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Questions tagged [computer-vision]

Computer Vision is a subfield of computer science which deals with analyzing and understanding images. This includes detection of objects like faces in images or segmenting images.

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computer vision raspberry pi

Would anyone know what a good 'side view' person detection algorithm that raspberry pi can handle? I am utilizing haar cascades to create a region of interest and I can get the ...
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What architecture would best perform image material segmentation?

I want to perform semantic/stuff segmentation, but then classify and segment with respect to the material properties of objects in an image, rather than the objects themselves. This means that, ...
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A noise robust Local binary patterns variant

I’m thinking of using Local Binary Patterns (LBP) to extract features from MRI images of brain tumours to build a module for classification, due to its computational simplicity and good performance, ...
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1answer
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Pre-processing on MRI images

I have MRI images of brain tumors collected from a hospital (not a benchmark dataset). And I am planning to use them to predict/classify tumour types using a typical machine learning approach: texture ...
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What is the most efficient method to detect drowsiness?

What all parameters other than face detection, speed and steering variations, yawning frequency can be used to detect drowsiness? What method is more efficient in drowsiness detection? What ...
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What is the difference between SSIM and MS-SSIM?

I would like to know what is the difference between SSIM and MS-SSIM? Also, there is a built-in function in Tensorflow for both of them, I am curious to know when should I use SSIM and when MS-SSIM? ...
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how to apply similarity algorithm(or comparision) of over one million vectors with other one million vectors?

how to apply similarity algorithm(or comparison) of over one million vectors with other one million vectors Please help I am a beginner in this field I am following this pyimage search tutorial but ...
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Machine learning PhD Interview technical questions [closed]

I'm Software Engineer who applied to grad school for Machine Learning/Computer Vision PhD and currently waiting for interview calls. I'm brushing up Linear algebra/ ML topics. What kind of technical ...
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Uniformity of color and texture in an image

I am new to the field of deep learning and have a problem in determining whether two images have uniform color and texture. For example, I have a Master image - Now, with respect to this image i ...
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Mask RCNN: Random predictions during inference for the same image

I recently trained the Mask RCNN (matterport's implementation) on some satellite images, but during inference mode, I'm getting random predictions for the same set of weights for the same image. That ...
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2answers
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Analyzing Videos using Deep Learning

Is there any work done on analyzing sequence of frames from a video using Deep Learning techniques? By "analyzing" I mean like memorizing them in order to classify or predict something (e.g. by ...
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1answer
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Keypoint matching using HoG and SIFT

I have two images and I've found their keypoints using sift keypoint detector, Now I have to match their keypoints with HoG features, I know how to extract HoG description, but I dont know how to ...
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Fluctuating Learning rate in tensorboard

I am trying to train an faster rcnn for object detection. While I am trying to fine-tune the object detection network I am seeing my learning rate is fluctuating even though I didn't change it. The ...
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Recognize polygons and get coordinates on transparent image

I have to implement a program to recognize polygons on transparent images, like this: So, in this picture we have 4 main polygons, we need to recognize them with a blue background and more dark blue ...
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Using tensorflow object detection in another model

I am trying to use Tensorflow (tf) object detection API models in another custom model I built. Specifically, I am trying to do: jointly train tf object detection models Y with another model X. in a ...
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How and why does YOLO fail to localize well?

In the paper 'You Only Look Once:Unified, Real-Time Object Detection' there are a lot of points suggesting that YOLO faces localisation errors but there is no mention of how or why it fails. I want to ...
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Sub-Object Attention models

Questions first: I need help to focus myself on the most relevant attention model papers (Attention to Attention if you will). Where should I start? Have you heard of attention models that focus on ...
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ResNet10 300x300 ssd architecture

I found good model for OpenCV on Caffe https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py Can smbd advice links or articles about how to make such model ResNet10 ...
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1answer
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MR images segmentation for feature extraction

I have datasets of brain MR images with tumours, the tumours are already selected manually by a physicist using Image J. I have read about segmentation, but I still couldn't understand how do they ...
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1answer
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Feature selection/reduction techinique for combination of features in image processing

I have a combination of features extracted from 3 descriptors, namely GLCM based feaures(correlation, homogeneity,energy and contrast ), Local binary patterns (256) and discrete wavelet transform ...
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segmentation of brain tumor in MRI images

I have a dataset of brain tumours images. and I have to build a model to classify the malignancy grade of these tumours. The size of the tumours varies from small to large. The ROI are already ...
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Understanding YOLO Loss Function

In the following formulation, if xi and yi represent center of anchor box, shouldnt we have ...
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1answer
58 views

cross validation for small dataset

I have a dataset of 39 medical MR images, and I have to build a model to classify the tumor type. so is it suitable to use k-fold cross validation for validating the model? if so, what would be the ...
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1answer
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How would you a apply a cnn to do age estimation on static images? [closed]

After doing some reading on age estimation using the IMDB wiki dataset I wanted to try it out myself on a smaller scale but I dont quite understand the application of the CNN. Any clarification would ...
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working on different size of images for CAD system

Is it possible to work on image dataset of different sizes for CAD system using supervised learning modules, i.e., I have a dataset of brain tumour images with a different size of tumours, they are ...
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Literature Resources on Object Detection

