“I would be happy to help look for the shooter but vision isn’t covered under my healthcare plan,” one comment read on Instagram.

we impute three-dimensionality onto the world as we walk around we use things like Parallax and assumptions about what thing is bigger than what other thing and so on

For approximate reference, sRGB/Rec709 cover roughly 35% of visible colour. DCI-P3/Display-P3/AdobeRGB cover roughly 50%, and Rec2020/2100 covers roughly 75%. So even "HDR" standards don't cover everything a human being is capable of seeing.

TOKEN MERGING: YOUR VIT BUT FASTER

(Weight W1) (Rope Rp) (Rope Rq) (Pulley Pa) (hangs W1 from Rp) (pulley-system Rp Pa Rq) (Weight W2) (hangs W2 from Rq) (Rope Rx) (Pulley Pb) (Rope Ry) (Pulley Pc) (Rope Rz) (Rope Rt) (Rope Rs) (Ceiling c) (hangs Pa from Rx) (pulley-system Rx Pb Ry) (pulley-system Ry Pc Rz) (hangs Pb from Rt) (hangs Rt from c) (hangs Rx from c) (hangs Rs from Pc) (hangs W2 from Rs) (value W1 1) (b) P1. P2. P3. P4. .. Single-string support. (weight < Wx>) (rope <Ry >) (value <Wx> <n>) (hangs <Wx> <Ry>) -(hangs <Wx> <Rx>) - (value <Ry> <W-number>) Ropes over pulley. (pulley <P>) (rope <R1>) (rope <R2>) (pulley-system <R1 > <P> <R2>) (value <R1> <nl>) - (value <R2> <nl>) Rope hangs from or supports pulley. (pulley <R1>) (rope <R1>) (rope R2>) (pulley-system <R1> <P> <R2>) { (hangs <R3> from <P>) or (hangs <P> from <R3>) } (value <R1> <nl>) (value <R2> <n2>) - (value <R3> <nl + <n2>) Weight and multiple supporting ropes. (weight <W1 >) (rope <R1 >) (rope R2>) (hangs <W1> <Rl>) (hangs <W1> <R2>) -(hangs <W1> <R3>) (value <R1> <nl>) (value <R2> <n2>) - (value <W1> <nl> + <n2>) P2. Ropes over pulley. If a pulley system < P> has two ropes < RI > and < R2> over it, and the value (tension) associated with < RZ > is < nl > , then < nl > is also the value associated with rope < RZ > . P3. Rope hangs from or supports pulley. If there is a pulley system with ropes < RZ > and < R2> over it, and the pulley system hangs from a rope < R3 > , and c R1> and < R2 > have the values (tensions) < nl > and < n2 > associated with them, then the value (tension) associated with < R3 > is the sum of < nl > plus <n2>.

to the bottom of the next image, about a fifth of a second later, like that. And they're getting faster and faster each time, and if I stack these guys up, then we see the differences; the increase in the speed is constant. And they say, "Oh, yeah. Constant acceleration. And how shall we

price, specs, release date and more

It also includes some advice that might be obvious to professional programmers, but that might not be obvious to researchers or others who are just starting out

él también viene al mundo equipadocon la capacidad de reconocer a sus congé-neres. La cara de la madre jugará un rolfundamental

When a person uses a tool to achieve some goal, they quickly learn how towork around the short-comings of the tool in order to get their job done. As a result, the personbecomes desensitized to problems in the tool.

park a crucial question that any leader who has to make a difficult, often unpopular, decision must ask themself: What must I do in this situation, given my position?

Meaningful change, in contrast, supports a culture of integrity that is driven by formative institutional principles and that provides a structure for future growth and change.

When we speak, we are afraid our words will not be heard or welcomed. But when we are silent, we are still afraid. So it is better to speak …”

Building and sharing an app should be as easy as creating and sharing a video.

No longer does anyone simply type in a few HTML tags into edit.com and FTP it onto the internet.

and if you want software that's any more niche than that

Dreams or vision quests: among Iroquoian-speaking peoplesin the sixteenth and seventeenth centuries it was consideredextremely important literally to realize one’s dreams. ManyEuropean observers marvelled at how Indians would be willingto travel for days to bring back some object, trophy, crystal oreven an animal like a dog that they had dreamed of acquiring.Anyone who dreamed about a neighbour or relative’spossession (a kettle, ornament, mask and so on) couldnormally demand it; as a result, such objects would oftengradually travel some way from town to town. On the GreatPlains, decisions to travel long distances in search of rare orexotic items could form part of vision quests.34

This is the cybercrud problem: advice and creation of systems, supposedlybased on technical requirements, whose categories and rigidities are unnecessary.In the worst cases they are not only unnecessary but wrong.

