Full explanation of the five core research tasks of computer vision: classification and recognition, detection and segmentation, human body analysis, 3D vision, video analysis

This post provides an in-depth look at the definition and main tasks of computational vision. The content covers technologies such as image classification and recognition, object detection and segmentation, human body analysis, 3D computer vision, video understanding and analysis, and finally demonstrates the application of unsupervised learning and self-supervised learning in computer vision.

The author, TechLead, has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and a billion-dollar AI revenue. Head of Product Development

I. Introduction

Computer Vision (Computer Vision) is a discipline that imparts human vision capabilities to machines. It covers multiple directions such as image recognition, image processing, and pattern recognition, and has become an important part of artificial intelligence research. This article will introduce in detail the definition, historical background and development of computer vision, and an overview of current application areas.

1.1 Definition of Computer Vision

Computer vision is not only a science that studies how to enable machines to understand and interpret the visual world, but also a technology that pursues to enable machines to have visual processing capabilities similar to humans. It analyzes digital images and videos to enable machines to recognize, track and understand objects and scenes in the real world. In addition, computer vision also includes in-depth research directions such as image restoration and 3D reconstruction.

1.1.1 Core Technology

Core technologies include but are not limited to feature extraction, target detection, image segmentation, 3D reconstruction, etc., through the combination of multiple technologies to achieve more complex visual tasks.

1.1.2 Application scenarios

Computer vision is widely used in many fields such as autonomous driving, medical diagnosis, and intelligent monitoring, and has promoted the rapid development of related industries.

1.2 Historical background and development

The development history of computer vision is rich and colorful. From the initial exploration in the 1960s to the deep learning technology revolution today, it can be divided into the following main stages:

1.2.1 1960s-1980s: Early stage

• Image Processing: Mainly focus on simple image processing and feature engineering, such as edge detection, texture recognition, etc.
• Pattern Recognition: Implementation of elementary tasks such as handwritten digit recognition.

1.2.2 1990s-2000s: The era of machine learning

• Feature Learning: Feature learning and object recognition has become more sophisticated and powerful through machine learning methods.
• Applications of Support Vector Machines and Random Forests: New solutions are provided.

1.2.3 2010s-Present: The Deep Learning Revolution

• Convolutional Neural Networks: The widespread use of CNNs has brought breakthroughs in computer vision.
• Combining Transfer Learning and Reinforcement Learning: Significant Progress on Computer Vision Tasks.

1.3 Overview of application fields

Computer vision has penetrated many industries, and its application is not limited to the field of science and technology, but affects our daily life more broadly.

1.3.1 Industrial Automation

Using image recognition technology to automatically perform product quality inspection and classification, improving production efficiency and accuracy.

1.3.2 Medical image analysis

Computer vision combined with deep learning for disease diagnosis and prediction has changed traditional medical methods.

1.3.3 Autonomous Driving

Computer vision plays a key role in autonomous driving, analyzing the surrounding environment in real time and providing accurate information for vehicle path planning and decision-making.

1.3.4 Virtual Reality and Augmented Reality

Creating an immersive virtual environment through computer vision technology provides a new way of experience in fields such as entertainment and education.

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2. Five core tasks of computer vision

Of course, technical depth and content richness are very important. Here's an improved version for what was provided:

2.1 Image classification and recognition

Image classification and recognition is one of the core tasks of computer vision, which involves assigning an input image or video frame into one or more predefined categories. This chapter will delve into the key concepts, technological evolution, recent research results, and possible future directions of this task.

2.1.1 Basic concepts of image classification and recognition

Image classification is the task of assigning images to a certain category, while image recognition goes a step further by associating categories to specific entities or objects. For example, a classification task might identify the presence or absence of a cat in an image, while a recognition task would distinguish between different cat species, from pet cats to wild leopards.

2.1.2 Early methods and technology evolution

Early image classification and recognition methods relied heavily on handcrafted features and statistical machine learning algorithms. The history of the development of these methods includes:

• Feature extraction: Use features such as SIFT and HOG to capture the local information of the image.
• Application of classifiers: Use SVM, decision tree and other classifiers to classify images.

However, these methods have limited performance in many practical applications due to the complexity of feature engineering and limited generalization capabilities.

2.1.3 Introduction and Innovation of Deep Learning

With the advent of deep learning, significant progress has been made in image classification and recognition. In particular, the introduction of Convolutional Neural Networks (CNN) has brought revolutionary changes to research and practical applications in the field.

