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For a 3 channel image (RGB), each filter in a convolutional layer computes a feature map which is essentially a single channel image. Typically, 2D convolutional filters are used for multichannel images. This can be a single filter applied to each layer or a seperate filter per layer. These filters are looking for features which are independent of the color,...


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Aesthetics of images has a strong subjective element and possibility of multiple dimensions depending on purpose of the media. That means: It is hard to define what we mean by scoring aesthetics. Given any well-constrained definition, it is then time-consuming to collect relevant data. However, there is some interest in the machine-learning community, as ...


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There are many computer vision (CV) algorithms and models that are used for different purposes. So, of course, I cannot list all of them, but I can enumerate some of them based on my experience and knowledge. Of course, this answer will only give you a flavor of the type of algorithm or model that you will find while solving CV tasks. For example, there are ...


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If the computational components of the forward feed through the network have no curvature, which is normally the case in a sum of products, then it can be proven that any constant pixel value is equivalent in terms of effect on convergence results. We wouldn't expect a proof for that, since it would be too trivial to spend time writing up for publication. ...


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The Wikipedia article related to computer vision gives, in my opinion, a good description of the field and its relation to image processing. Below, I will only cite the most relevant parts of the article. Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or ...


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I don't know if there is an existing pretrained NN that does this but it wouldn't be very hard to modify one to do this. First, I'd take a pretrained image classification NN (e.g. VGG, ResNet), drop its final layer and replace it with one with 4 neurons, representing the 4 orientations (so that you know which way to rotate it). Then I'd take again a ...


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There are hundred of papers on this task some older than I am! Normally this is done by trying to form a box shape around the image than estimate the volume. This task is typically done with multiple images so the two can generate a more clear picture of the size of the object than one image alone. An object could be 'infinitely' large but its mass could be ...


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If the measurements you want from the object aren't too complicated (ie. length of a clearly defined feature), and if you are able to acquire a training dataset of images of the objects similar to what your model will see in your use case (same scale/distance), their bounding boxes and their measurements, a model you could try to implement is a Multi-Task ...


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Father Ted explains why this is a hard problem. Seriously -- if you have stereo images it should be possible, since that's what we use for depth perception. If you know how far away points x1 and x2 are, then you can measure distance using trigonometry. No neural networks needed, I guess. https://en.wikipedia.org/wiki/Triangulation_(computer_vision)


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You are partially correct. On CNNs the output shape per layer is defined by the amount of filters used, and the application of the filters (dilation, stride, padding, etc.). CNNs shapes In your example, your input is 30 x 30 x 3. Assuming stride of 1, no padding, and no dilation on the filter, you will get a spatial shape equal to your input, that is ...


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About the images inside the CNN layers: I really recommend this article since there is no one short answer to this question and it probably will be better to experiment with it. About the RGB input images: When needed to train on RGB pictures it is not advised to split the RGB channels, you can think of it by trying to identify a fictional cat with red ears,...


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After a quick scan, it would seem that, in the history of object detection, machine learning has always been at the forefront. Before then, it would just be a heuristic approach. For a quick answer, here: https://towardsdatascience.com/real-time-object-detection-without-machine-learning-5139b399ee7d That goes over object detection without using machine ...


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Apart from the multitudes of traditional image segmentation techniques (Watershed, Clustering or Variational methods), newer Segmentation schemes using Deep Learning are actively being used, which provide better results and are better for real-time applications, owing to minimum computation overheads involved. The following blog provides a detailed review ...


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(Of course, similar questions have been asked in the past and there are many sites, papers, video lessons, online that explain how CNNs work, but I think it's still a good idea to have a reference answer that hopefully will give you the main ideas behind CNNs.) A convolutional neural network (CNN) is a neural network that performs the convolution (or cross-...


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This is a very hard problem, you have many overlapping points with objects which aren't completely round. I'm not very knowledgeable on CV but I suspect you will find it very challenging. I would probably say a handcrafted detection algorithm would probably be easier, something like an edge detector which fit circles to arcs and labeled the points. But it's ...


