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3. GK Current Affair SSC-IBPS-IAS. This app provides GK quiz and knowledge on a broad range of topics. The app has quite well designed.

So in the case of an IMG element, this will get the actual dimensions of the visible image. If you are looking for something similar in xml there is: I took a different approach from the ones stated here, and it is working really well, so I wanted to share it. I'm using a Style to create a custom button with image at the left and text at the center-right. Just follow the 4 "easy steps" below:. Create your 9 patches using at least 3 different PNG files and the tool you have at: After this you should have:.

The states should be like this:. Note that changing the drawableLeft in the ButtonNormalTextWithIcon style, to drawableRight, drawableTop or drawableBottom you can place the icon in other position with respect to the text. For me, this is the better way to do it!

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You can also create a theme for your App and add the "parent style" to it so all the buttons look the same, and apply the "child style" with the icon only where you need it. As others have said SQL comes with a Filestream type that allows you to store a filename or identifier as a pointer in the db and automatically stores the image on your filesystem which is a great scenario.

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If you're on an older database, then I'd say that if you're storing it as blob data, then you're really not going to get anything out of the database in the way of searching features, so it's probably best to store an address on a filesystem, and store the image that way. That way you also save space on your filesystem, as you are only going to save the exact amount of space, or even compacted space on the filesystem.

Also, you could decide to save with some structure or elements that allow you to browse the raw images in your filesystem without any db hits, or transfer the files in bulk to another system, hard drive, S3 or another scenario - updating the location in your program, but keep the structure, again without much of a hit trying to bring the images out of your db when trying to increase storage.

As a note, this seems to be fairly efficient. I did a test where I requested the image through the controller http: The average was calculated by making thousands of requests on the local machine, so the totals should not include network latency or bandwidth issues. This is a pleasantly easy fix. The first is a standard approach in computer vision, keypoint matching. This may require some background knowledge to implement, and can be slow. The second method uses only elementary image processing, and is potentially faster than the first approach, and is straightforward to implement. However, what it gains in understandability, it lacks in robustness -- matching fails on scaled, rotated, or discolored images.

Certain parts of an image have more information than others particularly at edges and corners , and these are the ones you'll want to use for smart image matching.

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Google "keypoint extraction" and "keypoint matching" and you'll find quite a few academic papers on the subject. One downside to keypoint matching is the running time of a naive implementation: Some clever algorithms might find the closest match faster, like quadtrees or binary space partitioning.

Another less robust but potentially faster solution is to build feature histograms for each image, and choose the image with the histogram closest to the input image's histogram. I implemented this as an undergrad, and we used 3 color histograms red, green, and blue , and two texture histograms, direction and scale. I'll give the details below, but I should note that this only worked well for matching images VERY similar to the database images.

Re-scaled, rotated, or discolored images can fail with this method, but small changes like cropping won't break the algorithm. Computing the color histograms is straightforward -- just pick the range for your histogram buckets, and for each range, tally the number of pixels with a color in that range. For example, consider the "green" histogram, and suppose we choose 4 buckets for our histogram: Then for each pixel, we look at the green value, and add a tally to the appropriate bucket.

When we're done tallying, we divide each bucket total by the number of pixels in the entire image to get a normalized histogram for the green channel. For the texture direction histogram, we started by performing edge detection on the image. Each edge point has a normal vector pointing in the direction perpendicular to the edge. We quantized the normal vector's angle into one of 6 buckets between 0 and PI since edges have degree symmetry, we converted angles between -PI and 0 to be between 0 and PI.

After tallying up the number of edge points in each direction, we have an un-normalized histogram representing texture direction, which we normalized by dividing each bucket by the total number of edge points in the image. To compute the texture scale histogram, for each edge point, we measured the distance to the next-closest edge point with the same direction. For example, if edge point A has a direction of 45 degrees, the algorithm walks in that direction until it finds another edge point with a direction of 45 degrees or within a reasonable deviation.

After computing this distance for each edge point, we dump those values into a histogram and normalize it by dividing by the total number of edge points. Now you have 5 histograms for each image. To compare two images, you take the absolute value of the difference between each histogram bucket, and then sum these values.

For example, to compare images A and B, we would compute. The smaller the result, the better the match. Repeat for all images in the database, and the match with the smallest result wins. You'd probably want to have a threshold, above which the algorithm concludes that no match was found.

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This involves extracting simple keypoints and using a collection decision trees to classify the image. This is faster than simple SIFT keypoint matching, because it avoids the costly matching process, and keypoints are much simpler than SIFT, so keypoint extraction is much faster. However, it preserves the SIFT method's invariance to rotation, scale, and lighting, an important feature that the histogram method lacked. My mistake -- the Semantic Texton Forests paper isn't specifically about image matching, but rather region labeling.

The original paper that does matching is this one: Keypoint Recognition using Randomized Trees. Also, the papers below continue to develop the ideas and represent the state of the art c. I have made comparison of the three algorithms in the accepted answer. I generated colors in cycle where only about every th color was used. Each color is represented by 2x2 pixels, colors are sorted from darkest to lightest left to right, top to bottom.

Pattern can be sometimes spotted on 1st and 2nd picture depending on the number of colors in one row. I never spotted any pattern on picture from 3rd or 4th algorithm. Toggle Navigation. Favicon generator Published: Reviewed by: Review of: On February 15, Last modified: February 11, More Details. After generating a favicon with this tool download and save to the root directory of your site. Then include the following code in the head of your HTML document. Selection One of the prerequisites for many of the applications mentioned below is a method of selecting part s of an image, thus applying a change selectively without affecting the entire picture.

Most graphics programs have several means of accomplishing this, such as: Image size alteration Image editors can resize images in a process often called image scaling, making them larger, or smaller. Cropping an image Main article: How to vertically align an image inside a div? So there is a solution: Options ; options. How do I auto-resize an image to fit a div container Do not apply an explicit width or height to the image tag. Instead, give it: Choosing whether to do height or width depends on your image and container dimensions: When to use IMG vs. CSS background-image?

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  8. This ensures that the meaning of the image can be communicated in all user-agents, including screen readers. Note, this is no longer true with CSS3 background-size; see 6 below. Above answers considers only Design aspect. I am listing it in SEO aspects. If your image is not too small not iconic images. No Relation With Content.

    Small Images which we can play with CSS3. Repeating Images In blog author icon , date icon will be repeated for each article etc. Your SVG file will look like this: Using that approach you'd end up with something like: PNG vs. GIF vs. JPEG vs. SVG - When best to use? First, there are two types of compression: If you saved an image in a Lossy format over and over, the image quality would get progressively worse and worse.

    GIF images can also be animated and have transparency. Also, gradients. Any text will appear above the background. Just follow the 4 "easy steps" below: After this you should have: The states should be like this: You got your button with an image at the left side.

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