Applications of Convolutional Neural Networks in Image Classification

Convolutional neural networks automatically learn image features through stacked convolutional layers, pooling layers, and fully connected layers, eliminating the need to manually design features. Here is an example of a simple CNN structure:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# 输出模型结构
model.summary()

Summarize

Image classification and recognition are the cornerstones of computer vision, and its technological evolution perfectly reflects the rapid progress of the entire field. From hand-designed features to complex deep learning models, this field not only demonstrates the powerful capabilities of computer vision, but also lays a solid foundation for future innovation and development. With the development of more advanced algorithms and hardware, we expect image classification and recognition to play a role in more scenarios in the future to meet people's growing needs.

2.2 Object Detection and Segmentation

Object detection and segmentation is at the heart of computer vision. It is not only about identifying objects in images, but also about precisely locating and segmenting those objects. The challenges involved in this field range from basic image processing to complex deep learning methods. This chapter will delve into the key concepts, mainstream methods and latest developments of object detection and segmentation.

2.2.1 Object Detection

Object detection requires not only identifying objects in an image, but also precisely determining their location and category. Its applications include face recognition, traffic analysis, product quality inspection, etc.

early approach

Early object detection methods mainly relied on handcrafted features and traditional machine learning methods.

• Sliding window: Combining manual features such as HOG, find objects at multiple scales and positions through sliding windows.
• SVM classifier: Usually combined with a sliding window, use an SVM classifier for object classification.

deep learning method

The advent of deep learning techniques has greatly advanced the field of object detection.

• R-CNN series: From R-CNN to Faster R-CNN, gradually evolved to achieve accurate detection of objects, especially in the use of region proposal network (RPN) and ROI pooling innovations.
• YOLO: YOLO (You Only Look Once) has attracted attention for its real-time detection capability of one forward pass.
• SSD: SSD (Single Shot Multibox Detector) detects objects of different sizes through multi-scale feature maps, and also has the advantage of real-time detection.

# 使用YOLO进行物体检测的代码示例
from yolov3.utils import detect_image

image_path = "path/to/image.jpg"
output_path = "path/to/output.jpg"
detect_image(image_path, output_path)
# 输出图片包括检测到的物体的边界框

2.2.2 Object Segmentation

Object segmentation tasks are more detailed and involve object analysis at the pixel level.

semantic segmentation

Semantic segmentation aims to assign each pixel in an image to a specific category without distinguishing between different instances of the same category.

• FCN: FCN (Fully Convolutional Network) is one of the pioneering works in semantic segmentation.
• U-Net: U-Net achieves accurate medical image segmentation through a symmetrical encoder and decoder structure.

instance segmentation

Instance segmentation further distinguishes different object instances of the same category.

• Mask R-CNN: Mask R-CNN adds an object mask generation branch based on Faster R-CNN to achieve instance segmentation.

Summarize

Object detection and segmentation combine aspects of image processing, machine learning, and deep learning, and are complex and multifaceted tasks in computer vision. It has a wide range of applications in autonomous driving, medical diagnosis, intelligent monitoring and other fields. Future research will focus more on cutting-edge challenges such as multi-modal information fusion, few-sample learning, and real-time high-precision detection, and continue to promote innovation and development in this field.

2.3 Human body analysis

Human body analysis is an important and active research field in computer vision, covering various tasks such as human body recognition, detection, segmentation, pose estimation, and action recognition. The research and application of human analysis has far-reaching impacts in many fields, including security monitoring, medical health, entertainment, virtual reality, etc.

2.3.1 Face recognition

Face recognition is not only a technique for locating faces in images, but also involves the verification and recognition of faces.

• Face detection: By using algorithms such as Haar cascade, accurately locate the position of the face in the image.
• Face verification and recognition: apply deep learning methods, such as FaceNet, to determine whether two faces belong to the same person, or to find matching faces from large databases.

2.3.2 Human Pose Estimation

Human pose estimation involves identifying the key joint positions and overall pose of the human body, and it has important applications in fields such as motion analysis and health monitoring.

• Single-person pose estimation: By identifying the key joints of a single human body, for example, using methods such as OpenPose.
• Multi-person pose estimation: For complex scenes, the key joints of multiple human bodies can be recognized at the same time.

