I was reading about Automatic Image Distortion Identification. There I encountered a term "BQIA".
What does "Blind Quality Image Assessment" mean ?
Related
This may be a basic conceptual question, but reading on different CNN's such as VGG, Alexnet, GoogleNet, etc it seems that once the model has been trained on a specific image size as input (lets say 256x256), I can't give a different image size to the model (1,920 x 1,080) during inference without resizing or croping. Is this true?
I know that YOLO handles images with different resolutions, is Yolo resizing the image before giving it to the convolution layers?
The requirement that I have is to do object recognition on a series of images that may not have the same image size, the obvious approach would be resizing the image, but that may lead to losing information on the image.
If so, do I need to train a model for every image size that I have, and then reload the model each time for that specific image?
There are more conceptual issues, VGG, AlexNet, GoogleNet are image classification models, while YOLO is an object detection model. Only if the network is fully convolutional it can accept variable-sized images.
So your only option is resize images to a common size, this works well in practice, so you should do it and evaluate different image sizes to see how accuracy changes with it. Only after doing such experiment you can decide if resizing is not appropriate.
i need some help with a corner detection.
I printed a checkerboard and created an image of this checkerboard with a webcam. The problem is that the webcam has a low resolution, therefore it do not find all corners. So i enhanced the number of searched corner. Now it finds all corner but different one for the same Corner.
All Points are stored in a matrix therefore i don't know which element depends to which point.
(I can not use the checkerboard function because the fuction is not available in my Matlab Version)
I am currently using the matlab function corner.
My Question:
Is it possible to search the Extrema of all the point clouds to get 1 Point for each Corner? Or has sb an idea what i could do ? --> Please see the attached photo
Thanks for your help!
Looking at the image my guess is that the false positives of the corner detection are caused by compression artifacts introduced by the lossy compression algorithm used by your webcam's image acquisition software. You can clearly spot ringing artifacts around the edges of the checkerboard fields.
You could try two different things:
Check in your webcam's acquisition software whether you can disable the compression or change to a lossless compression
Working with the image you already have, you could try to alleviate the impact of the compression by binarising the image using a simple thresholding operation (which in the case of a checkerboard would not even mean loosing information since the image is intrinsically binary).
In case you want to go for option 2) I would suggest to do the following steps. Let's assume the variable storing your image is called img
look at the distribution of grey values using e.g. the imhist function like so: imhist(img)
Ideally you would see a clean bimodal distribution with no overlap. Choose an intensity value I in the middle of the two peaks
Then simply binarize by assigning img(img<I) = 0; img(img>I) = 255 (assuming img is of type uint8).
Then run the corner algorithm again and see if the outliers have disappeared
I am using an optical microscope and camera to record some videos which are post-processed in MATLAB.
Real time acquisition and pixel statistics would be extremely helpful, because some of what I am looking at absorbs very little light (I am using transmission mode). An example is that a blank (background) sample would give me an an average pixel value across a 512x512 ccd array of something like 144 (grayscale). An actual sample might have an average value of 140 or so. This subtle shift in pixel intensity would be useful in helping me focus the microscope.
Unfortunately, my camera setup is not supported by MATLAB, so I cannot use the image acquisition toolbox for real time. So I was wondering, is there a way that I could 'fake' real time image acquisition by selecting say a rectangle of my current desktop (the rectangle that is the video output of the microscopes camera), for matlab to record in real time?
Thanks
As part of my project, I want to build an illumination invariant face authentication system. The constraint is I have only 1 enrollment image, but I have the option of adding authentication images to enrollment folder adaptively. The face images are almost frontal. Can anyone suggest an algo or combination of 2-3 algos to make my face auth system robust to illumination.
I have tried LBP and variants of it with DoG, and also a tool box from MATLAB INface toolbox which has some 20 odd illumination normalization algos.
-Chaitanya
The illumination can be corrected (there are algorithms such as Gray-World, Retinex...) => try to preprocess an image with them.
By using different color-spaces such as Lab and HSV you will obtain illumination invariant components (H and S in HSV, a,b in Lab) BUT only for WHITE light (it is good for sun light also). If you have blue lamp (for example) those color spaces would not be appropriate.
My suggestion is to use invariant features such as orientation of gradients, LBP features, SIFT and SURF features and then training a classifier with them.
Most common approach for a face recognition is to use PCA (or kernel PCA) with a SVM classifier.
You will probably have to use more than one image because person will not be in the same environment with the same head orientation.
Have you tried converting to Lab color space? http://en.wikipedia.org/wiki/Lab_color_space
Code:
http://robotics.stanford.edu/~ruzon/software/rgblab.html
I am working on iris recognition and for pre-processing I have to check if the image is
defocused
in motion
Can you help how to find if the image falls in above category?
I am using Matlab 2010b and all the images are grayscale 480x240 containing eyes.
So you want to measure blur? I assume by "in motion" you mean motion blur, so check out: http://www.mathworks.com/matlabcentral/fileexchange/24676-image-blur-metric
Never used it myself but maybe it will help.