"Computer vision is a core aspect of the intelligence that we're bringing to these systems."
"Deep learning can build powerful computer vision systems capable of solving extraordinarily complex tasks that maybe just 15 years ago would have not even been possible to solve."
"CNNs have touched so many different fields of computer vision ranging across robotics and medicine and many many other fields."
"It did amazingly well, and it sort of kick-started this whole deep learning revolution in computer vision."
"This is object detection... So, this is an object detection loss function."
"You want to penalize more errors on small bounding boxes rather than big bounding boxes."
"Training a CNN involves learning weights for filters and fully connected layers."
"CNNs have dominated image classification challenges, surpassing human accuracy."
"Identifying these objects and events is the job of the computer vision system." - Bruce Tannenbaum
"Computer vision system toolbox is our key product for computer vision with MATLAB." - Bruce Tannenbaum
"We are going to focus on object detection and tracking." - Bruce Tannenbaum
"We've supported object detection for many years with our tools and image processing." - Bruce Tannenbaum
"DeepStack AI computer vision analyzes each event."
"Computer vision is interdisciplinary, touching engineering, physics, biology, psychology, computer science, and mathematics."
"Exciting year in computer vision: celebrating its 50th anniversary."
"It was a massive tectonic change in computer vision."
"All of the things that you're seeing here such as the road boundaries, the lane lines, vehicles including their position, orientation, velocity, all of it is produced by running algorithms and neural networks on our on-board computer."
"The AI use computer vision to learn to recognize and consistently hit pitches while also finding an optimal pitching strategy."
"...the significance of the ViT architecture is not just within computer vision because, for the first time, it demonstrated that the Transformer architecture can be a unified architecture for many problems."
"Vision Transformer (ViT) applies Transformer architecture to 2D image processing, demonstrating its generality across different problems."
"Common applications of computer vision: image classification, object detection, semantic segmentation, image analysis, phase detection, and recognition, optical character recognition."
"If you want to stay on the bleeding edge of computer vision, sit down, relax, and let me show you how to train YOLO V8 object detection model on custom data set."
"This sort of technology is really interesting and it's a really cutting edge area of computer vision."
"Welcome to the ECCV 2020 tutorial on normalizing flows, convertible neural networks in computer vision."
"We're seeing in biology and medicine computer vision being used to diagnose cancers."
"Deep learning has really taken computer vision systems by storm because of their ability to learn directly from raw pixels and directly from data."
"Computer vision is when we use machine learning neural networks to gain high level understanding of digital images or videos."
"Template matching is a technique we can use to detect images inside of another image without training a neural network."
"We've seen rapid progress in deep learning, especially in computer vision."
"The single shot refers to the fact that we're going to make a single forward pass through the network to perform inference and yet detect multiple objects within an image."
"Self-supervised learning is rapidly closing the gap with supervised methods on large computer vision benchmarks."
"Computer vision is the study of building artificial systems that can process, perceive, and otherwise reason about visual data."
"The role of computer vision in our modern society will just continue getting more and more important."
"This is the most important and exciting research topic that we can be studying right now."
"Computer vision technology really has massive and massive potential to improve all of our lives."
"Computer vision as a whole has a massive trek, massive ability, and potential to continue leading to massive improvements in all of our day-to-day lives."
"Easily create your own machine learning model from scratch using tiny YOLO and open CV."
"Computer vision is, these days, dominated by data driven approaches by machine learning."
"This course is ideal for beginners as well as for more advanced developers as it contains very valuable information and insights I gathered from years of experience as a computer vision engineer."
"With recent advancements in computer vision, you can actually automate most of that process."
"The first wave of AI that focused on computer vision and speech recognition has achieved superhuman capabilities."
"Computer vision is about a computer being able to understand visual data."
"It's amazing how it works, and you would have asked people in computer vision how long it's gonna take us before we can do this, they would have told you, you know, I don't know, 20 years. It happened really quickly."
"The vision Transformer was introduced in a very famous paper, 'An image is worth 16x16 words,' from Google research, Google brain."
"We're specialists in computer vision and machine learning, and we put these skills in the sports industry."
"...this concept of supervised learning lets us solve a lot of different types of computer vision tasks."
"Meta learning has been used in a variety of computer vision applications such as image recognition, modeling the motion and the pose of humans."
"Computer vision is a field where computers identify, classify, and react to objects visually."
"OpenCV, also known as Open Source Computer Vision, is the largest computer vision library."
"Simple CV is a beginner-friendly open source framework for building computer vision applications."
"Such max pool then the result, this 1024 features would be something that should ideally represent the global features of the shape."
"This is just one example of the many cool applications you can create using computer vision and machine learning."
"It's an exciting time to be involved in computer vision and living at the forefront of what is possible."
"Computer vision actually solves the problem of what is where in the images."
"We're not just solving the computer vision problem at autopilot, we're also applying the state-of-the-art in language modeling and machine learning more generally."
"OpenCV is like the thing for vision stuff on robots."
"We use computer vision which is a form of AI to understand what's happening and to create all sorts of interactive experience."
"Residual networks become very efficient, able to achieve very low errors on ImageNet with very few amounts of floating-point operations."
"Residual networks became a baseline that is widely used even still to this day for a wide variety of different tasks in computer vision."
"We are getting pretty good segmentation."
"Want to learn how to deploy computer vision apps? Let's do it."
"This is a revolutionary result that has really changed the whole field of computer vision."
"The point of this video is not just to show you the corner detection... it's to give you kind of an introduction to some of the interesting algorithms that OpenCV has built in."
"We all know convnets are a breakthrough in computer vision."
