Boosting is a class of machine learning methods based on the idea that a combination of
simple classifiers (obtained by a weak learner) can perform better than any of the simple …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

Scalability of object detectors with respect to the number of classes is a very important issue
for applications where many object classes need to be detected. While combining single …

Due to the rapid growth of free text documents available in digital form, efficient techniques
of automatic categorization are of great importance. In this paper, we present an efficient rule …

In order for recognition systems to scale to a larger number of object categories building
visual class taxonomies is important to achieve running times logarithmic in the number of …

This thesis presents important insights and concepts related to the topic of the extraction of
geometric primitives from the edge contours of digital images. Three specific problems …

In this paper, an automatic approach for zebra crossing extraction and reconstruction from
high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

To overcome the high computational complexity in real-time classifier design, we propose a
fast classification scheme. A new measure called 'reconstruction proportion'is exploited to …

In this dissertation, we present a framework to enhance the retrieval, ranking, and
categorization of text documents in medical domain. The contributions of this study are the …