# 使用OpenPose估计人体姿态的代码示例
import cv2
body_model = cv2.dnn.readNetFromTensorflow("path/to/model")
image = cv2.imread("path/to/image.jpg")
body_model.setInput(cv2.dnn.blobFromImage(image))
points = body_model.forward()
# points中包括了人体的关键关节信息

2.3.3 Action Recognition

Action recognition recognizes specific human actions or behaviors from images or videos.

• Sequence-based methods: Use RNN or LSTM to analyze a series of images to capture the temporal characteristics of motion.
• 3D convolution-based method: 3D CNN is used to analyze the spatio-temporal features in the video to obtain richer action information.

2.3.4 Human Body Segmentation

Human segmentation is the technique of separating the human body from the background and other objects.

• Semantic Segmentation: Separating the whole human body from the background without distinguishing individuals.
• Instance Segmentation: Further distinguishing different human instances, suitable for

2.4 3D Computer Vision

3D computer vision is not only an exciting research field, but also provides the basis for many practical applications, including virtual reality (VR), augmented reality (AR), 3D modeling, robot navigation, etc. This chapter will delve into the main concepts and methods of 3D computer vision.

2.4.1 3D reconstruction

3D reconstruction is the process of reconstructing a 3D scene from a set of 2D images. This process involves multiple complex techniques and algorithms.

stereo vision

Stereo vision is the estimation of depth information of a scene by comparing images from two or more cameras. This provides the basis for further 3D reconstruction.

Multiple View Geometry

Multi-view geometry is a method to reconstruct 3D structures by exploiting the geometric relationships of multiple views. Accurate 3D reconstructions can be achieved through the application of epipolar geometry and triangulation.

Point cloud generation and fusion

Point cloud generation and fusion methods such as SLAM (simultaneous localization and mapping) techniques can generate accurate 3D structures from multi-view images.

2.4.2 3D object detection and recognition

3D object detection and recognition involves not only identifying the class of an object, but also determining its orientation and pose in three-dimensional space.

2D image-based methods

These methods leverage 2D images and depth information for 3D inference, such as using 3D CNNs to recognize and localize 3D objects.

Point Cloud Based Methods

Some advanced methods, such as PointNet, directly process 3D point cloud data, which can achieve accurate detection and recognition in more complex scenes.

2.4.3 3D Semantic Segmentation

3D semantic segmentation involves segmenting a 3D scene into meaningful parts and assigning semantic labels to each part.

voxel-based method

Like 3D U-Net, these methods divide the 3D space into voxels and perform segmentation, providing powerful 3D segmentation capabilities.

Point Cloud Based Methods

Point cloud-based methods, such as PointNet, are able to directly process point cloud data to achieve accurate 3D semantic segmentation.

2.4.4 3D Pose Estimation

3D pose estimation involves estimating the position and orientation of objects in 3D space.

single view method

Estimating 3D pose from a single image, while challenging, is effective enough for some specific applications.

multi-view method

Combining information from multiple viewpoints for precise estimation provides a key technique for many advanced 3D vision tasks.

Summarize

3D computer vision is a field full of challenges and opportunities. From basic 3D reconstruction to complex 3D object recognition and semantic segmentation, research in this field has had a profound impact on many advanced technologies and applications. With the continuous advancement of hardware and algorithms, 3D computer vision will continue to drive the development of many cutting-edge technologies, such as autonomous driving, smart city construction, virtual and augmented reality, etc. In the future, we can expect more innovations and breakthroughs in this field.

2.5 Video Understanding and Analysis

Video understanding and analysis is an important branch of computer vision, which not only involves the recognition and interpretation of video content, but also includes the reasoning of spatio-temporal structure. Compared with single image analysis, video analysis can dig deeper into the continuity and inner connection of visual information, thus opening up a new field of computer vision.

2.5.1 Video classification

The purpose of video classification is to identify and label the overall content of videos, which can be further subdivided into different tasks.

• Short video classification: mainly focus on specific activities or scenes in the video, such as recognizing actions, expressions, etc. This task is widely used in social media content analysis, advertisement recommendation, etc.
• Classification of feature films: Analysis of the entire movie or TV series may involve recognition of emotions, styles, themes, etc. This technology can be used in recommender systems, content moderation, and more.

2.5.2 Action Recognition

Action recognition is the process of capturing specific actions or behaviors from video.

