For humans, describing what we see with our eyes—the "visual world"—often seems too simple to be noticed. Yet, this is exactly what we do every moment of the day. When we look at something—a car, a tree, or a person—we instinctively name it without much thought. However, for computers, distinguishing between "human objects" and "non-human objects" like dogs, chairs, or alarm clocks is a complex challenge.
Solving this issue brings immense benefits. "Image recognition" technology, more broadly known as "computer vision," serves as the foundation for many emerging technologies. From self-driving cars and facial recognition software to less obvious but equally important advancements such as "smart factories" that monitor pipeline defects and automation tools used by insurance companies to process and classify claim photos—these innovations rely heavily on image recognition.
In the following sections, we will explore the challenges of image recognition and examine how scientists use a special type of neural network to tackle them.
Learning to "see" is a difficult and costly task. One approach is to apply metadata to unstructured data. In previous articles, we discussed the challenges of text classification and search in environments where metadata is either scarce or missing. Manually classifying movies and music is already a daunting task for people, but some tasks are not only hard but nearly impossible. For example, training a driverless car's navigation system to distinguish pedestrians from vehicles, or labeling and categorizing thousands of user-uploaded images and videos on social media daily.
The only viable solution is using neural networks. While conventional neural networks can theoretically analyze images, they are computationally expensive. For instance, even processing a small 30x30 pixel image would require 900 inputs and over 500,000 parameters. This might be manageable for powerful machines, but when dealing with larger images, like 500x500 pixels, the number of parameters becomes impractical.
Additionally, using neural networks for image recognition may lead to another problem: overfitting. Overfitting occurs when a model becomes too closely tailored to its training data, reducing its ability to generalize to new data. This often increases computational costs and decreases accuracy.
The real solution lies in convolution. Fortunately, modifying the structure of neural networks leads to more efficient image processing. These modified networks are called "convolutional neural networks" (CNNs). Unlike traditional neural networks, CNNs are specifically designed for image processing, sacrificing some general adaptability for better performance in this domain.
Images have strong spatial correlations, meaning nearby pixels are more related than distant ones. Traditional neural networks treat each pixel independently, increasing computational burden. CNNs solve this by limiting connections, allowing neurons to focus on small image segments, similar to how the human brain processes visual information.
The secret behind CNNs lies in two key layers: the convolutional layer and the pooling layer. Let’s take an example: determining if a photo contains a "grandmother." The first step is the convolutional layer, which breaks the image into overlapping 3x3 pixel tiles. Each tile is processed by a simple neural network, producing feature maps. These maps are then arranged into a 3D numerical representation, capturing height, width, and color.
Next comes the pooling layer, which reduces the spatial dimensions while retaining important features. This minimizes computation and prevents overfitting. Finally, the processed data is fed into a fully connected neural network, which makes the final decision—how confident the system is in identifying a grandmother in the photo.
This is just a simplified overview. In reality, CNNs are far more complex, often involving hundreds or even thousands of layers.
Implementing a convolutional neural network is time-consuming and resource-intensive. However, many APIs now allow organizations to perform image analysis without internal expertise. Google Cloud Vision, for example, uses TensorFlow and offers object detection, face recognition, OCR, and image search capabilities. IBM Watson Visual Recognition allows customization based on provided images, while Clarif.ai provides a REST API with customizable units for specific scenarios like weddings or food.
While these APIs suit common tasks, specialized applications may still require custom solutions. Fortunately, many deep learning frameworks, such as TensorFlow and Theano, help reduce the computational burden, allowing developers to focus more on model training.
LCD Module
According to the different backlight sources, LCDs can be divided into two types: CCFL displays and LED displays.
Misunderstanding:
Many users believe that liquid crystal displays can be divided into LEDs and LCDs. To some extent, this understanding is misguided by advertisements.
The LED display on the market is not a true LED display. To be precise, it is an LED-backlit Liquid Crystal Display. The liquid crystal panel is still a traditional LCD display. In a sense, this is somewhat fraudulent. nature! South Korea`s Samsung was once convicted by the British Advertising Association as violating the country`s advertising laws because its [LEDTV" LCD TVs were suspected of misleading consumers. For liquid crystal displays, the most important key is its LCD panel and backlight type, while the LCD panels of displays on the market generally use TFT panels, which are the same. The difference between LEDs and LCDs is only their backlight types: LED The backlight and CCFL backlight (that is, fluorescent lamps) are diodes and cold cathode lamps, respectively.
LCD is the acronym for Liquid Crystal Display, which means "liquid crystal display", that is, liquid crystal display. The LED display refers to a type of liquid crystal display (LCD), that is, a liquid crystal display (LCD) with LED (light emitting diode) as the backlight source. It can be seen that LCD includes LEDs. Corresponding to the LED display is actually a CCFL display.
(1) CCFL
Refers to a liquid crystal display (LCD) with CCFL (cold cathode fluorescent lamp) as the backlight source.
The advantage of CCFL display is good color performance, but the disadvantage is higher power consumption.
(2) LED
Refers to a liquid crystal display (LCD) that uses LEDs (light emitting diodes) as a backlight source, and generally refers to WLEDs (white light LEDs).
The advantages of LED displays are small size and low power consumption. Therefore, using LEDs as a backlight source can achieve high brightness while taking into account lightness and thinness. The main disadvantage is that the color performance is worse than that of CCFL monitors, so most professional graphics LCDs still use traditional CCFL as the backlight source.
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