MIRU2016 invited talk - Recovering Transparent Shape from Time-of-Flight Distortion (CVPR 2016)
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Talk on MIRU2016 Hamamatsu, Japan. The paper is published in the proceedings of Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas.
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Lidar (also written LIDAR, LiDAR or LADAR) is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light. Although thought by some to be an acronym of Light Detection And Ranging,[1] the term lidar was actually created as a portmanteau of "light" and "radar".[2][3] Lidar is popularly used as a technology to make high-resolution maps, with applications in geodesy, geomatics, archaeology, geography, geology, geomorphology, seismology, forestry, remote sensing, atmospheric physics,[4] airborne laser swath mapping (ALSM), laser altimetry, and contour mapping.
（DL輪読）Matching Networks for One Shot Learning
1. Matching Networks is a neural network architecture proposed by DeepMind for one-shot learning.
2. The network learns to classify novel examples by comparing them to a small support set of examples, using an attention mechanism to focus on the most relevant support examples.
3. The network is trained using a meta-learning approach, where it learns to learn from small support sets to classify novel examples from classes not seen during training.
Diamond West 3D Laser Scanning Presentation
3D Survey and Visualization
Diamond West utilizes LIDAR (Light Detection and Ranging) 3D scanning technology to digitally reconstruct an existing environment in a 3D digital model with sub-centimeter accuracy.
Digitally scanned data can be modeled in 3D or converted to 2D drawings (plan view, elevation cross section, and topography) for export to any standard CADD platform. Scanned information is linked together to form a comprehensive digital image.
Applications include:
Architectural as-builts, historic preservation/archive, structural steel mapping/cataloging, conceptual design and interference checking, fabrication and construction inspection, manufacturing and reverse engineering, topographic mapping, accessibility renovations, civil traffic and utility planning, as-builts for plant facilities, movie industry; the list goes on. If you can see it, you can scan it.
Advantage to using LIDAR 3D Scanning Technology:
Reduces field work, reduces risk of liability for field crews, competitive cost to conventional surveying, dramatically increases available information without multiple site visits, creates (sub-centimeter) accurate 3D models, remote sensing/minimizes need for access to structures.
High-definition 3D scanning provides: more accurate base mapping, detailed information along structural facades from the ground to the sky, documentation of not only surface conditions but also building conditions. Due to the potential for design and construction abutting building façades, the sub-centimeter accuracy will be a vital design and construction quality control tool.
The scanned 3D model can also be used in the future as a visualization tool by generating “fly-by” animated movies and still frames from any requested vantage point.
In summary, the use of sub-centimeter accurate 3D survey data will provide the design team with the accuracy needed to ensure proper quality control for design and construction activities. This technology will also provide the community and decision-makers with an extremely useful visual aid tool in evaluating the proposed design against existing conditions.
Time of Flight Cameras - Refael Whyte
This document discusses time-of-flight cameras and range imaging. It explains that time-of-flight cameras work by illuminating a scene with amplitude modulated light and measuring the phase difference between the transmitted and reflected light, which encodes distance information. It describes the correlation process used to measure distance at each pixel and discusses sources of error such as multipath interference and temperature drift. Real data examples from a 120x160 sensor are shown. Applications discussed include uses in the Kinect, 3D scanning, and potential future uses in mobile phones.
（DL hacks輪読）Bayesian Neural Network
The document discusses Bayesian neural networks and related topics. It covers Bayesian neural networks, stochastic neural networks, variational autoencoders, and modeling prediction uncertainty in neural networks. Key points include using Bayesian techniques like MCMC and variational inference to place distributions over the weights of neural networks, modeling both model parameters and predictions as distributions, and how this allows capturing uncertainty in the network's predictions.
Track 4 session 1 - st dev con 2016 - body area network and sensor synchron...
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深層リカレントニューラルネットワークを用いた日本語述語項構造解析
既存の日本語述語項構造解析では，形態素解析や構文解析を行い，それらの情報に基づいて各述語の項を予測していた．しかし，パイプライン処理に起因する誤り伝搬や，人手による素性設計コストの上昇などが問題点となっている．本研究では，深層リカレントニューラルネットワークを利用し，単語などの表層情報のみを用いて述語項構造解析を行う手法を提案する. また，複数の述語を同時に考慮して述語構造解析の精度を向上させるために，述語ごとのモデルをグリッド上に連結した深層リカレントグリッドモデルを提案する．NAIST テキストコーパスを用いた実験の結果，これまでに報告されている最先端の日本語述語項構造解析器の精度を上回ることを確認した．
Sensors for Biometry and Recognition - 2016 Report by Yole Developpement
In a global biometric hardware market worth over $4B, traditional fingerprint/palm sensors still monopolize 95% of the market, but face and iris sensors lie in wait.
