# Tag Info

15

Check this handout! Well, there a few so... lets go: Given two images $J[x,y]$ and $I[x,y]$ with $(x,y) \in N^{N \times M}$... A - Used in template matching: Template Matching is linear and is not invariant to rotation (actually not even robust to it) but it is pretty simple and robust to noise such as the ones in photography taken with low illumination. ...

3

I want to view a specific image or a dataset's distribution, and see if they are different. Does this do the trick? It depends what you want to understand or learn about your data. what does each axis mean then? In all of your plots, the x-axis ranges from 0-255, which is because in all your plots, you are creating histograms of the individual pixel ...

3

MNIST is not an interesting data set. You use MNIST to learn how to do machine learning that you will you on interesting data sets. Thus... If you just want to figure out how to do the Keras code for an image classification problem, feel free to use 0/1 encoding of white vs not-white spaces in the images. It might lower the computational demands so you can ...

2

This question is slightly philosophical, but can be explained in this way - If your model is trained on real photographs it will likely not generalize well to things like drawings unless they are photorealistic or contain the features that the model is using to classify the images. You would likely have to include drawings in your training data to be able to ...

2

I don't know about any papers, though making your objects more distinct should definitely help your model to learn proper filters. One thing to keep in mind though is your production data. Will you be also feeding images with poor exposure, white balance, contrast? If so, you need to train your model on such data. As far as I know, this repository produces ...

2

Try specifying mode so that PIL is aware of data format. img = Image.fromarray(source_array, mode="CMYK") If that does not work, what is the shape of source array ?

2

One choice is to train a neural network model to take these values and output original images. Notice that usually some data is loss in this process so it might be impossible to reconstruct the image with perfection. You could try inverting the functional form but: CNNs usually use ReLu activation which is not bijective. Pooling layers throws information ...

2

The output is going to be "continuous" if you don't use it for "classification". You can follow few approaches here: This would be the simplest approaches but you probably need some post processing to fill holes in lines: Define a Threshold to make the image binary; Redefine the last layer to output a image with 2 channels and define your final image by ...

2

These types of connections between layers are called skip connections, searching for this will give you a way to find more in-depth information. Broadly speaking, there are two advantages to using skip connections in this case: They allow gradients to more freely flow through the model, mitigating the issue of vanishing gradients They allow features from ...

1

I did this implementation, #img = [w,h,c] numpy array of image out = img == [0,0,0] np.sum(np.sum(out,axis=1) == 3) Its working. Let me know in case we can optimized it further.

1

The answer is no, they are not limited. However, your statements seem to contain multiple misunderstandings, so let's first clarify them: The sequential and functional APIs in Keras are different approaches for structuring the layers of a neural network. Both can have dense layers and convolutional layers. Convolutional layers exploit information locality ...

1

One can never 100% say that a data preprocessing approach will yield positive results. So, if you are trying different things, always test and use the metrics to see what works best. With that said, what you described is often referred to data augmentation, namely the generation of more data points, typically from existing data points. It is very common ...

1

Suppose you trained two identical neural nets on different datasets. Network A is trained using a dataset of cat pictures. Network B is trained using a dataset of traffic sign images. Because the two networks are identical, they will obviously produce a feature map in the same space, right? But the distribution of features in that space will be different ...

1

Couple of Things straight up: Inject some noise in the process. When the gan or autoencoder learns that there is some noise it will start to generalise better Use weaker architectures. (Analogy to weak learners, you cant build random forest wit hstrong trees). Basically allowing for your Architecture to be weak enough to be able to generalise and not learn ...

1

$\hat{p}=2\big(\frac{p}{p_{max}}\big)-1$ so that $\hat{p}\in [-1,1]$ where $p\in[0,p_{max}]$ E.g. with 8-bit channels, $p_{max}=255$, black/white correspond to $-1$ and $1$ respectively, and grayscale is linearly mapped in $[-1,1]$.

1

Assuming you already have a logo sample to detect. In that case you can use OpenCV template detection. You need to pass source logo and target image. Link - Template Matching using OpenCV

1

This usually happens when your discriminator starts to just understand that the difference between your generated(fake) and real samples only differ by some n/255 factor. This is easy to learn, and if your generator cannot properly generate "good" samples then within a few 10-100 epochs your Discriminator will learn this highly trivial ...

1

You're probably seeing those artifacts because your model doesn't see those pixels immediately outside your tile and so can't know how to "blend" things. (I'm assuming your tiles have a stride equal to the input size) A typical approach I've seen used (and used myself) is, at inference time, to keep only a central portion of each tile and then to ...

1

The lines connecting the ends seem also parallel to existing lines with a similar pattern but faded a lot more. I think they are the 2D equivalent of harmonics. Maybe start with a generated straight line segment at a small angle, e.g. $\pi / 6$ rad. Not end-to-end, and maybe not even centered.

1

Below is my code for visualizing a decision tree. Hope it helps.

1

Would I be able to use BGR images for an RGB-trained network? I think the performance will be much worse than RGB input. Color Permutations as augmentation Paper Rethinking Data Augmentation: Self-Supervision and Self-Distillation: if the augmentation results in large distributional discrepancy among pictures (e.g., rotations), forcing their label ...

1

Task dependent but could be important. Lets find some arbitrary representation that separates luminance from chrominance (represented as a color 2D vector)? That way, you are detecting bright objects independent of color, and colorful objects less dependent on brightness. Obviously under assumption that you are color agnostic. Check this paper where they ...

1

DQNs don't only accept image frames as input. For instance, this DQN for the CartPole environment takes in a state with only 4 elements (the position and velocity of the cart and the angle and velocity of the pole.) Images aren't necessarily more efficient representations than other options. For instance, here is another DQN for the same CartPole ...

1

If it's a screenshot of a computer game, where each constituent has purely that box, that is by default any given box is white or black. then, any simple matrix operation like, variance of color in that box could be a feature in determining if there is a piece or not. If it's a hover cam image (Top view), then it's a different problem. you can simply do a ...

1

Both answers are good. The opposite happens a lot, if you look for "eye pupil detection from synthesis" in Google you will find a lot of papers using Unity Eyes to train different models. Particularly I like the work of Chao Gou in the matter: In Learning-by-Synthesis for Accurate Eye Detection, he trains a model in pure synthetic data and shows ...

1

As a rule of thumb, the data distribution of your test set should be of the same nature as the one in the train set. So for example if you have a network that classifies cats and dogs and you trained it with super clean and good images and then you try to feed it fuzzy images done with crappy phones, you might be surprised about the results... performance ...

1

I contacted the authors of the mobile net architecture and papers and they provided me with the following answer: The multiplier accomplishes what is being articulated in C): it acts as a re-scaling of the original input image to fit the value specified by the parameter. The default image size is 224x244, which corresponds to a resolution multiplier of 1.0. ...

Only top voted, non community-wiki answers of a minimum length are eligible