Our demonstration consists of sensor nodes suitable for imageintensive network applications. We developed nodes for stationary and mobile deployment, for face recognition and human detection applications, respectively. Both designs consist of a visible spectrum camera sensor and a Zigbee-compliant wireless data transceiver. The stationary model receives input from a PIR motion sensor, while the mobile model incorporates distance measures from either an infrared or ultrasonic sensor and includes a robot for demonstration of mobility. Our node uses strictly commercial off the-shelf components, and not including the robotic accoutrement, is relatively inexpensive. The stationary node will be on display for face recognition of attendees. Additionally, we intend to demonstrate the mobile node for a human detection application, wherein the node detects objects, and transmits them to a host machine for face detection processing.

Videotext refers to text superimposed on video frames. A videotext based multimedia description scheme has been adopted in the MPEG-7 standard. A study of published work in the area of videotext extraction and recognition reveals that, despite previous interest, a reliable general purpose video character recognition (VCR) system is yet to be developed. In our research and development of a character recognition algorithm designed specifically for the low resolution output from automatic videotext extractors, we observed that raw VCR accuracies obtained using various classifiers including kernel space methods such as SVMs, are inadequate for accurate video annotation and browsing. Intelligent post-processing mechanisms that are supported by general data characteristics of the domain are hence, required for performance improvement. We describe one such method, referred to as the font context analysis, which works independently of the raw character recognition technique. As a result, it can be easily implemented in conjunction with other VCR algorithms being developed elsewhere, and offer the same performance gains. Experimental results on various video streams show notable improvements in recognition rates with our system incorporating a SVM-based character recognition mechanism and font context analysis.

Videotext refers to text superimposed on still images and video frames, and a videotext based Multimedia Description Scheme has recently been adopted into the MPEG-7 standard as one of the normative media content description interfaces. While much of the previous work including ours concentrates on the task of locating and extracting text from the video frames automatically, very little research has focused on reliably recognizing segmented text. The low resolution of videotext, unconstrained font styles and sizes, poor separation of characters often resulting from video compression and decoding, all pose severe problems even to commercial OCRs in recognizing videotext accurately. This paper describes novel end-to-end video character recognition system featuring new character attributes emphasizing macro shapes, a Support Vector Machine-based character classifier, videotext object synthesis, font context analysis, and temporal contiguity analysis, to successfully address the issues confounding accurate videotext recognition. We present results from our experiments with real video data that demonstrate the strengths of this system.

Region extraction and labeling is a fundamental process in building computer systems that automatically analyze and interpret images and image sequences of a scene. It involves extracting "meaningful" regions and their attributes from an image that are of interest in a given domain, e.g., a human face in face recognition and surveillance applications, a vehicle in intelligent highway monitoring, or an industrial tool in automated manufacturing and inspection applications. In this paper, we present a generalized and fast region labeling algorithm that can be applied to numerous image processing tasks: region extraction in binary images, region segmentation from gray scale or color images, region boundary extraction, chain code extraction, run-length coding of images, and image restoration from chain codes. The general applicability of our algorithm stems from its strengths in identifying the fundamental and common computational steps and providing robust and efficient procedures to accomplish these steps without requiring multiple scans of images. Our algorithm is shown to be more effective in terms of space and computational efficiency, in comparison with many existing approaches to region labeling and extraction. Results from our experiments with over 5,000 frames obtained from MPEG-1 video bitstreams demonstrate the good performance of our algorithm in terms of video text segmentation accuracy and computational efficiency.

Efficient content-based retrieval of image and video databases is an important application due to rapid proliferation of digital video data on the Internet and corporate intranets. Text either embedded or superimposed within video frames is very useful for describing the contents of the frames, as it enables both keyword and free-text based search, automatic video logging, and video cataloging. We have developed a scheme for automatically extracting text from digital images and videos for content annotation and retrieval. We present our approach to robust text extraction from video frames, which can handle complex image backgrounds, deal with different font sizes, font styles, and font appearances such as normal and inverse video. Our algorithm results in segmented characters that can be directly processed by an OCR system to produce ASCII text. Results from our experiments with over 5000 frames obtained from twelve MPEG video streams demonstrate the good performance of our system in terms of text identification accuracy and computational efficiency.