After the conference dinner, Theodor Nelson of The Nelson Organization, Inc.,described a vision of what the computer's use in instruction might become, if onlywe could see beyond the "trivial horizons" of most computer peop:.e

Cada aspecto de la visión, desde las proteínas de opsina hasta los ojos y las formas en que sirven al comportamiento animal, es increíblemente diverso. Solo con una perspectiva evolutiva se puede comprender y apreciar plenamente esta diversidad. En esta revisión, describo y explico la diversidad en cada nivel y trato de transmitir una comprensión de cómo el origen de la primera ops en hace unos 800 millones de años pudo iniciar la avalancha que produjo la asombrosa diversidad de ojos y visiones que vemos hoy. A pesar de la diversidad, muchos tipos de fotorreceptores, ojos y roles visuales han evolucionado varias veces de forma independiente en diferentes animales, revelando un patrón de evolución ocular estrictamente guiado por limitaciones funcionales e impulsado por la evolución de comportamientos gradualmente más exigentes.

Vision Statement

Avoid 'global magic' or things that are defined outside of scope where they are not visible.

OpenCV Android Application Programming with OpenCV Mastering OpenCV with Practical Computer Vision Projects Practical OpenCV Learning OpenCV

What I’d like more of is a social web that sits between these two extremes, something with a small town feel. So you can see people are around, and you can give directions and a friendly nod, but there’s no need to stop and chat, and it’s not in your face. It’s what I’ve talked about before as social peripheral vision (that post is about why it should be build into the OS).

It was clear no one was interested in what I was working towards.

The idea of a web browser being something we can comprehend, of a web page being something that more people can make, feels exciting to me.

This follows the original vision of the Web as a space for collaboration and not just a one-way publishing medium.

À l’inverse, ces artefacts, en privant la mémoire interne d’informations à synthétiser, ne risquent-ils pas de l’affaiblir et, à terme, de porter atteinte à notre façon de penser et à notre libre arbitre ?

Réfléchir aux conséquences de l’externalisation de la mémoire humaine ne date pas d’hier.

Installation in Windows Compatibility: > OpenCV 2.0 Author: Bernát Gábor You will learn how to setup OpenCV in your Windows Operating System!

Here you can read tutorials about how to set up your computer to work with the OpenCV library. Additionally you can find very basic sample source code to introduce you to the world of the OpenCV. Installation in Linux Compatibility: > OpenCV 2.0

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.

Franchise products are governed too much by economic logic and not enough by artistic vision

The main purpose of the Discovery IN is to provide interfaces and other user-facing services for data discovery across disciplines. We explore new and innovative ways of enabling discovery, including visualizations, recommender systems, semantics, content mining, annotation, and responsible metrics. We apply user involvement and participatory design to increase usability and usefulness of the solutions. We go beyond academia, involving users from all stakeholders of research data. We create FAIR and open infrastructures, following the FAIR principles complemented by the principles of open source, open data, and open content, thus enabling reuse of interfaces and user-facing services and continued innovation. Our main objectives are:

R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms

malignant eye,

I saw the movement of content across media as an enhancement of the creative process. He saw it as a distraction or corruption.

When irrationalism, as the counterplay of rationalism, talks about the things to which rationalism is blind, it does so only with a squint.

By the year 2020 Estonian Smart City Cluster is internationally known as the leading developer and exporter of smart city solutions that are based on ICT and other technologies.

if we ever find the translation is dominant in a direction other than forward, we simply ignore that motion.

Most Computer Vision algorithms are not complete without a few heuristics thrown in

RANSAC. It is an iterative algorithm. At every iteration, it randomly samples five points from out set of correspondences, estimates the Essential Matrix, and then checks if the other points are inliers when using this essential matrix.

T his step compensates for this lens distortion.

For every pair of images, we need to find the rotation matrix RRR and the translation vector ttt, which describes the motion of the vehicle between the two frames.

An efficient solution to the five-point relative pose problem

our job is to construct a 6-DOF trajectory

Instead, we define the goal of the growth team as accelerating the realization of LinkedIn’s vision, which is to create economic opportunity for every member of the global workforce. Keeping this vision top of mind led us to set the right growth objectives and priorities.

