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Current fiducial marker detection algorithms rely on marker IDs for false positive rejection. Time is wasted on potential detections that will eventually be rejected as false positives. We introduce ChromaTag, a fiducial marker and detection algorithm designed to use opponent colors to limit and quickly reject initial false detections and grayscale for precise localization. Through experiments, we show that ChromaTag is significantly faster than current fiducial markers while achieving similararXiv:1708.02982v1 fatcat:znwj25souvh5jguzozzfqa6qim
more »... r better detection accuracy. We also show how tag size and viewing direction effect detection accuracy. Our contribution is significant because fiducial markers are often used in real-time applications (e.g. marker assisted robot navigation) where heavy computation is required by other parts of the system.
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalized-camera-model pose-and-scale estimator thatarXiv:2004.02052v1 fatcat:qq5pcaapsjfwroouqwjq4w7ybu
more »... utilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g., gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy.
Recent learning-based multi-view stereo (MVS) methods show excellent performance with dense cameras and small depth ranges. However, non-learning based approaches still outperform for scenes with large depth ranges and sparser wide-baseline views, in part due to their PatchMatch optimization over pixelwise estimates of depth, normals, and visibility. In this paper, we propose an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizationsarXiv:2108.08943v1 fatcat:47vla23zoveifdaywzdokaxsoe
more »... h pixelwise estimates. To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals. We incorporate normal estimation by using dilated patch kernels, and propose a recurrent cost regularization that applies beyond frontal plane-sweep algorithms to our pixelwise depth/normal estimates. We evaluate our method on widely used MVS benchmarks, ETH3D and Tanks and Temples (TnT), and compare to other state of the art learning based MVS models. On ETH3D, our method outperforms other recent learning-based approaches and performs comparably on advanced TnT.
Lecture Notes in Computer Science
In this paper, we present an incremental structure from motion (SfM) algorithm that significantly outperforms existing algorithms when fiducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present. Our algorithm uses markers to limit potential incorrect image matches, change the order in which images are added to the reconstruction, and enforce new bundle adjustment constraints. To validate our algorithm, we introduce a newdoi:10.1007/978-3-030-01219-9_17 fatcat:ehdlw4e2mzf5nfjry54e7vlscu
more »... with 16 image collections of large indoor scenes with challenging characteristics (e.g., blank hallways, glass facades, brick walls) and with markers placed throughout. We show that our algorithm produces complete, accurate reconstructions on all 16 image collections, most of which cause other algorithms to fail. Further, by selectively masking fiducial markers, we show that the presence of even a small number of markers can improve the results of our algorithm.
Our goal is to recognize material categories using images and geometry information. In many applications, such as construction management, coarse geometry information is available. We investigate how 3D geometry (surface normals, camera intrinsic and extrinsic parameters) can be used with 2D features (texture and color) to improve material classification. We introduce a new dataset, GeoMat, which is the first to provide both image and geometry data in the form of: (i) training and testingdoi:10.1109/cvpr.2016.172 dblp:conf/cvpr/DeGolFH16 fatcat:4yvvqgkscbc3dokadnclvzl2ua
more »... s that were extracted at different scales and perspectives from real world examples of each material category, and (ii) a large scale construction site scene that includes 160 images and over 800,000 hand labeled 3D points. Our results show that using 2D and 3D features both jointly and independently to model materials improves classification accuracy across multiple scales and viewing directions for both material patches and images of a large scale construction site scene.
Storyboards offer designers a way to illustrate a narrative. Their creation can be enabled by tools supporting sketching or widget collections. As designers often incorporate previous ideas, we contribute the notion of blending the reappropriation of artifacts and their design tradeoffs with storyboarding. We present PIC-UP, a storyboarding tool supporting reappropriation, and report on two studies-a long-term investigation with novices and interviews with experts. We discuss how it may supportdoi:10.1145/1978942.1979171 dblp:conf/chi/WahidMDEH11 fatcat:wvablmw6mzhche2fgm3okhuwdm
more »... design thinking, tailor to different expertise levels, facilitate reappropriation during storyboarding, and assist with communication.
