Uğur Güdükbay

Computer vision based method for real-time fire and flame detection

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues.

Scene Representation Technologies for 3DTV—A Survey

3-D scene representation is utilized during scene extraction, modeling, transmission and display stages of a 3DTV framework. To this end, different representation technologies are proposed to fulfill the requirements of 3DTV paradigm. Dense point-based methods are appropriate for free-view 3DTV applications, since they can generate novel views easily. As surface representations, polygonal meshes are quite popular due to their generality and current hardware support. Unfortunately, there is no inherent smoothness in their description and the resulting renderings may contain unrealistic artifacts. NURBS surfaces have embedded smoothness and efficient tools for editing and animation, but they are more suitable for synthetic content. Smooth subdivision surfaces, which offer a good compromise between polygonal meshes and NURBS surfaces, require sophisticated geometry modeling tools and are usually difficult to obtain. One recent trend in surface representation is point-based modeling which can meet most of the requirements of 3DTV, however the relevant state-of-the-art is not yet mature enough. On the other hand, volumetric representations encapsulate neighborhood information that is useful for the reconstruction of surfaces with their parallel implementations for multiview stereo algorithms. Apart from the representation of 3-D structure by different primitives, texturing of scenes is also essential for a realistic scene rendering. Image-based rendering techniques directly render novel views of a scene from the acquired images, since they do not require any explicit geometry or texture representation. 3-D human face and body modeling facilitate the realistic animation and rendering of human figures that is quite crucial for 3DTV that might demand real-time animation of human bodies. Physically based modeling and animation techniques produce impressive results, thus have potential for use in a 3DTV framework for modeling and animating dynamic scenes. As a concluding remark, it can be argued that 3-D scene and texture representation techniques are mature enough to serve and fulfill the requirements of 3-D extraction, transmission and display sides in a 3DTV scenario.

Real-time fire and flame detection in video

The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments.

Fuzzy color histogram-based video segmentation

We present a fuzzy color histogram-based shot-boundary detection algorithm specialized for content-based copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

Content-based retrieval of historical Ottoman documents stored as textual images

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. A framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domains, based on angular and distance span of shapes, are used to extract the symbols. In order to make content-based retrieval in the historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in the textual images. The query process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Silhouette-Based method for object classification and human action recognition in video

In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtract-tion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.

BilVideo: Design and Implementation of a Video Database Management System

With the advances in information technology, the amount of multimedia data captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in today’s world, and hence, a need for organizing this data, and accessing it from repositories with vast amount of information has been a driving stimulus both commercially and academically. In compliance with this inevitable trend, first image and especially later video database management systems have attracted a great deal of attention, since traditional database systems are designed to deal with alphanumeric information only, thereby not being suitable for multimedia data.In this paper, a prototype video database management system, which we call BilVideo, is introduced. The system architecture of BilVideo is original in that it provides full support for spatio-temporal queries that contain any combination of spatial, temporal, object-appearance, external-predicate, trajectory-projection, and similarity-based object-trajectory conditions by a rule-based system built on a knowledge-base, while utilizing an object-relational database to respond to semantic (keyword, event/activity, and category-based), color, shape, and texture queries. The parts of BilVideo (Fact-Extractor, Video-Annotator, its Web-based visual query interface, and its SQL-like textual query language) are presented, as well. Moreover, our query processing strategy is also briefly explained.

BilVideo: a video database management system

The BilVideo video database management system provides integrated support for spatiotemporal and semantic queries for video. A knowledge base, consisting of a fact base and a comprehensive rule set implemented in Prolog, handles spatio-temporal queries. These queries contain any combination of conditions related to direction, topology, 3D relationships, object appearance, trajectory projection, and similarity-based object trajectories. The rules in the knowledge base significantly reduce the number of facts representing the spatio-temporal relations that the system needs to store. A feature database stored in an object-relational database management system handles semantic queries. To respond to user queries containing both spatio-temporal and semantic conditions, a query processor interacts with the knowledge base and object-relational database and integrates the results returned from these two system components. Because of space limitations, we only discuss the Web-based visual query interface and its fact-extractor and video-annotator tools. These tools populate the systems fact base and feature database to support both query types.

Automatic detection of salient objects and spatial relations in videos for a video database system

Multimedia databases have gained popularity due to rapidly growing quantities of multimedia data and the need to perform efficient indexing, retrieval and analysis of this data. One downside of multimedia databases is the necessity to process the data for feature extraction and labeling prior to storage and querying. Huge amount of data makes it impossible to complete this task manually. We propose a tool for the automatic detection and tracking of salient objects, and derivation of spatio-temporal relations between them in video. Our system aims to reduce the work for manual selection and labeling of objects significantly by detecting and tracking the salient objects, and hence, requiring to enter the label for each object only once within each shot instead of specifying the labels for each object in every frame they appear. This is also required as a first step in a fully-automatic video database management system in which the labeling should also be done automatically. The proposed framework covers a scalable architecture for video processing and stages of shot boundary detection, salient object detection and tracking, and knowledge-base construction for effective spatio-temporal object querying.