taking into account the uncertainty in relative pose measurements. Moreover it can be naturally extended to different communication technologies (bluetooth, RFId, wifi, etc.) regardless their sensing range. The proposed approach is validated through experimental test.
Lab Meeting July 13,2010(Nicole):Mutual Localization in a Team of Autonomous Robots using Acoustic Robot Detection
Authors: David Becker and Max Risler
In RoboCup 2008: Robot Soccer World Cup XII ,Volume 5399/2009
In order to improve self-localization accuracy we are exploring ways of mutual localization in a team of autonomous robots. Detecting team mates visually usually leads to inaccurate bearings and only rough distance estimates. Also, visually identifying teammates is not possible. Therefore we are investigating methods of gaining relative position information acoustically in a team of robots.
The technique introduced in this paper is a variant of code-multiplexed communication (CDMA, code division multiple access). In a CDMA system, several receivers and senders can communicate at the same time, using the same carrier frequency. Well-known examples of CDMA systems include wireless computer networks and the Global Positioning System, GPS. While these systems use electro-magnetic waves, we will try to adopt the CDMA principle towards using acoustic pattern recognition, enabling robots to calculate distances and bearings to each other.
First, we explain the general idea of cross-correlation functions and appropriate signal pattern generation. We will further explain the importance of synchronized clocks and discuss the problems arising from clock drifts.
Finally, we describe an implementation using the Aibo ERS-7 as platform and briefly state basic results, including measurement accuracy and a runtime estimate. We will briefly discuss acoustic localization in the specific scenario of a RoboCup soccer game.
Tuesday, July 06, 2010
Monday, July 05, 2010
Jennifer Dolson, Jongmin Baek, Christian Plagemann and Sebastian Thrun (Stanford University)
We present a flexible method for fusing information from optical and range sensors based on an accelerated high-dimensional filtering approach. Our system takes as input a sequence of monocular camera images as well as a stream of sparse range measurements as obtained from a laser or other sensor system. In contrast with existing approaches, we do not assume that the depth and color data streams have the same data rates or that the observed scene is fully static. Our method produces a dense, high-resolution depth map of the scene, automatically generating confidence values for every interpolated depth point. We describe how to integrate priors on object shape, motion and appearance and how to achieve an efficient implementation using parallel processing hardware such as GPUs.