A Classifier which detects hand in images... I am doing skin based detection for detecting hand ...
Blockquote
"but i need that the hand would be detected without skin based color"
If you want to detect without skin based color, you can train your own model with neural nets. If you don't know or how to train neural nets, I recommend that to look Cascade Classifier Training . To train your own model with both methods, you need to hand images dataset. There are many hand dataset on the internet.
Related
Is my classification correct?
LeNet-5: Image classification,
AlexNet: Image classification,
VGG-16: Image classification,
ResNet: Image classification,
Inception module: Image classification,
MobileNet: Image classification,
EfficientNet: Image classification,
Neural Style Transfer: Image generation,
Sliding Windows Detection algorithm: Object detection,
R-CNN: Object detection,
YOLO: Object detection,
Siamese network: Image recognition,
U-Net: Semantic segmentation
If wrong, please correct me. THANKS!
Your classification is correct if the purpose is - why they were invented initially. However rather than the task based taxonomy, CNNs are better studied on the basis of what different they are doing. Initially CNNs were designed for image classification, but the same network works for Object detection with slight modifications in last layer. For example, Faster RCNN (designed for Object detection) can use any of the architecture designed for classification such as VGG, ResNet etc (link). Similarly Faster-RCNN can be modified to do segmentation task in Mask-RCNN architecture (link).
Here is a chart showing evolutionary history of deep CNNs showing architectural innovations (source)
Here is another taxonomy showing different categories based on architecture style.
I have written an object classification program using BoW clustering and SVM classification algorithms. The program runs successfully. Now that I can classify the objects, I want to track them in real time by drawing a bounding rectangle/circle around them. I have researched and came with the following ideas.
1) Use homography by using the train set images from the train data directory. But the problem with this approach is, the train image should be exactly same as the test image. Since I'm not detecting specific objects, the test images are closely related to the train images but not essentially an exact match. In homography we find a known object in a test scene. Please correct me if I am wrong about homography.
2) Use feature tracking. Im planning to extract the features computed by SIFT in the test images which are similar to the train images and then track them by drawing a bounding rectangle/circle. But the issue here is how do I know which features are from the object and which features are from the environment? Is there any member function in SVM class which can return the key points or region of interest used to classify the object?
Thank you
i'm working on a project (using opencv) where i need to accomplish the following:
Train a classifier so that it can detect people in an thermal image.
I decided to use opencv and classify with HOG and SVM.
So far, i have gotten to the point where i can
Load several images, positive and negative samples (about 1000)
extract the HOG Features for each image
Store the features with their label
Train the SVM
Get the SVM Settings (alpha and bias), set it as HOG Descriptor's SVM
Run testing
The Testing is horrible, even worse then the original one with
hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());
I think i'm doing the HOG Features wrong, bc i compute them for the whole image, but i need them computed on the image part where the person is. So i guess, that i have to crop the images where the Person is, resize it to some window size, train the SVM on classifing those windows and THEN pass it to the HOG Descriptor.
When i test the images directly on the trained SVM, i have observed, that i get almost 100% false positives. I guess this caused by the problem i described earlier.
I'm open for any ideas.
Regards,
hh
After obtaining the image dataset, the feature database is constructed for all images which is a vector based on mean and sd of RGB color model and HSV color model for a portion of the image. How can I use a svm to retieve related images from the database once the query image is given.
Also how to use unsupervised learning for the above problem
Assuming the query images are unlabeled, applying SVM would require a way of knowing the labels for dataset images since SVM is a form of supervised learning, which seeks to correctly determine class labels for unlabeled data. You would need another method for generating class labels, such as unsupervised learning, so this approach does not seem relevant if you only have feature vectors but no class labels.
A neural network allows for unsupervised learning with unlabeled data, but is a rather complex approach and is the subject of academic research. You may want to consider a simpler machine learning approach such as k-Nearest Neighbors, which allows you to obtain the k closest training samples that are similar in your feature space. This algorithm is simple to implement and is found in many machine learning libraries. For example in Python you can use scikit learn.
I am unsure what type of images you are working with, but you might also want to explore using feature detector algorithms such as SIFT rather than just pixel intensities.
I need to annotate frontal (or near frontal) images using openCV. I'm currently going through the OpenCV manual and the book "Mastering OpenCV". This is the first time I'm using OpenCV and due to that I'm little bit confused with annotation and face detection.
I need to mark about 25 points in the human face. The required points are there in eyes, mouth, nose, eyes, ears .My question is :
Is it necessary to detect the face first, and then eyes, eyebrows, mouth, nose, ears. Is it the case that then only I can proceed with annotation. The reason why I'm asking this is that I'll be doing the annotation manually. So that, obviously I can see where the face is and then eyes, nose etc. I don't see the point of detecting the face first.
Can someone explain whether face detection is really needed in this case ?
According to the book "Mastering openCV" , I need to do the following step-by-step.
(1) Loading Haar Detector for face Detection
(2) Grayscale colour conversion
(3) Shrinking the image
(4) Histogram Equalization
(5) Detecting the face
(6) Face preprocessing to detect eyes, mouth, nose etc.
(7) Annotation
Face detection allows a computer algorithm to search an image much much faster for the features like eyes & mouth.
If you are annotating the image yourself then it is of course much quicker just to annotate the wanted features and ignore unwanted ones.
No, You don't need to annotate landmarks for face detection, Opencv provide you by some functions to detect faces, using some already trained models using Haar Cascades classifiers, prepared in opencv package as xml files, you just need to call them as explained here
Annotation of images by some predefined landmarks is used to detect facial expression, and some facial details as estimation of head pose in the space, for these purposes AAM, ASM models are used.
As well, annotating images is a step to train a model, for that you may use a lot of universal annotated databases, available on internet, whereas your test images don't need to be annotated