"We'll talk about a recap of what we've learned this semester as well as some of my thoughts about where I think computer vision will be going in the future."
"Edge detection is a useful technique which normally forms part of a broader computer vision pipeline."
"We're trying to eliminate the shadow because OpenCV will think that the shadow is the object and it'll mess up your stuff."
"This is actually a really interesting time to be studying computer vision, and we have a lot going on."
"OpenCV allows you to see your object detection in real time."
"Our world actually has a third dimension, so we should build computer vision systems that know how to make predictions in three dimensions."
"...this really helps to show that this idea of differentiable rendering can be really powerful in relaxing the kinds of supervision that we need for training 3D computer vision shape prediction systems."
"The goal of video object segmentation is to generate accurate and temporally consistent pixel masks for all the objects in a video sequence."
"Image classification is a type of computer vision problem."
"Computer vision is kind of the poster child for what deep learning has accomplished."
"Clearly, it works really well in computer vision and image classification."
"Convolutional layers are used for computer vision most commonly."
"We are at the tipping point of that same technology coming to computer vision and robotics."
"Deep learning is currently the best way of making computers perceive the world."
"Computer vision is all about computational methods for analyzing and understanding images."
"Graphs are also used in computer vision, for example, in image segmentation."
"OpenCV is the CV stands for computer vision, and the purpose of OpenCV is to provide this huge library of functions that are useful for image recognition."
"This is one of the most influential papers in computer vision."
"Object detection has been a great topic of research and development in the computer vision community for decades."
"Panoptic segmentation is basically a combination of both: instant segmentation and semantic segmentation."
"Object detection is a computer vision task that analyzes an input image and returns a list of known objects and the location of the objects in the image."
"Object detection is a very powerful computer vision task and it supports a lot of variety of use cases."
"We'll develop an algorithm for optical flow."
"Hope to share with you some thoughts on how you can build computer vision systems maybe in minutes."
"Getting computers to see, it's amazing."
"OpenCV is a really full-featured package that allows you to work with images but also video."
"It's really important when it comes to seeing the outputs of your computer vision work."
"Object detection is a computer vision technique for locating instances of objects in images or videos."
"Computers can see better than humans; their vision is literally better."
"Finding edges is so important that this is like all people did in computer vision for like 40 years."
"Non-maximum suppression is important for edge detection but it's also important for a lot of other computer vision applications."
"The holy grail in computer vision is we want to train a system that achieves human level scene understanding."
"The success story for representation learning in computer vision has been the ImageNet moment."
"Predicting rotations of an image works empirically very well."
"The optimal edge detector must minimize the probability of false positives as well as false negatives."
"We're going to learn how to process mouse clicks or mouse events in OpenCV."
"OpenCV is an open computer vision library which allows you to work with your webcam, allows you to work with images really well."
"The reason that graph cuts became so popular in image processing and computer vision is that there are very efficient ways of solving graph cut problems on grid-based graphs."
"OpenCV is designed for machine vision type of applications."
"SIFT stands for Scale-Invariant Feature Transform and it is both a detector and a descriptor."
"This lecture is kind of like maybe one step towards what I would call image understanding or computer vision."
"You can train these networks to predict the depths of the scene from just one single image."
"I am a big believer in computer vision, and I do not think it's too far away."
"Optical flow is a huge area of research in computer vision."
"Computer vision is a super interesting field; it has some amazing applications in industrial space, safekeeping, or even in wildlife preservation."
"The interesting thing is that the fundamental matrix is so crucial, so important in computer vision."
"It gives you high-level, on-device solutions to computer vision problems."
"This is all fully autonomous using computer vision to understand the world around it."
"Computer vision can be used in many different applications and it's changing the whole game."
"We're excited by OpenMV, it's a really great project for bringing this kind of computer vision stuff to the embedded world."
"Hello everyone and welcome to this section. I'm super excited to discuss a very important topic in computer vision."
"You are the future of computer vision."
"If we can solve computer vision with machine learning, I think we can pretty much solve everything."
"SIFT is really the one that you will come across the most often and is used pervasively in computer vision."
"SURF stands for Speeded Up Robust Features."
"The Vision API lets you label images, identify landmarks, logos, do OCR."
"Matting is an important problem, but what I do want to talk about is an optimization framework and algorithm that is really useful for general computer vision problems."
"The Harris corner detector... calculates the two-dimensional plot at every location in the image and calculates eigenvalues, and if you have two large eigenvalues, you have a corner."
"The problem of reconstructing the location of points in 3D and the motion of the camera is often called structure and motion."
"The optical flow constraint is a very important equation in the computer vision field."
"It's often used for tasks such as email filtering, detection of network issues, and computer vision."
"It nicely selects indeed a lot of points that are corners, which is why it's often referred to as Corner detector."
"The OpenCV library provides an open source and easy-to-use framework for implementing computer vision projects."
"State of the art neural networks can learn a very complex function like one that takes the pixel values of a photo and outputs the objects in the photo."
"The objective of large-scale 3D reconstruction is to find out the motions of the cameras and recover the 3D structures from image correspondences."
"This is the way that a lot of computer vision is done today."
"Computer vision has many fascinating challenges, and one of them may be the most classical problem in computer vision."
"Using computer vision technology, tracking every player on the field."
"Welcome to the next class on variational methods for computer vision."
"Computer vision is just exactly the same thing but going in the other direction."
"Computer vision is a lot harder than that and every time you think of a rule there is always an exception."
"Making computer vision accessible for anyone."
"I'm really excited to see what's going to happen in the next 12 months with transformers in the computer vision space."
"We are now at a place where computers can see."
"Intersection over Union... you compute the area of the overlap between these two and you divide it by the total area."