Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

Advanced Programming Language Features for Executable Design Patterns "Better Patterns Through Reflection

The Design Patterns book [GOF95] presents 24 time-tested patterns that consistently appear in well-designed software systems. Each pattern is presented with a description of the design problem the pattern addresses, as well as sample implementation code and design considerations. This paper explores how the patterns from the "Gang of Four'', or "GOF'' book, as it is often called, appear when similar problems are addressed using a dynamic, higher-order, object-oriented programming language. Some of the patterns disappear -- that is, they are supported directly by language features, some patterns are simpler or have a different focus, and some are essentially unchanged.

Relative Contributions of Internal and External Features to Face Recognition

The central challenge in face recognition lies in understanding the role different facial features play in our judgments of identity. Notable in this regard are the relative contributions of the internal (eyes, nose and mouth) and external (hair and jaw-line) features. Past studies that have investigated this issue have typically used high-resolution images or good-quality line drawings as facial stimuli. The results obtained are therefore most relevant for understanding the identification of faces at close range. However, given that real-world viewing conditions are rarely optimal, it is also important to know how image degradations, such as loss of resolution caused by large viewing distances, influence our ability to use internal and external features. Here, we report experiments designed to address this issue. Our data characterize how the relative contributions of internal and external features change as a function of image resolution. While we replicated results of previous studies that have shown internal features of familiar faces to be more useful for recognition than external features at high resolution, we found that the two feature sets reverse in importance as resolution decreases. These results suggest that the visual system uses a highly non-linear cue-fusion strategy in combining internal and external features along the dimension of image resolution and that the configural cues that relate the two feature sets play an important role in judgments of facial identity.

LLSIM Reference Manual

A program that simulates a Digital Equipment Corporation PDP-11 computer and many of its peripherals on the AI Laboratory Time Sharing System (ITS) is described from a user's reference point of view. This simulator has a built in DDT-like command level which provides the user with the normal range of DDT facilities but also with several special debugging features built into the simulator. The DDT command language was implemented by Richard M. Stallman while the simulator was written by the author of this memo.

The Japanese Business System: Key Features and Prospects for Change

This paper argues that the Japanese business system cannot be adequately understood without extending the focus of analysis beyond the individual firm to the vertical keiretsu, or business group. The vertical group or keiretsu structure was first identified and studied in the auto and electronics industries, where it is most strongly marked, but it characterizes virtually all sectors, service industries as well as manufacturing. Large industrial vertical keiretsu are composed of subsidiaries engaged in three distinct types of activities (manufacturing, marketing, and quasirelated business). The coordination and control systems are built on the flows of products, financial resources, information and technology, and people across formal company boundaries, with the parent firm controlling the key flows. The paper examines the prevailing explanations first for the emergence and then for the persistence of the vertical group structure, and looks at the current pressures for change and adaptation in the system.