Vision plays a vital role in both the biological world and in humans. With the rise of artificial intelligence, intelligent vision, including machine vision and computer vision, is playing an increasingly important role in the field of artificial intelligence.
Intelligent vision involves many fields such as psychophysics, computer science, image processing, pattern recognition, and neurobiology. It mainly refers to the technology of using computers to simulate or reproduce certain intelligent behaviors related to humans. Objectively speaking, it is the process of extracting information from images of things, processing and understanding it, and finally applying it to actual production and life.
Therefore, image analysis is the most important part of intelligent vision. Image analysis and image processing are closely related and have some overlap, but they are also different.
Image analysis focuses more on studying the content of an image, including but not limited to using various image processing techniques. It tends to analyze, interpret, and recognize the content of an image. Image processing, on the other hand, focuses on signal processing research, such as adjusting image contrast, image coding, denoising, and filtering.
Image analysis is more closely related to pattern recognition and computer vision in the field of computer science. In general, image analysis typically uses mathematical models combined with image processing techniques to analyze low-level features and high-level structures, thereby extracting information with a certain degree of intelligence.
Image analysis requires us humans to teach computers to recognize objects. We feed a large number of images of a type of object to the computer and let it recognize them. Then we build simple geometric models based on the different characteristics of different objects, such as combinations of rectangles, triangles, circles, etc., so that the computer can better recognize different objects.
However, the actual results deviated significantly from the above principles because the same type of object often has different shapes in the real world. For example, a cup can be cylindrical, cubic, irregularly shaped, etc. If we were to perform image analysis according to the above principles, we would need to design corresponding models for all cups to teach the computer how to recognize this simple everyday object, which is clearly an impossible task.
So scientists later drew inspiration from how children learn. When parents teach their children about "cups," they don't teach them how to build a geometric model of a cup; children learn to recognize what a "cup" is through experience. Therefore, scientists used machine learning to address this problem, and a crucial technique in this approach is the "convolutional neural network."
A convolutional neural network (CNN) is a multi-layered neural network. Its biggest difference from other deep learning networks is that it has convolutional layers that can directly convolve with two-dimensional data. The advantage of CNNs is their ability to directly convolve with image pixels, extracting image features from them. This processing method is closer to how the human brain's visual system processes information.
The basic structure of a convolutional neural network (CNN) can be divided into four parts: input layer, convolutional layer, fully connected layer, and output layer. In image analysis using a CNN, the image is first decomposed into partially repetitive small regions. Each small set of neurons in the CNN is connected to a small region of the input image; essentially, each small region is input into the neural network for recognition.
The advantage of this approach is that the sets are laid out with overlaps, and each layer in the network repeats the same process, so the network can tolerate a certain degree of distortion in the input image. Then, convolution is performed on the neighborhood of the input image to obtain the neighborhood feature map of the image, and then pooling is used to downsample the small neighborhood to obtain new features.
In this way, we reduce an image to a smaller sequence, which is then fed into another fully connected neural network that determines whether the images match. Therefore, the entire process involves convolution, max pooling, and a fully connected neural network. Depending on the specific problem, we can determine the number of convolutions and max pooling operations. Increasing the number of convolutional layers helps identify more complex features, while using the max pooling function helps reduce the data size. In recent years, convolutional neural networks have been widely used in image analysis.
With the rapid development of technology, image analysis in the field of visual intelligence has become increasingly challenging. The emergence of convolutional neural networks has solved the problems that have arisen in traditional processing methods.
With the continuous development of artificial neural networks, the visual intelligence of artificial intelligence will become more efficient and accurate in the future. The ever-improving image analysis process will also bring huge advantages to the development of artificial intelligence. Therefore, we have every reason to believe that artificial intelligence will continue to bring surprises to mankind in the future.
