Towards human-like and transhuman perception in AI 2.0: a review <s> Active vision-from looking to looking around <s> We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture. <s> BIB001 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Active vision-from looking to looking around <s> The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond. <s> BIB002 </s> Towards human-like and transhuman perception in AI 2.0: a review <s> Active vision-from looking to looking around <s> With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers. <s> BIB003
In the past decades, computer vision has played an important role in AI. We have witnessed the dramatic change in various recognition tasks. After about 30-year work on general object recognition and 3D recovery, researchers in the 1990s began focusing their attention on recognition of special objects such as faces, pedestrians, and vehicles BIB001 . Now, computer vision systems are superior to human beings in large-scale face recognition tasks under controlled environments. Meanwhile, object categorization has been extended from several classes to thousands of classes BIB002 . As one of the deep learning models, the convolutional neural network (CNN) and its derivatives have achieved great success in ImageNet and in other tasks  . This achievement seemed to break the barrier to automatic recognition. However, this is misleading. Human beings, and even other animals, never look at something from a passive acquisition of information point of view. They look around under various conditions, sometimes even touching and using other sensory organs. Therefore, active sensing and recognition will be the terminator in computer vision. In contrast to an animal's active vision which can move only on six degrees of freedom (DoFs), an active computer vision system can have more DoFs. Some efforts in this regard have made significant progress in recent years. One example is Microsoft's Kinect. With an active ejected infrared pattern, 3D reconstruction has become easier than ever before. This kind of device takes image/video based human-computer interface (HCI) and other indoor applications a step forward BIB003 . Another example is city-scale reconstruction from multiple uncalibrated cameras  . Although these images are captured passively, the whole set can still be viewed as an active set. Self-driving cars, drones, and other mobile robots bring computer vision systems to a full capability of moving around in their environment. This mobility enables the computer vision system to observe an environment actively and continuously. Note that in the DARPA Robotics Challenge competition, continuous vision systems have shown their power preliminarily  . Therefore, seeking methods to implement more comprehensive active sensing and recognition systems should be one of the most important tasks in the AI 2.0 era.