Thermal infrared imaging has shown effective results as a diagnostic tool in breast cancer detection. It can be used as a complementary to traditional mammography. Asymmetry analysis are usually used to help detect abnormalities. However, in infrared imaging, this cannot be done without human interference. This paper proposes an automatic approach to asymmetry analysis in thermograms. It includ...

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree...

Automatic assistants could guide a person or a robot in performing new tasks, such as changing a car tire or repotting a plant. Creating such assistants, however, is non-trivial and requires understanding of visual and verbal content of a video. Towards this goal, we here address the problem of automatically learning the main steps of a task from a set of narrated instruction videos. We develop...

A growing body of work shows that compatible actions executed in parallel with cognitive tasks contribute beneficially to cognition, compared to incompatible actions. We investigate how such complementary actions are generated. Two models from imitation research, Associated Sequence Learning (ASL) and Active Intermodal Matching (AIM), were extended to develop models of complementary action gene...

Learning and evolution are two adaptive processes in the natural world that have been modelled in the study of artificial intelligence in computer science. In both biology and in artificial intelligence, learning and evolution are complementary processes. The nature of the interactions between learning and evolution has been the subject of much research in scientific disciplines. Evolution of a...

In this paper, a multiclass classification problem is solved using multiple complementary neural networks. Two techniques are applied to multiple complementary neural networks which are one-against-all and error correcting output codes. We experiment our proposed techniques using an extremely imbalance data set named glass from the UCI machine learning repository. It is found that the combinati...

A novel distinguished region detector, complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER) is proposed. The basic idea is to find distinguished regions by clusters of interest points. In order to determine the number of clusters we use the concept of maximal stableness across scale. Therefore, the ...