6,419 research outputs found
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
Robust correlated and individual component analysis
© 1979-2012 IEEE.Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) thetemporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methodson these application domains, outperforming other state-of-the-art methods in the field
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
Cramer–Rao lower bounds for change points in additive and multiplicative noise
The paper addresses the problem of determining the Cramer–Rao lower bounds (CRLBs) for noise and change-point parameters, for steplike signals corrupted by multiplicative and/or additive white noise. Closed-form expressions for the signal and noise CRLBs are first derived for an ideal step with a known change point. For an unknown change-point, the noise-free signal is modeled by a sigmoidal function parametrized by location and step rise parameters. The noise and step change CRLBs corresponding to this model are shown to be well approximated by the more tractable expressions derived for a known change-point. The paper also shows that the step location parameter is asymptotically decoupled from the other parameters, which allows us to derive simple CRLBs for the step location. These bounds are then compared with the corresponding mean square errors of the maximum likelihood estimators in the pure multiplicative case. The comparison illustrates convergence and efficiency of the ML estimator. An extension to colored multiplicative noise is also discussed
MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel
This paper considers probabilistic estimation of a low-rank matrix from
non-linear element-wise measurements of its elements. We derive the
corresponding approximate message passing (AMP) algorithm and its state
evolution. Relying on non-rigorous but standard assumptions motivated by
statistical physics, we characterize the minimum mean squared error (MMSE)
achievable information theoretically and with the AMP algorithm. Unlike in
related problems of linear estimation, in the present setting the MMSE depends
on the output channel only trough a single parameter - its Fisher information.
We illustrate this striking finding by analysis of submatrix localization, and
of detection of communities hidden in a dense stochastic block model. For this
example we locate the computational and statistical boundaries that are not
equal for rank larger than four.Comment: 10 pages, Allerton Conference on Communication, Control, and
Computing 201
Locating Camera Position in 3-D Space from Distinct Features of Architecture on 2-D Image
This research aimed to develop an algorithm that estimates the camera position in space from which an image was created using computer vision techniques. The implemented algorithm involves 3 major steps: defining a distinct combination of features of the object, recognizing the object with the distinct features, and calculating camera position using the mapping information between the projected 2-D image and the 3-D object. A generalized approach and a specific case study of the Cathedral of Notre Dame in Paris are discussed in detail
Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis
Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem
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