“Rap Genius is going be the fabric of the internet,” co-founder Mahbod Moghadam said in 2014 “Rap Genius is going be the fabric of the internet,” co-founder Mahbod Moghadam said in 2014. “We’re going to have annotations on other sites, so every other site in the world like the Wall Street Journal and the New York Times are going be Genius-powered and they’re going to have our annotations on them. And then the Genius platform will take over the internet; everyone’s most important statistic that they have in life is their Genius IQ.”

future

Vision Zero

Estamos comprometidos con el acceso libre y equitativo a la información y al conocimiento

Of another parcel of 153 acres near the former, and including a considerable eminence very favorable for the erection of a future observatory.

As well might it be urged that the wild & uncultivated tree, hitherto yielding sour & bitter fruit only, can never be made to yield better: yet we know that the grafting art implants a new tree on the savage stock, producing what is most estimable both in kind & degree.

Vision and Mission Statements -- a Roadmap of Where You Want to Go and How to Get There

Duncan (16) found that less than 5% of the figures in a typical textbook contain data. It is no wonder that students using a traditional and passive textbook do not know how to support their answers with data.

However, if reading the book is a key component of class time and tests, students will use the text to help them construct their own understanding of the material.

Second, students must come to class prepared for classroom activities, which also facilitates studying for exams as the semester progresses.

First, student work outside of class needs to be intentionally and effectively structured.

he admission of enlargement to any degree to which the institution may extend in future times.

Who is Mistaken?Benjamin EysenbachMITbce@mit.eduCarl VondrickMITvondrick@mit.eduAntonio TorralbaMITtorralba@csail.mit.eduFigure 1: Can you determine who has a false belief about this scene? In this paper, we study how to recognize when a person in a short sequence is mistaken. Above, the woman is mistaken about the chair being pulled away from her.TimeFigure 1:Can you determine who believes something incorrectly in this scene?In this paper, we study how to recognizewhen a person in a scene is mistaken. Above, the woman is mistaken about the chair being pulled away from her in the thirdframe, causing her to fall down. Thered arrowindicates false belief. We introduce a new dataset of abstract scenes to studywhen people have false beliefs. We propose approaches to learn to recognizewhois mistaken andwhenthey are mistaken.AbstractRecognizing when people have false beliefs is crucial forunderstanding their actions. We introduce the novel prob-lem of identifying when people in abstract scenes have in-correct beliefs. We present a dataset of scenes, each visuallydepicting an 8-frame story in which a character has a mis-taken belief. We then create a representation of characters’beliefs for two tasks in human action understanding: pre-dicting who is mistaken, and when they are mistaken. Ex-periments suggest that our method for identifying mistakencharacters performs better on these tasks than simple base-lines. Diagnostics on our model suggest it learns importantcues for recognizing mistaken beliefs, such as gaze. We be-lieve models of people’s beliefs will have many

The Tesla accident in May, researchers say, was not a failure of computer vision. But it underscored the limitations of the science in applications like driverless cars despite remarkable progress in recent years, fueled by digital data, computer firepower and software inspired by the human brain.

Bird eyes have had eons longer to optimize. Along with their higher cone count, they achieve a far more regular spacing of the cells.

Bird eyes have had eons longer to optimize. Along with their higher cone count, they achieve a far more regular spacing of the cells.

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

Unsupervised Learning of 3D Structure from Images Authors: Danilo Jimenez Rezende, S. M. Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, Nicolas Heess (Submitted on 3 Jul 2016) Abstract: A key goal of computer vision is to recover the underlying 3D structure from 2D observations of the world. In this paper we learn strong deep generative models of 3D structures, and recover these structures from 3D and 2D images via probabilistic inference. We demonstrate high-quality samples and report log-likelihoods on several datasets, including ShapeNet [2], and establish the first benchmarks in the literature. We also show how these models and their inference networks can be trained end-to-end from 2D images. This demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner.

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning as standard practice for improved new task performance.

Low-shot visual object recognition

The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies

Beyond Sharing Weights for Deep Domain Adaptation

Deep Convolutional Inverse Graphics Network

Dynamic Filter Networks

Atl=xtifl= 0MAXPOOL(RELU(CONV(Etl1)))l >0(1)^Atl=RELU(CONV(Rtl))(2)Etl= [RELU(Atl^Atl);RELU(^AtlAtl)](3)Rtl=CONVLSTM(Et1l;Rt1l;Rtl+1)(4)

Unsupervised convolutional neural networks for motion estimation

Adversarial Feature Learning