School going kids wear a certain uniform on respective days within a week.
(e.g. for boys = White half pant, white shirt, tie of a certain color, belt ..)
(e.g. for girls = blue checked frock, navy blue pinafore, black shoes with navy blue socks and belt). They will have different variations to wear on different days.
What would be a good way to show such information in the user interface for boys / girls. Can you point me to any sites which depict such information very clearly.
Show the uniforms graphically through images of a boy and a girl wearing the correct uniform for the selected day of the week. If you want to go all out, you could have 360 degree views (spinnable by mouse/gesture) for the boy and girl.
How about this kind of table, replace smiley's with uniform images and Description of the uniform below that.
Photo and text. It's absolutely enough for parents. May be not enough for designer...
It seems to me to be over-complicated to have a "uniform" that changes on a daily basis, to the point that you require an app which informs people what to wear. But given the requirements, I'd simply show the list of items for each day.
If, for example, there are only a couple combinations, I'd give each combination a name and put the combination name on the calendar. Then I'd make the name itself a link to further information about it, including the detailed list of clothes and pictures of each item. Perhaps a picture of a model wearing the clothes but that might not be necessary.
If the uniform varies a lot, such that enumerating the combinations is impractical or impossible, I'd show the full list (with pictures) for each day.
Aside, is this an actual requirement for a real school? My high-school had a uniform and it wasn't nearly so complicated. There was just a list of allowable/required elements and it was distributed to the parents before the start of the school year. Most of the time the rules could be stated in a couple sentences, such as "From the start of the school year until October 1st, and from Easter until the end of the school year, the 'Summer Uniform' may be worn; the rest of the school year only the standard uniform is permissible. The standard uniform is ... for boys and ... for girls.' This certainly was straightforward enough that there was never any serious question, and if there was a problem the teachers would simply inform the students that they were "out of uniform".
Related
I have read all that staff about image similarity index on that forum but i think that my subject is kind different because images that i want to compare comes from an L-system generator and as you can see bellow it's hard to find obvious differences. So i couldn't decide which method and software to choose for my problem.
But let's take the story from the beginning. I have a collection of data , by measuring angles and lengths of branches of some plants (15 in total), and i represented them with L-system fractals method as already told.
These images looks like the above ones:
Plant A
Plant B
Plant C
Till now i tried to find differences using two methods.
1) By calculating the fractal dimension of those images but as expected, it was 2 in all of them
2) By calculating the % of area coverage in a same canvas. Numbers in that case show some differences but there are not statistically significant.
So the thought was to use an other similarity index but there are too many protocols and ideas out there that i couldn't find a starting point. I read about OPENCV , VisualCI etc but because i've never used such methods again, i feel somehow lost.
Any of your suggestions will be welcome.
Thank you.
What I need is actually just a hint where I can start.
I'm somewhat familiar to Mahout, at least theoretically. I know how it work, how to set it up, etc, and I could build a simple recommendation system based in collaborative filtering.
However, now I'm trying to do something more complex and even after reading quite some about different algorithms, I'm not sure which direction to go.
Quickly what I want to do is:
The final goal is to define one scalar (a "score") of each one of a set of entities based on some "known" entities. The entities interact with each other, known scores influence and define the unknown ones. You can imagine with the following example.
I have a lot if white clothes and a few pieces of colorful ones; red, blue, green... I put them into the washing machine. I want to know what colors the white ones will get after the wash.
Things to take into account:
we make a series of washing with different "actors"... some clothes are washed in the 1st and 3rd washing, some of them only in the 2nd, some of them are washed in all
in consecutive washes the clothes that were white before but now colored also influence the rest, but not as strong (as they are not as colored)
some colors don't "color" as much as others. for example red has a strong effect on most of the clothes, but green not so much
coloring effect also depends on how many clothes are in one washing. If you wash a red shirt with a white t-shirt, it gets much more colored, than if there were 100 other white t-shirt
clothes don't "lose" their color when influencing others
You can see that while calculating, entities actually have 2 assigned scalars:
the color hue (this also defines "coloring power" as mentioned above). The hue can be represented as a number, from 0 to 1, let's say. The coherence between the coloring power and the color number is not linear. It is more like the ends of the scale have more coloring power (0 and 1) while the middle (0.5) has less
the color "lightness" (how much an entity is colored, for originally colored clothes it's 1, for white ones it's 0), which in the same time also defines coloring power regardless of the hue
So, again, what I know:
which clothes where washed in which consecutive washing
I know the original color of some of them, the rest is white in the beginning
What I want to know:
- the hue of all clothes in the end of the washing
The problem is that I don't know what (type) of algorithm should I start with. If you were so kind to read so far, please suggest me something (or further reading).
Obviously I don't ask for any detailed thing, again, only hints.
Thank you!
The only thing I can think of that sounds like this problem is PageRank. It's computed by a sort of iterative simluation. Each page has some influence (color) which flows via its links (socks its washed with) and at some point the page influence reaches a steady state (final color). You can look up PageRank algorithms but it is essentially a matter of calculating eigenvectors of a big, erm, sock color matrix.
I doing a small program using Processing, and it's basically a map of Europe and a question will ask where a certain capital is in Europe. For example, if the question is where Milan is and the person clicks on Madrid I want to use a GPS to calculate the distance between Milan and Madrid. So hopefully the output would be "You have clicked on the wrong city. You are xxx miles away from Milan".
How do I code for this?
