How about a SELF-DRIVING car? Or a doctor diagnosing diseases from an image? And that would be made possible with computer vision! But computers don’t “see” like we do — they see numbers. So, how do we bridge the gap?
The feature extraction is probably the part where you get to learn the most. It is like identifying the salient components of an image, that assist the computer in understanding what it is viewing. It’s similar to highlighting key words in a text. A successful feature extraction setup makes computer vision possible.
Feature Detection: the Basics of Computer Vision
Let’s talk features. So what are they in computer vision?
What Are Features in Computer Vision?
Papatos gasps at special details in pictures. They are the missing detail which makes you realize what it actually is. This can be edges, corners or shapes. You can think of these as the primary identifiers that make items distinct.
Low-Level vs High-Level features
There are two types of features: low-level and high-level features. Low-level features are basic things that are extracted directly from the pixels. High-level features are more abstract, learned concepts about what is in the image. These types of problems often require a more complex machine learning algorithm: deep learning.
The Classics: Classical Algorithms for Feature Extraction
Well, before fancy computers we did it the old-fashioned way. Or whatever: either one of those things would be helpful to know!
Edge Detection: Lokasi Boundary
Edge detection is seeing where something starts or where it stops. This is helpful, as edges reveal the shapes of objects. The two standard algorithms used to detect edges are Sobel and Canny.
Similar to a simple filter is the Sobel operator. It detects edges by searching for significant variations in brightness. It is simple to use, but it might be noisy.
The Canny edge detector is more complicated, but better. The first step is to blur the image to reduce noise. It then discovers the gradient, or direction of change, and finally it thins the edges. Lastly, it applies two thresholds to retain only the actual edges. This method is more precise — but it requires more computing power.
Detecting corners: Key Points
تجريد*>(1) : Corner detection finds points where edges meet This is useful for determining the orientation and position of an object. Common methods for corner detection include the Harris and Shi-Tomasi corner detectors.
The Harris corner detector is a mathematical approach to find points that are corners. It searches for spots where the image varies significantly in any direction. It works well, but is sensitive to scale and rotation changes.
Shi-Tomasi (correcting Harris) It’s more stable and responds better to changes in rotation. This can be useful for tracking objects.
Blob Detection: Seeing Areas of Interest
Blob Detection: Looking for regions that are different than their area. Blobs also can be circles, squares or other shapes. One of the blob detection methods is Difference of Gaussians (DoG) and Laplacian of Gaussian (LoG).
Difference of Gaussians (DoG) blurs the image twice, but with different blur amounts. Then, it takes the difference between the two blurred images. The result features blobs of various dimensions. It is quick, but it can also be very noise sensitive.
LoG: Laplacian of Gaussian (uses same filter to find blobs). It merges blurring and edge detection. It does well with locating blobs of some size.
Deep Learning-based Feature Extraction Techniques
Feature extraction is now changed with deep learning. Time went on, and now computers can learn features! The spotlight is on Convolutional Neural Networks (CNNs).
Convolutional Neural Networks (CNNs): Let feature learning take care of itself
As a special branch of the neural network types, the CNNs. They have layers that learn to extract features from images. We use convolutional layers with filters to detect patterns. Pooling layers reduce the features. Activation functions determine the salient features.
Convolutional layers are like filters. Every filter searches for a certain pattern like an edge or a curve. The filter then slides over the image and produces a feature map. The filter itself and how far it moves, known as the stride, determine what features it recognizes.
Pooling layers shrink the feature maps. Max pooling selects the largest value in each region. Average pooling simply takes an average of the values. This guides the network towards the most salient features.
Pre-trained Models: Transfer Learning as a Feature Extractor
Through transfer learning, we can use pre-trained models. VGG, ResNet, Inception, etc have learned a lot about Images. They can be used to extract features from our images.
VGG is a deep CNN with many depth layers. It’s good at object recognition. If you have a lot of data and a powerful model, you can use VGG.
ResNet is another deep CNN. It employs special connections to circumvent issues with vanishing gradients. This allows it to train its deeper networks. In conclusion, ResNet is effective for complex tasks in which you need to extract highly fine-grained features.
Feature Selection and Dimensionality Reduction: Reducing the Number of Features
Sometimes we have to many features. This is where feature selection and dimensionality reduction come in to help us choose the best ones. It makes the model faster and more accurate.
We got you covered for Feature Selection : Which Features You Should Choose
Feature selection connotes choosing a subset of the original features. That means discarding the useless ones, and pare down to only the significant features. There are several methods of selecting such as filter methods, wrapper methods and embedded methods.
Statistics directly tries to score features → Filter. Each features scored and based on scoring it selects features. This means that filter methods, while fast, can be less effective at finding the critical combinations.
Wrapper methods visit different combinations of features. They evaluate each combination using a machine learning model. They are precise but very time-consuming.
Feature Extraction — Dimensionality Reduction
Dimensionality reduction makes changes to the features. It extracts new features that contain essential information. These methods include Principal Component Analysis and t-distributed Stochastic Neighbor Embedding.
Polynomial Components Analysis (PCA) retrieves the base orientations of difference within a given data set. It uses these directions to project the data. This preserves the most important information while reducing the number of features. While PCA is fast, it may fail with non-linear data.
T-distributed Stochastic Neighbor Embedding (t-SNE) used for high-dimensional data visualization. It associates each point with a point in a lower dimensional space. It aims to cluster similar points near to one another. This helps identify clusters in the data.
Featured Extraction in Practice: Applications of Computer Vision
Feature Extraction is used in a lot of different domains. Let’s look at some examples.
This already exists as a service.
Feature extraction is a technique used in computing to detect and classify images. It involves saying what is in the image and labeling it. ImageNet challenge demonstrated the serial importance of feature extraction. Well defined feature extraction processes are crucial to top performing algorithms.
In surveillance systems, feature extraction is used as well. It aids in identifying objects such as humans or vehicles. It can help automatically accomplish basic tasks — like counting how many customers visit a shop every day.
Object Detection and Tracking
Feature extraction by searching and tracking an object in videos. Autonomous vehicles rely on it to recognize pedestrians and traffic signals. It is also used in video surveillance to track objects.
Medical Image Analysis
Medical images such as X-rays or MRIs help doctors identify diseases by using features. It is used in detecting cancer, and in analyzing bone fractures. Medical diagnosis is more efficient and faster due to feature extraction.
Conclusion
Feature extraction is the center of importance in computer vision. It is used for image recognition by identifying important feature. There are many methods to extract features from classic to deep learning. Features like these power image recognition, object detection and medical diagnosis. With the development of technology, feature extraction will also become more sophisticated, leading to new opportunities. And now you know how computers know how to “see”. So what will you do with this information?