• 2D convolution-based methods: By capturing the continuity in the temporal dimension, such as using C3D models, it is suitable for short-term action recognition.
• 3D convolution-based methods: such as I3D models, better capture spatiotemporal information for more complex scenes.

# 使用I3D模型进行动作识别的代码示例
import tensorflow as tf
i3d_model = tf.keras.applications.Inception3D(include_top=True, weights='imagenet')
video_input = tf.random.normal([1, 64, 224, 224, 3])  # 随机输入
predictions = i3d_model(video_input)
# 输出预测结果
print(predictions)

2.5.3 Video Object Detection and Segmentation

Video object detection and segmentation integrates object detection, tracking and segmentation techniques.

• Object detection: Through timing analysis, combined with methods such as Faster R-CNN and optical flow, objects can be precisely located in video sequences.
• Instance Segmentation: Segment a single instance in a video in more detail. Application scenarios include medical imaging, intelligent monitoring, etc.

2.5.4 Video Summary and Highlight Detection

The purpose of video summarization and highlight detection is to extract key information from a large amount of video data.

• Keyframe-based methods: Representative frames are selected as summaries for quick browsing or indexing.
• Learning-based methods: such as using reinforcement learning to select highlights, applied to automatically generate replays of game highlights, etc.

2.5.5 Video generation and editing

Video generation and editing involves a higher level of creation and customization.

• Video style conversion: Different styles can be converted through neural style transfer technology.
• Content generation: For example, using GANs technology, new video content can be synthesized, which provides new possibilities for art creation and entertainment industries.

Summarize

As a multi-dimensional and multi-level field, video understanding and analysis not only promotes the progress of media and entertainment technology, but also shows a wide range of practical value in monitoring, medical treatment, education and other directions. Its research involves the intersection and fusion of image analysis, spatio-temporal modeling, machine learning and other aspects. With the continuous development and deepening of technology, future video understanding is expected to achieve a more precise, smarter, and more automated level, providing greater convenience and possibilities for people's life and work.

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3. Application of unsupervised learning and self-supervised learning in computer vision

The application of unsupervised learning and self-supervised learning in computer vision is currently a hot research direction. Compared with supervised learning, these methods do not require expensive and time-consuming labeling process and have great potential. The main applications of these two learning methods in vision are explored in depth below.

3.1 Unsupervised Learning

clustering

Clustering tasks in unsupervised learning focus on how to group similar data.

• Image clustering: If using the K-means algorithm, images can be grouped by features such as color and texture for image retrieval and classification.
• Depth clustering: such as DeepCluster, clustering through features extracted by deep learning can capture more complex patterns.

Dimensionality Reduction and Representation Learning

Dimensionality reduction and representation learning can reveal the intrinsic structure of data.

• Principal Component Analysis (PCA): PCA is a commonly used image dimensionality reduction method that helps to remove noise and better understand the main components of an image.
• Autoencoder (AE): An autoencoder can learn a compressed representation of data and is often used for tasks such as image denoising and compression.

3.2 Self-supervised learning

Self-supervised learning uses a part of the data to predict the rest, and trains in an unsupervised environment, covering a variety of training tasks.

Comparative study

Contrastive learning learns representations of data by comparing positive and negative examples.

• SimCLR: SimCLR learns feature representations by comparing positive and negative examples.

# SimCLR的代码示例
from models import SimCLR
model = SimCLR(base_encoder)
loss = model.contrastive_loss(features)  # 对比损失

• MoCo: MoCo uses queues and momentum encoders for more robust contrastive learning, which helps train more accurate models.

Pre-training task design

• Predicting Color: Predicting the original color from a grayscale image helps to understand the color composition of the image.
• Autoregressive prediction: For example, use PixelCNN to predict the value of the next pixel of the image to enhance the control over image generation.

3.3 Cross-modal learning

• Image-to-text matching: learning visual and textual representations simultaneously, such as using CLIP, advances multimodal research.
• Audio-image matching: Unsupervised methods establish associations between audio and images, opening up new frontiers in multimedia analysis.

4. Summary

Unsupervised learning and self-supervised learning open a new path that does not rely on expensive annotations. This field is increasingly used in computer vision through rich methods such as clustering, contrastive learning, autoregressive prediction, etc. The latest research demonstrates the ability of self-supervised learning to approach or surpass supervised methods in visual representation learning, suggesting possible future research directions and a wide range of application scenarios.

The author, TechLead, has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji University, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and a billion-dollar AI revenue. Head of Product Development