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera. Achieving high speed and maintaining high accuracy in a pose-and-scale estimator are often conflicting goals. To simultaneously achieve both, we exploit a priori knowledge about the solution space. We present gDLS*, a generalizedcamera-model pose-and-scale estimator thatdoi:10.1109/cvpr42600.2020.00228 dblp:conf/cvpr/FragosoD020 fatcat:qawzmx63encn5hvsvqnrsy2r4u
more »... tilizes rotation and scale priors. gDLS* allows an application to flexibly weigh the contribution of each prior, which is important since priors often come from noisy sensors. Compared to state-of-the-art generalized-pose-and-scale estimators (e.g. gDLS), our experiments on both synthetic and real data consistently demonstrate that gDLS* accelerates the estimation process and improves scale and pose accuracy.
The Ohio University game in 2012 was the first game played following the death of former longtime coach Joseph Paterno, with his successor Bill O'Brien serving as the new team coach. ...doi:10.30542/jcems.2021.04.01.03 fatcat:xnfpjhwl7nb5rknxjswsadr2xe
Grasp DeepGrasping ImageNet HandCam Key 0.0 % 11.8 % 20.0 % Pinch 21.8 % 10.6 % 20.0 % Power 47.0 % 47.5 % 20.0 % Three Jaw Chuck 28.0 % 19.2 % 20.0 % Tool 3.2 % 10.9 % 20.0 % DeGol ...doi:10.1109/embc.2016.7590732 pmid:28261002 pmcid:PMC5325038 fatcat:pbbxxnyr4rfele7wkfk5weomka
Journal of the American Pharmacists Association
., Manhat- an DeGoler, Harvey, Kansas City Kenball, John C., Lansing MARYLAND Sterling, tertown Alonzo L., Ches- MASSACHUSETTS Hackett, Joseph John, Bel- mont Kelleher, J. ... J., Palisades Park Yakubik, Joseph, Ford NEW MEXICO Butier, James P., Clovis Quisenberry, M. G., Albu- querque NEW YORK Allar, S. Sanford, Mt. Ver- non Cadman, M. ...
Hopkins Albert Hu Sonya Huffman Joseph Hughes Christian Hutter Thomas Hyclak Roberto Iacono Nicolae-Bogdan Ianc Mansor Ibrahim Tomohiko Inui Małgorzata Iwanicz- Drozdowska Ichiro Iwasaki Arun ... Natacha Gilson Blaise GNIMASSOUN Christophe Godlewski Stefan Goldbach Manuela Goretti Daryna Grechyna Louise Grogan Steve Loris Gui-Diby Thorvaldur Gylfason Volkan Hacioglu Miroljub Hadzic Degol ...doi:10.1057/s41294-019-00088-x fatcat:h44sqled65c35b2uqmf2aqd7uq
Partner, DeGol- yer & MacNaughton, 5625 Daniels Ave., Dallas, Texas DEGOOD, R. ... *DENN, WILLIAM JOSEPH, Computer, Geophysi- cal Service Inc., 5900 Lemmon Avenue, Dallas 9, Texas DENNING, WAYNE H., Supervisor, United Geo- Physical Co., 474 Fresno St., Morro Bay, California *DENNING, ...
Journal of Pharmaceutical Sciences
Olney Ave., 20, DeF ey Wash DeForge, Robert (53), DeFur, Ronald (51), 203°/2 Van Haute, Ind DeGoler, James (55), 2215 N DeGomar, Emanuel E., Jr St. Petersburg, Fla DeGroff, Norman (52), 202 E. ... ., East Detroit, DeCuir, Joseph (51), 2748 19, La DeDominicis, more, Md Dee, William H., i Deeb, Alexander E N.Y New Orleans Amelia C. (36), 2621 E. ...
., Railway and Harbor Works, Santos Degol- lado No. 24, Mexico, D. F., Mexico. SMOUSE, KENNETH JAMES (Jun. ’38), Junior Hydr. Engr., Water Resources Branch, U. S. ... SNOW, MARTIN JOSEPH (Jun. °38), Office and Fieldman, City Water Dept., City Hall (Res., 968 Medio Rd.), Calif. SNOWDEN, RUSSELL ELSTNER (Assoc. M. "18; M. ’22), Civ. and Cons. den, N. C. ...
SMYTH, RAPHAEL JOSEPH (M. ’21), Asst. Engr., "With Pres. of Borough of the Bronx, 1625 Undercliff Ave., New York, N. Y. ... ., Railway and Harbor Works, Santos Degol- lado No. 24, Mexico, D. F. Mexico. SMOUSE, KENNETH JAMES (Jun. ’38), Junior Hydr. Engr., Water Resources Branch, U. S. Geological Survey, 606 Post Office e. ...
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