This year, the joint team of UCF and the University of Modena has participated in the following tasks: (1) shot boundary detection, (2) low-level feature extraction, (3) high-level feature extraction, (4) topic search and (5) BBC rushes management. The shot boundary detection was contributed by the Image Lab at the University of Modena. The other tasks were performed by the Computer Vision Team...

Usually, the rst processing step in computer vision systems consists of a spatial convolution with only a few simple lters. Therefore, information is lost or it is not represented explicitly for the following processing steps. This paper proposes a new hierarchical lter scheme that can e ciently synthesize the responses for a large number of speci c lters. The scheme is based on steerable lters...

This paper examines the issue of direct extraction of low level features from compressed images. Specifically, we consider the detection of areas of interest and edges in images compressed using the discrete cosine transform (DCT). For interest areas, we show how a measure based on certain DCT coefficients of a block can provide an indication of underlying activity. For edges, we show using an ...

In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can...

A typical content-based information retrieval (CBIR) system, e.g., an image or video retrieval system, includes three major aspects: feature extraction, high dimensional indexing and system design [1]. Among the three aspects, high dimensional indexing is important for speed performance; system design is critical for appearance performance; and feature extraction is the key to accuracy performa...

Robust low-level image features have proven to be effective representations for a variety of high-level visual recognition tasks, such as object recognition and scene classification. But as the visual recognition tasks become more challenging, the semantic gap between low-level feature representation and the meaning of the scenes increases. In this paper, we propose to use objects as attributes...

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underlying representation. This paper proposes to acquire representations for machine learning by reading text written to accommodate human learning. We propose a novel form of semantic analysis called reading to learn, where the goal is to obtain a high-level semantic abstract of ...

Affective computing researchers adopt a variety of methods in analysing or synthesizing aspects of human behaviour. The choice of method depends on which behavioural cues are considered salient or straightforward to capture and comprehend, as well as the overall context of the interaction. Thus, each approach focuses on modelling certain information and results to dedicated representations. How...

This paper presents a novel approach for automatic people counting in videos captured through a conventional closed-circuit television (CCTV) using computer vision techniques. The proposed approach consists of detecting and tracking moving objects in video scenes to further counting them when they enters into a virtual counting zone defined in the scene. One of the main problems of using conven...

Teaching a robot to perceive and navigate in an unstructured natural world is a difficult task. Without learning, navigation systems are short-range and extremely limited. With learning, the robot can be taught to classify terrain at longer distances, but these classifiers can be fragile as well, leading to extremely conservative planning. A robust, high-level learning-based perception system f...