Computer Vision Techniques
We can see, process, understand, and act on anything we can see or any visual input as humans; in other words, we can see and understand any visual data. But how can we do the same thing with machines? As a result, Computer Vision enters the picture.
Although machines still have some limitations in their ability to visualize the same way humans do, they are getting closer to analyzing, comprehending, and extracting meaningful information from any visual input. Computer vision is currently one of the most popular research areas for deep learning.
In this topic, we will gain a thorough understanding of various computer vision techniques currently in use in various applications. However, before we begin, let us first understand the fundamentals of computer vision.
What exactly is computer vision?
Computer vision is the study of enabling computers to interpret and comprehend visual information from the outside world, such as images and videos. Examples of such tasks are image recognition, object detection, and image segmentation. Algorithms for computer vision can be used in various applications, including self-driving cars, robotics, and medical imaging.
Deep learning’s hottest research field right now is computer vision. It applies to many academic disciplines, including computer science, mathematics, engineering, biology, and psychology. A relative understanding of visual environments is represented by computer vision.
As a result of its cross-domain expertise, many scientists believe the field paves the way for Artificial General Intelligence. Recent advances in neural networks and deep learning approaches have greatly improved the performance of cutting-edge visual recognition systems.
Computer Vision Techniques
Here are five of the most popular computer vision techniques.
1. Image Classification
Image clarification involves challenges such as viewpoint variation, scale variation, intra-class variation, image deformation, image occlusion, lighting conditions, and background clutter. Researchers in computer vision have developed a data-driven method for categorizing images. They give the computer a few examples of each image class and help to improve learning algorithms. It examines the bars to learn about their visual appearance. In short, they create a training dataset of labeled images and then feed it to the computer, which then processes the data.
Convolutional Neural Networks (CNNs) are the most well-known image classification architecture. A typical use case for CNNs is to feed the network images and let the network categorize the data. CNN’s typically begin with an input “scanner” that isn’t designed to parse all training data simultaneously. For example, to input a 100100-pixel image, one would not want a layer with 10,000 nodes.
Example:
import cv2
import numpy as np
# Load the pre-trained model
model = cv2.dnn.readNetFromCaffe("model.prototxt", "weights.caffemodel")
# Load the image
img = cv2.imread("image.jpg")
# Resize the image to the size expected by the model
img = cv2.resize(img, (224, 224))
# Convert the image to a blob
blob = cv2.dnn.blobFromImage(img, 1, (224, 224), (104, 117, 123))
# Pass the blob through the network
model.setInput(blob)
output = model.forward()
# Get the class with the highest probability
index = np.argmax(output)
# Print the class label
print("Class label:", index)
In this example, a pre-trained deep learning model for image classification is loaded using the cv2.dnn.readNetFromCaffe() function. The image is resized to the size expected by the model, converted to a blob, and passed through the network using the model.setInput() and model.forward() functions. The class with the highest probability is obtained using the np.argmax() function
2. Object Detection
Identifying objects within images entails producing bounding boxes and labels for individual items. It differs from the classification task in that it applies classification and localization to many objects rather than a single dominant object. Object classification is divided into two categories. The first is object bounding boxes, and the second is non-object bounding boxes.
For example, vehicle detection requires identifying all vehicles in a given image, including two-wheelers and four-wheelers, using their bounding boxes. If we use the Sliding Window technique to classify and localize images, we must apply a CNN to different image crops because CNN classifies each crop as an object or a background. Then we must apply CNN to many locations and scales, which is very computationally expensive.
An example of object detection using computer vision is using a Convolutional Neural Network (CNN) to identify objects in an image. This can be trained on a dataset of images labeled with the objects present, such as a “person,” “car,” “dog,” etc. The CNN can then be used to detect these objects in new images.
3. Instance Segmentation
For example, Segmentation entails different models of classes, such as labeling five cars with five different colors. The task in classification is to identify an image with a single object as the focus. We see complex scenes with multiple overlapping objects on different backgrounds. These other objects are not only classified, but their boundaries, differences, and relationships to one another are also detected.
An example of instance segmentation using computer vision is using a Mask R-CNN (Regional Convolutional Neural Network) algorithm to identify and segment multiple instances of different objects within an image. This algorithm extends Faster R-CNN by adding a parallel branch for predicting an object mask in addition to class and box predictions. The network is trained on a dataset of images with corresponding object masks, and can then be used to predict the masks for objects in new images.
4. Object Tracking
Following a specific object of interest or multiple items is called object tracking. It has traditionally been used in video and real-world interactions where observations are made after initial object detection. According to the observation model, it can be divided into two categories.
The generative method, for example, employs a generative model to describe the visible characteristics. Furthermore, the discriminative method can be used to distinguish between the object and the background. Its performance is more stable, and it is gradually replacing traditional tracking methods.
An example of object tracking using computer vision is using a algorithm such as the Kalman Filter, CamShift, or Optical Flow. These algorithms use information from previous frames in a video stream to predict the location of an object in the current frame, and track its movement over time. The object’s appearance or features may also be used to confirm or refine the object’s location in each frame. This can be used for a variety of applications, such as tracking a moving vehicle in a traffic surveillance system or tracking a player in a sports video.
5. Semantic Segmentation
Computer vision is the process of segmenting whole images into pixel groups that can be labelled and classified. Semantic Segmentation attempts to understand the function of each pixel in a flash. For example, if we choose a landscape with people, roads, cars, and trees, we must define the boundaries of each object. In contrast to classification, we require dense pixel-wise predictions from the models.
An example of semantic segmentation using computer vision is using a Fully Convolutional Network (FCN) to label every pixel in an image with a corresponding class label, such as “sky,” “person,” “road,” etc. This can be trained on a dataset of images with corresponding pixel-level annotations, and then used to perform semantic segmentation on new images. The output is an image with each pixel colored according to its predicted class label, allowing for a more fine-grained understanding of the scene than object detection or instance segmentation.
Conclusion
Computer vision techniques are a rapidly advancing field that is driving innovation in a wide range of industries, from healthcare to self-driving cars. The ability to process, analyze, and understand visual data is becoming increasingly important as we generate more and more visual data.
Computer vision techniques such as object detection, image segmentation, and image recognition allow machines to understand and interpret visual data in the same way that humans do. These techniques have been applied in various fields such as self-driving cars, surveillance systems, healthcare, and manufacturing.
Deep learning-based approaches have greatly improved the performance of computer vision techniques by providing more accurate and robust results. The use of convolutional neural networks (CNNs) and other deep learning architectures have led to state-of-the-art performance in tasks such as image classification, object detection, and semantic segmentation.