In general, do medical equipment users compress the image before modifying it? (where: "modify" means using histogram equalization, smoothing, image segmentation, etc)?
I am a newbie to image processing and eager to know about the basic working process of image processing.
Related
I am working on a project, which will intake multiple images (lets say 2 for the moment) and combine them to generate a better image. The resultant image will be a combination of those input images. As a requirement I want to achieve this by using OpenCV. I read about Image Stitching and saw some example images in the process and now I am confused whether image overlapping is equal to image stitching, or can the Stitcher class in OpenCV do Image overlapping? A little clarity as to how can I achieve the above project problem thru OpenCV.
"Image overlapping" is not really a term used in the CV literature. The general concept of matching images via transformations is most often called image registration. Image registration is taking many images and inserting them all into one shared coordinate system. Image stitching relies on that same function, but additionally concerns itself with how to blend multiple images. Furthermore, image stitching tries to take into account multiple images at once and makes small adjustments to the paired image registrations.
But it seems you're interested in producing higher quality images from multiple images of the same space (or from video feed of the space for example). The term for that is not image overlapping but super-resolution; specifically, super-resolution from multiple images. You'll want to look into specialized filters (after warping to the same coordinates) to combine those multiple views into a high resolution image. There are many papers on this topic (e.g.). Even mean or median filters (that is, taking the mean or median at every pixel location across the images) can work well, assuming your transformations are very good.
I'm using a simple neural network (similar to AlexNet) to classify images into categories. As a preprocessing stage, input images are resized to 256x256 before being fed into the network.
Lately, I have run into the following problem: Many of the images I deal with are of very high resolution (say, 2000x2000). In this case, doing a "hard resize" results in a severe loss of information. For example, a small 100x100 face, easily recognisable in the original image, would be unrecognisable in the resized version. In such cases, I may prefer taking several crops of the 2000x2000 image and run the classification on each crop.
I'm looking for a method to automatically determine which type of pre-processing is most adequate. Ideally, it would be able to recognize, for example, that a high resolution image of a single face should be resized, whereas a high resolution image of a crowd should be cropped several times. The basic requirements, on my part:
As computationally efficient as possible. Hence, something like a "sliding window" would be probably be ruled out (it is computationally cheaper to just crop all the images).
Ability to balance between recall and precision
What I considered thus far:
"Low-level" (image processing) approach: Implement an algorithm that uses local image information (like gradients) to distinguish between high resolution and low resolution images.
"High-level" (semantic) approach: Run the images through a pre-trained network for segmentation of some sort, and use its oputput to determine the appropriate pre-procssing.
I want to try the first option first, but not exactly sure how to go about it. Is there anything I can do in the Fourier domain? Something in OpenCv I can try? Does anyone have any suggestions/thoughts? Other ideas would be very welcome too. Thanks!
Given a logo image as a reference image, how to detect/recognize it in a cluttered natural image?
The logo may be quite small in the image, it can appear in clothes, hats, shoes, background wall etc. I have tried SIFT feature for matching without any other preprocessing, and the result is good for cases in which the size of the logo in images is big and the logo is clear. However, it fails for some cases where the scene is quite cluttered and the proportion of the logo size is quite small compared with the whole image. It seems that SIFT feature is sensitive to perspective distortions.
Anyone know some better features or ideas for logo detection/recognition in natural images? For example, training a classifier to locate candidate regions first, and then apply directly SIFT matching for further recognition. However, training a model needs many data, especially it needs manually annotating logo regions in images, and it needs re-training (needs to collect and annotate new image) if I want to apply it for new logos.
So, any suggestions for this? Detailed workflow/code/reference will be highly appreciated, thanks!
There are many algorithms from shape matching to haar classifiers. The best algorithm very depend on kind of logo.
If you want to continue with feature registration, i recommend:
For detection of small logos, use tiles. Split whole image to smaller (overlapping) tiles and perform usual detection. It will use "locality" of searched features.
Try ASIFT for affine invariant detection.
Use many template images for reference feature extraction, with different lightning , different background images (black, white, gray)
I am working on a project which identifies objects after capturing their images on Android platform. For this, I extracted features of sample images such as compactness, rectangularity, elongation, eccentricity, roundness, sphericity, lobation, and hu moments. After then, random tree is used as classifier. As I used pictures gathered from Google which are not in high resolution for creating my classifier, captured images of size 1280x720 gives 19/20 correct results when the image is cropped.
However, when I capture images of large sizes such as about 5 megapixels, and crop them for identification, the number of obtained correct results dramaticaly decreases.
Do I need to extract features of images with high resolution and train them in order to get accurate results when high resolution pictures are captured? Is there a way of adjusting extracted features related to the image resolution?
Some feature descriptors are sensitive to scaling. Others, like SIFT and SURF, are not. If you expect the resolution (or scale) of your images to change, it's best to use scale-invariant feature descriptors.
If you use feature descriptors that are not scale-invariant, you can still get decent results by normalizing the resolution of your images. Try scaling the 5 megapixel images to 1280x720 -- do the classification results improve?
I have a dataset of about 2000 images. This database contains some blurred images.
How can I automatically remove the blurred images from this database?
I read about fourier transformation to remove the blurred images. First I need to transform my images into fourier domain and then by applying some threshold I will be able to identify the blurred images. Could anybody give me some sample code in matlab for this? I don't know how to determine the threshold. Are there any way to determining this threshold?
This task is really not so simple, if you remove all the images that doesn't contain high frequencies you will end up removing many images that contain smooth scenes even though they are not blurred.
There is no 100% in computer vision, the best thing for you (in my opinion) is to make a human aided software, your software should suggest on the images that it thinks should be removed, but the final call must be made by a human being.