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You can find the dataset in the following links: Pomegranate Disease Detection Using Image Processing fruits 360 datasets


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The CNN should work without trying to do special feature extraction. As pointed out some pre-processing can aid in enhancing the CNN's classification results. The Keras ImageDataGenerator provides optional parameters you can set to provide pre-processing as well as provide data augmentation. One thing I know that works for sure but can be painful is ...


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The whole interest of using deep learning-based solutions is that you don't have to do all those pre-processings, i.e. binarization, segmentation of background. CNNs, such as YOLO or FasterRCNN, can learn how to retrieve that information by themselves.


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Some excerpts from Nutella 'Hired' an Algorithm to Design New Jars. And It Was a Sell-Out Success: The "algorithm" is called HP Mosaic and is included free in HP SmartStream Designer for HP printers. More about how the algorithm works here: https://www.linkedin.com/pulse/hp-mosaic-20-steven-chow HP Mosaic takes the vector PDF file as input (also known ...


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Perhaps you are getting checkerboard artifacts Explained here, solutions involve changing the kernel and stride size to prevent them from being not divisible. Besides that, a solution could be to apply Gaussian smoothing to minimize the artifacts. For example, using Gaussian smoothing in OpenCV with your image results in import cv2 img = cv2.imread('s.png') #...


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If we seek proven working source code to plug into a GPLv2-licence compatible solution, we should at least consider autotrace. Its source code is open for review. It can be tested against the example images we have and, if it works fine, called by our GPLv2 software. We can even use the calling code in Inkscape's plug-in image tracing implementation as a ...


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As a rule of thumb for image data augmentation, look at the augmented images: Can you correctly classify or measure your target label from the augmented images? Could something similar to the augmented images appear in the environment where you want to run inferences on previously unseen inputs? For your suggested augmentation of shuffling the channels, it ...


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For a simulation of SNES or N64, the resolution is probably not that high. You can use either online credits or buy new hardware. For online credits, it is recommended if you do teh simulation/training for only several dozens hours, as eache hour costs around 5-10 dollars. AWS is a good choice as it have a large variety of choice for hardware. For higher ...


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Your hardware choice depends majorly on the sensors and camera specifications you need for your solution. For example, we are running a 2536x1920 resolution 20fps camera to detect cars and then read their number plates. Nvidia's RTX 2080Ti 11 GB can handle 2 such cameras at 20fps (real-time). However, this involves 2 models: 1 for detection of plates and a ...


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One of the key terms in the literature that you are looking for is video captioning. You can have a look at some of the relevant papers with code on this subject. In short, it is an active area of research and a difficult problem, one reason is because videos are still difficult to learn about (because of larger amount of data + larger model, etc...) and ...


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The model (that I know of) which most resembles your description is the auto-encoder, which is trained to learn a compact representation (a vector) of the input, which can later be used to reconstruct the original input. In a certain way, this compact representation (implicitly) encodes the most important features of the input. In particular, you may be ...


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If you have a $h_i \times w_i \times d_i$ input, where $h_i, w_i$ and $d_i$ respectively refer to the height, width and depth of the input, then we usually apply $m$ $h_k \times w_k \times d_i$ kernels (or filters) to this input (with the appropriate stride and padding), where $m$ is usually a hyper-parameter. So, after the application of $m$ kernels, you ...


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As far as I know, more than 3 channel is perfectly fine, since, 3 channels are what we use for images and that's enough since we can only see this many colors, but I don't see why more than that wouldn't work Your 2nd question is like asking whether or not you will be good at a sport... Just try it For your 3rd question, I've never seen any language AI ...


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You can use cv2.bitwise_and and pass rectangle as a mask. im = cv2.imread(filename) height,width,depth = im.shape cv2.rectangle(img,(384,0),(510,128),(0,255,0),3) cv2.rectangle(rectangle,(width/2,height/2),200,1,thickness=-1) masked_data = cv2.bitwise_and(im, im, mask=rectangle) cv2.imshow("masked_data", masked_data) cv2.waitKey(0)


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