My family would eat at a restaurant, diner, or buffet at least once a week, often more than once. The restaurant seemed magical and fascinating to me when I was an elementary school kid, and surprisingly, the place still fascinates me today. Recently, when I visited my old family house for Thanksgiving, I was astonished and pleased to find out the place still operated and, in fact, was still run by the same family.
Part 7 A lot many things look difficult and mysterious. But once you take the time to deconstruct them, the mystery is replaced by mastery and that is what we are after.
EE-Sane. 1, likes · 13 talking about this · 4, were here. Thai Restaurant. Jump to. you must be real special for me to write a review! Hopefully this will make it back to my waitress and she takes this as constructive criticism! We will resume regular business hours on Tuesday. Sorry for any inconvenience this may have caused.  kwjWXajbWjnQta 投稿者：Archie 投稿日：/10/13(Mon) More or less not much going on worth mentioning. Pretty much nothing seems worth. Sight Words, Reading, Writing, Spelling & Worksheets. Everything you need to know about sight words. We also provide articles and worksheets for parents and teachers to provide assistance with spelling, writing and reading.
If you are a beginner and are finding Computer Vision hard and mysterious, just remember the following Q: How do you eat an elephant? One bite at a time! What is a Feature Descriptor A feature descriptor is a representation of an image or an image patch that simplifies the image by extracting useful information and throwing away extraneous information.
In the case of the HOG feature descriptor, the input image is of size 64 x x 3 and the output feature vector is of length Keep in mind that HOG descriptor can be calculated for other sizes, but in this post I am sticking to numbers presented in the original paper so you can easily understand the concept with one concrete example.
Clearly, the feature vector is not useful for the purpose of viewing the image. But, it is very useful for tasks like image recognition and object detection. The feature vector produced by these algorithms when fed into an image classification algorithms like Support Vector Machine SVM produce good results.
Suppose we want to build an object detector that detects buttons of shirts and coats. A button is circular may look elliptical in an image and usually has a few holes for sewing.
You can run an edge detector on the image of a button, and easily tell if it is a button by simply looking at the edge image alone. In addition, the features also need to have discriminative power. For example, good features extracted from an image should be able to tell the difference between buttons and other circular objects like coins and car tires.
In the HOG feature descriptor, the distribution histograms of directions of gradients oriented gradients are used as features. Gradients x and y derivatives of an image are useful because the magnitude of gradients is large around edges and corners regions of abrupt intensity changes and we know that edges and corners pack in a lot more information about object shape than flat regions.
How to calculate Histogram of Oriented Gradients?
In this section, we will go into the details of calculating the HOG feature descriptor. To illustrate each step, we will use a patch of an image. Of course, an image may be of any size.
Typically patches at multiple scales are analyzed at many image locations. The only constraint is that the patches being analyzed have a fixed aspect ratio. In our case, the patches need to have an aspect ratio of 1: Now we are ready to calculate the HOG descriptor for this image patch.
The paper by Dalal and Triggs also mentions gamma correction as a preprocessing step, but the performance gains are minor and so we are skipping the step. Calculate the Gradient Images To calculate a HOG descriptor, we need to first calculate the horizontal and vertical gradients; after all, we want to calculate the histogram of gradients.
This is easily achieved by filtering the image with the following kernels. We can also achieve the same results, by using Sobel operator in OpenCV with kernel size 1.
Absolute value of x-gradient. Absolute value of y-gradient. Notice, the x-gradient fires on vertical lines and the y-gradient fires on horizontal lines.
The magnitude of gradient fires where ever there is a sharp change in intensity.
None of them fire when the region is smooth. I have deliberately left out the image showing the direction of gradient because direction shown as an image does not convey much.
The gradient image removed a lot of non-essential information e. In other words, you can look at the gradient image and still easily say there is a person in the picture.
At every pixel, the gradient has a magnitude and a direction.They can then write a statement of claim, provide evidence from the graph to support their claim, and explain their scientific reasoning.
Here is an example of what that looks like in my classroom.
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30 Quirky Collective Nouns for Animals Since the earliest instances of visual communication, humans have demonstrated a fascination with animals. Depictions of horses, bison, aurochs, and deer adorn the walls of prehistoric caves. EE-Sane. 1, likes · 13 talking about this · 4, were here.
Thai Restaurant. Jump to. you must be real special for me to write a review! Hopefully this will make it back to my waitress and she takes this as constructive criticism!
We will resume regular business hours on Tuesday. Sorry for any inconvenience this may have caused. This book touches on all aspects of presentation design: layout, colours, fonts, story telling, tools, data visualisation, and discusses the .