Fingerprint technology impressively dominates the market - but changes are expected
Due to historical reasons, like the criminal fingerprint database established by the FBI with ink-based techniques, fingerprint sensing is the most common biometric technology currently used, by far. We estimate that the annual revenues generated by fingerprint-based solutions are currently $4.25B, representing 95% of the hardware market. Fingerprint sensing dominates technologies like iris, face, palm or voice recognition, because it meets almost all the requirements of a “perfect” biometric recognition technology. It is robust, stable and repeatable, time-invariant, difficult to spoof, has a distinctive meaning, is "unique" amongst a population, accessible, easy to use and acceptably non-intrusive. All other biometric technologies do not yet fulfill those requirements as specifically as fingerprint technology. Hardware revenues generated by the other biometric technologies are relatively low, estimated at $250M, mostly from iris and face recognition. This report identifies the players in the biometric hardware market, and provides technology, market and trend evolution insights. The fingerprint market has experienced an incredible volume increase in the consumer market with the adoption of the active capacitance detection on an increasing number of smartphones to answer the demand of online identification, mobile payment and unlocking applications. Diverse technologies, such as optical, thermal, and Piezoelectric Micromachined Ultrasonic Transducers (PMUT), are also trying to penetrate the consumer market, but are still very limited. The industrial and homeland/security markets are still widely using optical technology. Other biometric solutions like iris, face or voice recognition have been introduced but with a limited impact. Their performance hasn’t yet reached the requirements with regards to cost, reliability, false rejection rate and false acceptance rate to significantly penetrate either the consumer, industrial or homeland/security markets....
More information on that report: http://www.i-micronews.com/report/product/sensors-for-biometry-and-recognition.html
SlideShare moves to HTML5
The document discusses new capabilities of HTML5 including being 30% faster on mobile, supporting new web apps built on HTML5 that run on all browsers and devices. It notes that HTML5 runs faster on all browsers and devices, supports Apple iOS, and makes copying and pasting in the browser easier. It also mentions that HTML5 provides mobile distribution and works on all smart devices, and that 65% of mobile workers use tablets with 27% using them for work purposes.
NIPS読み会2013: One-shot learning by inverting a compositional causal process
This document summarizes research on one-shot learning using a hierarchical Bayesian program learning (HBPL) model. The key points are:
- The HBPL model achieved human-level performance on one-shot classification and generation tasks, outperforming other deep learning models.
- It used an Omniglot dataset of handwritten characters from different alphabets to learn concepts from a single example, as well as produce new examples.
- The model learns "motor programs" that represent common patterns in how symbols are drawn, from a library of primitive strokes. This allows it to generalize concepts from limited data.
Multi-camera Time-of-Flight Systems | SIGGRAPH 2016
This document describes a multi-camera time-of-flight imaging system that uses multiple synchronized cameras and light sources. It allows control over modulation signals to capture dynamic scenes and extract depth, velocity and non-line-of-sight motion information. The system architecture uses a direct digital synthesis chip to generate programmable modulation signals, synchronized signal conditioning circuits and a real-time controller to coordinate image capture across multiple time-of-flight cameras. It aims to enable applications like phased array imaging, simultaneous Doppler velocity capture and detecting motion behind scattering media.
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For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-benosman
For more information about embedded vision, please visit:
http://www.embedded-vision.com
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http://mesh.brown.edu/byo3d/
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DGPS
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(
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−
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∼
0.9
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19
,
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000
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Ca-rich population. Although such an object is too red for any low-
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
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) with
Λ
CDM. Therefore unlike low-
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
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truly diverge from their low-
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counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.Recently uploaded (20)
8.Isolation of pure cultures and preservation of cultures.pdf
8.Isolation of pure cultures and preservation of cultures.pdf
11.1 Role of physical biological in deterioration of grains.pdf
11.1 Role of physical biological in deterioration of grains.pdf
Immersive Learning That Works: Research Grounding and Paths Forward
Immersive Learning That Works: Research Grounding and Paths Forward
Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...
Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...
Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...
Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...
Microbiology of Central Nervous System INFECTIONS.pdf
Microbiology of Central Nervous System INFECTIONS.pdf
Farming systems analysis: what have we learnt?.pptx
Farming systems analysis: what have we learnt?.pptx
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
Compexometric titration/Chelatorphy titration/chelating titration
Compexometric titration/Chelatorphy titration/chelating titration
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...
MIRU2016 invited talk - Recovering Transparent Shape from Time-of-Flight Distortion (CVPR 2016)
- 1. Recovering Transparent Shape from Time-of-Flight Distortion (CVPR2016) 1 K. Tanaka Y. Mukaigawa H. Kubo Y. Matsushita Y. Yagi
- 2. Transparent Objects • Invisible, but distorted background can be seen. • 3D reconstruction of transparent material is challenging. 3 Sensor Estimated point by triangulation BackgroundReference Distorted
- 3. Time-of-Flight (ToF) Camera • Depth sensor based on time delay of light • Kinect v2, Project Tango, etc. 4 time Light signal Observation 𝑡Δ 𝑑 = 𝑐𝑡Δ 2 (speed of light x time delay)
- 4. Time of Flight Distortion • Speed of light slows down depending on refractive index. • Depth becomes longer ( = ToF Distortion). • We use this distortion for transparent shape recovery. 5 c c c
- 5. Contributions 6 1. ToF distortion can be used for transparent shape recovery. 2. Easy multi-path mitigation using retroreflective sheet. c c c
- 6. Problem Setting Input • Known refractive index • 1 distorted ToF depth • 2 references (3D points) 7 r f b r t v v r s Output • 3D points of both surfaces • Surface normals
- 7. Parameters and Candidate Shapes • Candidate shapes • Front surface is on camera ray at distance 𝑡 • Back surface is on reference ray at distance 𝑠 • Many candidates. (2 degree of freedom) 8 ToF camera 𝑡 Glass object Display or known pattern 𝑠
- 8. Candidate Shape using ToF Distortion • Candidate shapes • Front surface is on camera ray at distance 𝑡 • Back surface is on reference ray at distance 𝑠 • such that 𝑠 + 𝑡 + 𝜂 = 𝑙 𝑇𝑜𝐹 : ToF distortion • One degree of freedom 9 ToF camera 𝑡 Glass object Display or known pattern 𝑠
- 9. Surface Normal Consistency • Surface normal is unique Refractive normal Geometric normal • They should coincide. 10 ToF camera 𝑡 Glass object Display or known pattern 𝑠 𝑛 = sin 𝜃1 sin 𝜃2 Refractive normal camera ray Geometric normal
- 10. Real world experiment setup • modified Kinect v2 and LCD panel 11 Kinect v2 (IR Lens changed) LCD panel Linear stage Target object
- 11. Results and evaluations • Target materials and estimated results • Evaluation • Fit estimated points to ground-truth CAD model by ICP 12 Cube Wedge prism Schmidt prism Object Mean error Std. dev. Cube 0.188 mm 0.458 mm Wedge 0.226 mm 1.137 mm Schmidt 0.381 mm 1.398 mm
- 12. Summary Input • 1 distorted ToF depth • 2 references (3D points) • Known refractive index 13 r f b r t v v r s Output • 3D points of both surfaces • Surface normals
- 13. Time-of-Flight as alternative imager • Light-in-flight [Gkioulekas+2015] • Parameter tunable ToF camera (Texas instruments) 14
- 14. Imaging, Analyzing using ToF Camera • Recently Emerging Topic [Heide+2013], [Kadambi+2013], [Naik+2013], [Godbaz+2013], [Freedman+2014], [Lin+2014], [O’Toole+2014], [Gupta+2015], [Heide+2015], [Xiao+2015], [Kadambi+2015], [Peters+2015], [Tadano+2015], and more! • CVPR 2016 • 1 oral, 2 posters (including ours) [Kadambi et al.], [Su et al.] • SIGGRAPH 2016 • 2 technical papers. [Shrestha et al.], [Kadambi et al.] 15 We will continue working on ToF camera