Image in sentence

2082 examples of Image in a sentence

All those years of chasing that conventional image of beauty that we saw earlier was finally beginning to take its toll.

So this is a satellite image from August of 2008, and this satellite image is colored so that vegetations or plants show up as green and water shows up as blue.

So this satellite image is from just two weeks later.

Here's an image from about a week later, and you can see these are the previous pathways, and you can see this process of river-jumping continues as this river moves farther away from its major course.

If I put up a second version of the image here and join the two patches with a gray-colored bar, you can see there's no difference.

Here we've combined immersive virtual reality with image processing to simulate the effects of overly strong perceptual predictions on experience.

The professor would put up an image, bold strokes of blues and yellows, and say, "Who's that?"

This image is probably all too familiar.

And I realized that as the magazine had become known for its in-depth research and long reports, some of the humor had gotten lost along the way, because now often Eustace Tilley was seen as a haughty dandy, but in fact, in 1925, when Rea Irvin first drew this image, he did it as part of a humor magazine to amuse the youth of the era, which was the flappers of the roaring twenties.

So to get this image on the left by Anita Kunz, or the one on right by Tomer Hanuka, you have to play spot the differences.

Right after September 11, I was at a point, like everybody else, where I really didn't know how to deal with what we were going through, and I felt that no image could capture this moment, and I wanted to just do a black cover, like no cover.

And I sat down to draw this, and as soon as I saw it, a shiver ran down my spine and I realized that in this refusal to make an image, we had found a way to capture loss and mourning and absence.

And a simple image can speak volumes.

So this is the image that we published by Bob Staake right after the election of Barack Obama, and captured a historic moment.

So back in November 2016, during the election last year, the only image that we could publish was this, which was on the stand on the week that everybody voted.

And when we found out the result, we really were at a loss, and this is the image that was sent by Bob Staake again, and that really hit a chord.

And again, we can't really figure out what's going to come next, but here it felt like we didn't know how to move forward, but we did move forward, and this is the image that we published after Donald Trump's election and at the time of the Women's March all over the US.

So over those 24 years, I have seen over 1,000 images come to life week after week, and I'm often asked which one is my favorite, but I can't pick one because what I'm most proud of is how different every image is, one from the other.

So let's just see what Darknet thinks of this image that we have.

When we run our classifier on this image, we see we don't just get a prediction of dog or cat, we actually get specific breed predictions.

So we've made amazing strides in image classification, but what happens when we run our classifier on an image that looks like this? Well ... We see that the classifier comes back with a pretty similar prediction.

And it's correct, there is a malamute in the image, but just given this label, we don't actually know that much about what's going on in the image.

I work on a problem called object detection, where we look at an image and try to find all of the objects, put bounding boxes around them and say what those objects are.

So here's what happens when we run a detector on this image.

Now, when I started working on object detection, it took 20 seconds to process a single image.

And to get a feel for why speed is so important in this domain, here's an example of an object detector that takes two seconds to process an image.

So in just a few years, we've gone from 20 seconds per image to 20 milliseconds per image, a thousand times faster.

Well, in the past, object detection systems would take an image like this and split it into a bunch of regions and then run a classifier on each of these regions, and high scores for that classifier would be considered detections in the image.

But this involved running a classifier thousands of times over an image, thousands of neural network evaluations to produce detection.

With our system, instead of looking at an image thousands of times to produce detection, you only look once, and that's why we call it the YOLO method of object detection.

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