The authors have researched two-letter state name (state name abbreviation) recognition and full state name recognition. According to this research, they think that the accuracy of the character segmentation is essent...
详细信息
Structured light sensors are popular due to their robustness to untextured scenes and multipath. These systems triangulate depth by solving a correspondence problem between each camera and projector pixel. This is oft...
详细信息
ISBN:
(纸本)9781467388511
Structured light sensors are popular due to their robustness to untextured scenes and multipath. These systems triangulate depth by solving a correspondence problem between each camera and projector pixel. This is often framed as a local stereo matching task, correlating patches of pixels in the observed and reference image. However, this is computationally intensive, leading to reduced depth accuracy and framerate. We contribute an algorithm for solving this correspondence problem efficiently, without compromising depth accuracy. For the first time, this problem is cast as a classification-regression task, which we solve extremely efficiently using an ensemble of cascaded random forests. Our algorithm scales in number of disparities, and each pixel can be processed independently, and in parallel. No matching or even access to the corresponding reference pattern is required at runtime, and regressed labels are directly mapped to depth. Our GPU-based algorithm runs at a 1KHz for 1.3MP input/output images, with disparity error of 0.1 subpixels. We show a prototype high framerate depth camera running at 375Hz, useful for solving tracking-related problems. We demonstrate our algorithmic performance, creating high resolution real-time depth maps that surpass the quality of current state of the art depth technologies, highlighting quantization-free results with reduced holes, edge fattening and other stereo-based depth artifacts.
In this paper, we propose a practical scheme for multi-lingual) multi-font, and multi-size large-set character recognition using self-organizing neural network. In order to improve the performance of the proposed sche...
详细信息
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made th...
详细信息
ISBN:
(纸本)9781467388511
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing.pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.
Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolutio...
详细信息
The proceedings contain 67 papers. The topics discussed include: texture description with completed local quantized patterns;a local image descriptor robust to illumination changes;detection of curvilinear structures ...
ISBN:
(纸本)9783642388859
The proceedings contain 67 papers. The topics discussed include: texture description with completed local quantized patterns;a local image descriptor robust to illumination changes;detection of curvilinear structures by tensor voting applied to fiber characterization;simple-graphs fusion in image mosaic: application to automated cell files identification in wood slices;unsupervised segmentation of anomalies in sequential data, images and volumetric data using multiscale Fourier phase-only analysis;extended 3D Line Segments from RGB-d data for pose estimation;incorporating texture intensity information into LBP-based operators;Bayesian non-parametric image segmentation with Markov random field prior;evaluating local feature detectors in salient region detection;forest stand delineation using a hybrid segmentation approach based on airborne laser scanning data;and mean shift with flatness constraints.
The most popular approach to large scale image retrieval is based on the bag-of-visual-word (BoV) representation of images. The spatial information is usually re-introduced as a post-processing.step to re-rank the ret...
详细信息
An approach to image feature extraction is proposed. Complex moments of the Gabor power spectrum are used to detect linear, rectangular, hexagonal/triangular, and other structures with very fine to very coarse resolut...
详细信息
An approach to image feature extraction is proposed. Complex moments of the Gabor power spectrum are used to detect linear, rectangular, hexagonal/triangular, and other structures with very fine to very coarse resolutions. When the method is applied to texture segmentation, good results are obtained.< >
We present a proof of concept system to represent and reason about hockey play. The system takes as input player motion trajectory data tracked from game video and supported by knowledge of hockey strategy, game situa...
详细信息
The authors study the estimation of generalized motion parameters for a deformable object from the range data. If the correspondence between two sets of points representing the coordinates of different points of an ob...
详细信息
ISBN:
(纸本)081861952x
The authors study the estimation of generalized motion parameters for a deformable object from the range data. If the correspondence between two sets of points representing the coordinates of different points of an object undergoing rotational motion and deformation is known, the parameters can be estimated using different least-squares estimators. The total-least-squares (TLS) method is very appropriate when the observation and the data matrices are both perturbed by random noise. For Gaussian-distributed noise, the TLS solution is equivalent to maximum-likelihood estimation. The mean-square error in TLS is always smaller than in an ordinary least-squares (LS) estimator. The scope is analyzed of TLS in estimating the generalized motion parameters, as is the feasibility of decomposing the generalized motion parameters in terms of rotation and deformation parameters. The performance of TLS is compared to that of the LS estimator. It is found that the performance of the LS and TLS methods at low noise levels is almost identical. The accuracy of estimation of direction cosines of the rotation axis and the angle of rotation is not very dependent on whether LS or TLS method is used. However, as the noise level increases in the data matrix, TLS is more accurate in estimating the deformation parameters.
暂无评论