You definitely don't need a GPS for that, in fact a GPS won't even do what you want. All you need is the coordinates of the capitals for which you will ask the location. Presumably you can get this as latitude and longitude, although since you're displaying them on the screen, perhaps you will just get their x/y coordinates from whatever image/display you are using.
Lets assume you have an x and y for a city, and a click on the screen. The distance between two points on a plane is probably something you learned in high school geometry. The equation is available on Wikipedia.
If for some reason, you need to calculate the distance between two points of latitude/longitude, that's a little more complicated, and probably not worth it, but it's doable -- in fact, the question has been asked on SO.
That should be enough to get you started. If not, you should probably flesh your question out with some details.
i want to identify a ball in the picture. I am thiking of using sobel edge detection algorithm,with this i can detect the round objects in the image.
But how do i differentiate between different objects. For example, a foot ball is there in one picture and in another picture i have a picture of moon.. how to differentiate what object has been detected.
When i use my algorithm i get ball in both the cases. Any ideas?
Well if all the objects you would like to differentiate are round, you could even use a hough transformation for round objects. This is a very good way of distinguishing round objects.
But your basic problem seems to be classification - sorting the objects on your image into different classes.
For this you don't really need a Neural Network, you could simply try with a Nearest Neighbor match. It's functionalities are a bit like neural networks since you can give it several reference pictures where you tell the system what can be seen there and it will optimize itself to the best average values for each attribute you detected. By this you get a dictionary of clusters for the different types of objects.
But for this you'll of course first need something that distinguishes a ball from a moon.
Since they are all real round objects (which appear as circles) it will be useless to compare for circularity, circumference, diameter or area (only if your camera is steady and if you know a moon will always have the same size on your images, other than a ball).
So basically you need to look inside the objects itself and you can try to compare their mean color value or grayscale value or the contrast inside the object (the moon will mostly have mid-gray values whereas a soccer ball consists of black and white parts)
You could also run edge filters on the segmented objects just to determine which is more "edgy" in its texture. But for this there are better methods I guess...
So basically what you need to do first:
Find several attributes that help you distinguish the different round objects (assuming they are already separated)
Implement something to get these values out of a picture of a round object (which is already segmented of course, so it has a background of 0)
Build a system that you feed several images and their class to have a supervised learning system and feed it several images of each type (there are many implementations of that online)
Now you have your system running and can give other objects to it to classify.
For this you need to segment the objects in the image, by i.e Edge filters or a Hough Transformation
For each of the segmented objects in an image, let it run through your classification system and it should tell you which class (type of object) it belongs to...
Hope that helps... if not, please keep asking...
When you apply an edge detection algorithm you lose information.
Thus the moon and the ball are the same.
The moon has a diiferent color, a different texture, ... you can use these informations to differnentiate what object has been detected.
That's a question in AI.
If you think about it, the reason you know it's a ball and not a moon, is because you've seen a lot of balls and moons in your life.
So, you need to teach the program what a ball is, and what a moon is. Give it some kind of dictionary or something.
The problem with a dictionary of course would be that to match the object with all the objects in the dictionary would take time.
So the best solution would probably using Neural networks. I don't know what programming language you're using, but there are Neural network implementations to most languages i've encountered.
You'll have to read a bit about it, decide what kind of neural network, and its architecture.
After you have it implemented it gets easy. You just give it a lot of pictures to learn (neural networks get a vector as input, so you can give it the whole picture).
For each picture you give it, you tell it what it is. So you give it like 20 different moon pictures, 20 different ball pictures. After that you tell it to learn (built in function usually).
The neural network will go over the data you gave it, and learn how to differentiate the 2 objects.
Later you can use that network you taught, give it a picture, and it a mark of what it thinks it is, like 30% ball, 85% moon.
This has been discussed before. Have a look at this question. More info here and here.
I need to draw n different objects on a chart. I want to pick a different color for each of them to make them distinguishable. The objects will be moved around, so I cannot count on ideas like "four color theorem" to assign same color to non-adjacent items. So far my problem call for up to 20 different items.
Is there a good way to pick n different colors to make them as distinguishable from each other as possible?
First of all, I have since changed the design so that it is not important to use 20 distinct colors. The default palette of 10 colors show up quite well.
Secondly, I've found an answer to my own question. The thing I want to do is called Color scale for categorical coding. Here is a paper that propose a method to do it
An algorithm for generating color scales for both categorical and ordinal coding - Breslow - 2009 - Color Research & Application - Wiley Online Library
http://onlinelibrary.wiley.com/doi/10.1002/col.20559/full
I'm going to give the paper a glance. It is probably too technical than what I prepare to do.
I'd say colour distinction is a very subjective matter and you're probably better off looking for an existing colour palette and working your way from there. The higher your n, the higher your chance of two automatically generated colours being indistinguishable by your users even though by some colour-theoretic criterion they are very different.
And don't forget to make sure you don't use colour as the only distinction between objects, or:
you'll be in for a lot of hate mail from colour blind people
you risk people mistaking objects of similar colours as having some sort of implicit grouping
Do you really need to use 20 different colors? That is a lot of colors if you still want people to be able to distinguish them. Also realize that people who are colorblind will be lost looking at your charts. 10% of males are color blind. It would be better if you could further break down your objects into two to five groups. Then you could use different shapes as well as color to distinguish objects. For instance, you might have crosses, circles, triangles, stars, and squares of four different colors as shown here:
For choice of colors, I would check out the color brewer. However, notice it doesn't go up to 20 colors.