68,409 research outputs found
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
This work proposes a process for efficiently searching over combinations of
individual object 6D pose hypotheses in cluttered scenes, especially in cases
involving occlusions and objects resting on each other. The initial set of
candidate object poses is generated from state-of-the-art object detection and
global point cloud registration techniques. The best-scored pose per object by
using these techniques may not be accurate due to overlaps and occlusions.
Nevertheless, experimental indications provided in this work show that object
poses with lower ranks may be closer to the real poses than ones with high
ranks according to registration techniques. This motivates a global
optimization process for improving these poses by taking into account
scene-level physical interactions between objects. It also implies that the
Cartesian product of candidate poses for interacting objects must be searched
so as to identify the best scene-level hypothesis. To perform the search
efficiently, the candidate poses for each object are clustered so as to reduce
their number but still keep a sufficient diversity. Then, searching over the
combinations of candidate object poses is performed through a Monte Carlo Tree
Search (MCTS) process that uses the similarity between the observed depth image
of the scene and a rendering of the scene given the hypothesized pose as a
score that guides the search procedure. MCTS handles in a principled way the
tradeoff between fine-tuning the most promising poses and exploring new ones,
by using the Upper Confidence Bound (UCB) technique. Experimental results
indicate that this process is able to quickly identify in cluttered scenes
physically-consistent object poses that are significantly closer to ground
truth compared to poses found by point cloud registration methods.Comment: 8 pages, 4 figure
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
Face analysis using curve edge maps
This paper proposes an automatic and real-time system for face analysis, usable in visual communication applications. In this approach, faces are represented with Curve Edge Maps, which are collections of polynomial segments with a convex region. The segments are extracted from edge pixels using an adaptive incremental linear-time fitting algorithm, which is based on constructive polynomial fitting. The face analysis system considers face tracking, face recognition and facial feature detection, using Curve Edge Maps driven by histograms of intensities and histograms of relative positions. When applied to different face databases and video sequences, the average face recognition rate is 95.51%, the average facial feature detection rate is 91.92% and the accuracy in location of the facial features is 2.18% in terms of the size of the face, which is comparable with or better than the results in literature. However, our method has the advantages of simplicity, real-time performance and extensibility to the different aspects of face analysis, such as recognition of facial expressions and talking
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Hand motion capture is a popular research field, recently gaining more
attention due to the ubiquity of RGB-D sensors. However, even most recent
approaches focus on the case of a single isolated hand. In this work, we focus
on hands that interact with other hands or objects and present a framework that
successfully captures motion in such interaction scenarios for both rigid and
articulated objects. Our framework combines a generative model with
discriminatively trained salient points to achieve a low tracking error and
with collision detection and physics simulation to achieve physically plausible
estimates even in case of occlusions and missing visual data. Since all
components are unified in a single objective function which is almost
everywhere differentiable, it can be optimized with standard optimization
techniques. Our approach works for monocular RGB-D sequences as well as setups
with multiple synchronized RGB cameras. For a qualitative and quantitative
evaluation, we captured 29 sequences with a large variety of interactions and
up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer
Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a
single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14
hand tracking paper with several extensions, additional experiments and
detail
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