I would like to learn as much as I can about object detection, including the history and math behind it. My plan is to build up my knowledge all the way to state of the art object detection techniques ...
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2answers
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Autonomous evalution

Do you think it is possible to learn the app, how to autonomous evaluate good or bad parking of the bikes? The thing is you need to take a picture with your phone and app need to decide according to ...
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Equivariance vs Invariance in Convolutional Neural Networks

Could someone please explain to me in details (possibly from mathematical point of view) what is the role of Equivariance and Invariance in Convolutional Neural Networks, and how are they actually ...
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OCR on striked-out text

I have the following image with me: I want to identify the text, which is the amount mentioned at the bottom of the table. However, the bottom edge of the table goes through the text in this case. ...
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how can I get the original pixels that lead to the decision in CNN ? is that possible?

I work on medical images, I want to locate the most relevant regions of the image based on deep learning spatially CNN, so I feed my data into VGG16 architecture, I get the features maps, now I want ...
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1answer
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YOLO pretraining

I'm implementing YOLO network and have some questions. In the original paper the authors say: "For pretraining we use the first 20 convolutional layers from Figure 3 followed by a average-pooling ...
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Training detector without bounding box data

From what I can see most object detection NNs (Fast(er) R-CNN, YOLO etc) are trained on data including bounding boxes indicating where in the picture the objects are localized. Is there any model ...
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1answer
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YOLO layers size

According to the original paper, the input size of the YOLO network layer is 448x448x3 and after the filter (7x7x64-s-2) is applied the output shape is to be 221x221x192 as I suppose. Some sources ...
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Tensorflow object detection API issues

I use Tensorflow object detection API, and have 2 questions: Where can i find all data augmentation parameters that available for config file? When I train model on my own dataset, i see loss metric ...
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How to create a GAN which identifies bald patches and makes it look hairy?

I am a Data Science newbie. I want to create a GAN which highlights bald patches in images like these: I want the GAN to locate the patch and then put black dots (like hair roots) on that area. ...
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1answer
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Bounding box coordinates prediction

How to pass multiple bounding boxes coordinates to CNN model?My goal is to predict the coordinates of texts in an image.
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How to train object detection system for 2 classes having two seperate datasets for each class?

I have dataset of class A and a dataset of class B. However dataset A does not contain annotated class B and vice-versa. Is there a way to somehow train object detection system like SSD to detect ...
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Examples for multi-input Convolutional Neural Network

I want to create a multi inputs Convolutional Neural Network (cnn) that takes two inputs and produces one output of the inputs class by using Keras. I searched for resources that explain multi inputs ...
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1answer
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How to visualize image segmentation results

I am using u-net to do semantic segmentation for N>1 classes. The input size is (128,128,3), the output size will be (128,128,N). what is the correct way see the prediction as an image ot size n1 x n2 ...
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1answer
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What exactly does the model generation mean in this diagram?

I've been trying to grasp a research paper on image colorization using neural networks here I am stuck at this diagram. What I need help on, is the Model Generation step after Feature extraction. ...
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Bounding Boxes in YOLO Model

The YOLO model splits the image into smaller boxes and each box is responsible for predicting 5 bounding boxes. My question is how does the model make these bounding boxes for every grid cell ? Does ...
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Convolutional neural network for gray images

I am using vgg16 to design a CNN that takes gray input images. The model give me good results without changing anything related to colors. I am not sure if what I did is correct or not. I want to ...
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Deep Learning ROC and Average Precision Curve Results

I used Vgg16 to create a deep learning model and the dataset is imbalanced so, I used class_weight argument in fit_generator method. The model result as the following: accuracy= 98.9% and loss= 0....
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How exactly is equivariance achieved in capsule networks?

I have read quite a lot about capsule networks but cannot understand how the squashed vector would also rotate in response to rotation of the image.A simple example would be helpful.I understand how ...
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Do capsule networks have to be trained on different poses of an entity for them to work?

I have read about capsule networks and have failed to understand the following. A capsule network can identify objects at different poses(affine transforms) via its instantiation parameters.But my ...
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1answer
525 views

How to properly save and load an intermediate model in Keras?

I'm working with a model that involves 3 stages of 'nesting' of models in Keras. Conceptually the first is a transfer learning CNN model, for example MobileNetV2. (Model 1) This is then wrapped by a ...
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it is possible to use features maps of CNN to localised important areas in image?

I'm new in deep learning and CNN, I understand how convolutional and pooling layers work, I understand how and why feature maps are created. How I can localize from the feature maps important area in ...
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1answer
328 views

How to make two parallel convolutional neural networks in Keras?

I created two convolutional neural networks (CNN), and I want to make these networks work in parallel. Each network takes different type of images and they join in the last fully connected layer. ...
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1answer
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How to fetch text from pdf to further proceed with question answer based model from the same document?

To illustrate the above title. Suppose you have a pdf document, which is basically scanned from hardcopy, now there are set of fixed questions to answer from the document itself. For an example a ...
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Why is network in network architecture significant?

When talking about convolutions, we have seen networks carrying out the network in network architectures (1x1 convolutions), What is the significance of this process and how does it affect the network ...