"The lighter spots of the image will actually emit light, and the darker parts... basically won't emit very much light."
"Socratic, this one's actually by Google, and you can take pictures of things, and it will analyze the pictures and help you solve problems from the pictures."
"This is what we want to train on... we converted it into grayscale and we use that as a list of numbers that are related to this image."
"It's not high resolution enough for me to figure out if it's composited in."
"This is without any epatha T so this is the original image."
"This is as close as anyone has got to gazing upon the raw pixels that comprise the image since its creation in 1985."
"Pixel difference is measured in RGB brightness, so it might not be surprising that the first component of PCA is just the shading of the image."
"Why would one photo look so different than another?"
"...so what we're seeing is just this motion that's in the plane of Earth's orbit and we're just looking for a shift of a few pixels in that one direction."
"So rather than looking at a natural color image, it's actually always very efficient to look at the corresponding color composite image."
"I will use a convolutional neural network...it's a special kind of neural network that is especially good at analyzing images."
"This script uses either Landsat 8 or Sentinel-2 imagery to calculate the normalized burn ratio for pre and post-fire images."
"Image/video analysis: MLOps is used in applications like image classification, object detection, and facial recognition for security systems and medical imaging."
"Looking at this image, you can look at a particular color and it tells you something about what material the actual point in the image is made up of."
"They sometimes show pictures of celebrities and they put them into different categories of warm or cool, this way they discuss the picture itself, they're not discussing the person."
"CLIP is incredibly good at actually capturing the meaning across the entire image."
"Most digital image analysis works on these underlying numbers to do mathematical calculations that will enhance or suppress certain features in your image."
"You can't deny the fact that the image itself is unsettling."
"I'm fond of the image analysis focused interest group of the RMS."
"Photoshop does an amazing job at analyzing the images and figuring it all out."
"The shape of this array tells me the number of rows, the number of columns, and then the number of channels in my image."
"This algorithm did a pretty good job of selecting our foreground object from the background."
"The predictions are all correct, and the reconstructions are pretty good, they're pretty close to the original images."
"That is really cool thing what we got from this image."
"And always, always look at the image."
"As you go up one level, it's learning kind of slightly more complicated variations."
"Computer vision actually solves the problem of what is where in the images."
"I'm seeing a shift from large language models to large vision models... a lot of progress will not just be in generating images, it'll be in analyzing images so computers can see much better."
"This image, I believe, is a nine with 98% confidence."
"Doing some image processing functions like edge detection, image deconvolution, and finally look at one of the classic use cases from microscopy which is scratch assay or wound healing analysis."
"Let's do edge detection and deconvolution, for example."
"Canny is not just one operation; it does noise reduction, gradient calculations, edge tracking, among other things."
"Let's look at a real-world scenario, let's start from scratch, and this real-world scenario is scratch assay analysis or wound healing assay."
"Image classification is a type of computer vision problem."
"Convolutional neural networks can be quite powerful and quite popular when it comes to trying to analyze images."
"Now we should loop through the entire image and get a value for each pixel."
"As long as the image structures that you're interested in have a clear relation in terms of image intensities, you can still use a histogram-based measure like mutual information."
"The clustering step is capturing the essential information which is what images go together and what images do not."
"Predicting rotations of an image works empirically very well."
"Histogram is a distribution that gives you the overall distribution and that's a very important property of an image."
"Fundamentally, the goal of today is just to kind of show you how to think about things in the Fourier world when you're processing images."
"The name of the game today is how can we choose the best threshold for an image without having to manually screw around with that."
"Otsu's algorithm is a classical method for doing this."
"The key question is how to choose this threshold."
"You can also shuffle these elements you know sometimes especially when you are doing like image data analysis etc you want to randomly shuffle these elements."
"These properties are really useful to understand what the contour looks like."
"The aspect ratio is the ratio of width divided by height."
"The extent is the ratio of contour area to the bounding rectangular area."
"We have an image, \( I(x, y) \), the \( x, y \) tells us what pixel we care about, and the \( I(x, y) \) gives us the grayscale value or the color."
"If I wanted to be a little bit more mathematical, I could say it's like the cardinality of the set of pixels such that the pixel value is equal to \( D \)."
"There are many images that have the same histogram."
"We can't really tell exactly what the image is from just looking at the histogram, but we do get a sense of how the pixel intensities are distributed."
"The idea is to think of the image's histogram as a probability mass function."
"So this is how you can extract the biggest or the largest contour in an image."
"These types of enhancements are best applied to remote sensing images with Gaussian or near-Gaussian histograms."
"All we're doing when we're filtering an image is we're taking this little block of pixels and we're sliding it over the image."
"Histogram is such a measure which provides a global description of the appearance of an image."
"It's about the intelligent and concise summary of the contents of images."
"It's about pushing ourselves up into more higher level, more semantic notions about what's in the photographs."
"Image processing analysis is about computing properties of the 3D world from one or more digital images."
"The variational calculus is now one of the leading and top performing approaches to do a lot of things in image analysis."
"Just from one image, we'll get a 3D model."
"We can use machine learning to learn how to automatically analyze images."