4 research outputs found

    Solutions to non-stationary problems in wavelet space.

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    An application specific low bit-rate video compression system geared towards vehicle tracking.

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    Thesis (M.Sc.Eng.)-University of Natal, Durban, 2003.The ability to communicate over a low bit-rate transmission channel has become the order of the day. In the past, transmitted data over a low bit-rate transmission channel, such as a wireless link, has typically been reserved for speech and data. However, there is currently a great deal of interest being shown in the ability to transmit streaming video over such a link. These transmission channels are generally bandwidth limited hence bit-rates need to be low. Video on the other hand requires large amounts of bandwidth for real-time streaming applications. Existing Video Compression standards such as MPEG-l/2 have succeeded in reducing the bandwidth required for transmission by exploiting redundant video information in both the spatial and temporal domains. However such compression systems are geared towards general applications hence they tend not to be suitable for low bit-rate applications. The objective of this work is to implement such a system. Following an investigation in the field of video compression, existing techniques have been adapted and integrated into an application specific low bit-rate video compression system. The implemented system is application specific as it has been designed to track vehicles of reasonable size within an otherwise static scene. Low bit-rate video is achieved by separating a video scene into two areas of interest, namely the background scene and objects that move with reference to this background. Once the background has been compressed and transmitted to the decoder, the only data that is subsequently transmitted is that that has resulted from the segmentation and tracking of vehicles within the scene. This data is normally small in comparison with that of the background scene and therefore by only updating the background periodically, the resulting average output bit-rate is low. The implemented system is divided into two parts, namely a still image encoder and decoder based on a Variable Block-Size Discrete Cosine Transform, and a context-specific encoder and decoder that tracks vehicles in motion within a video scene. The encoder system has been implemented on the Philips TriMedia TM-1300 digital signal processor (DSP). The encoder is able to capture streaming video, compress individual video frames as well as track objects in motion within a video scene. The decoder on the other hand has been implemented on the host PC in which the TriMedia DSP is plugged. A graphic user interface allows a system operator to control the compression system by configuring various compression variables. For demonstration purposes, the host PC displays the decoded video stream as well as calculated rate metrics such as peak signal to noise ratio and resultant bit-rate. The implementation of the compression system is described whilst incorporating application examples and results. Conclusions are drawn and suggestions for further improvement are offered

    Self-localization in ubiquitous computing using sensor fusion

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    The widespread availability of small and inexpensive mobile computing devices and the desire to connect them at any time in any place has driven the need to develop an accurate means of self-localization. Devices that typically operate outdoors use GPS for localization. However, most mobile computing devices operate not only outdoors but indoors where GPS is typically unavailable. Therefore, other localization techniques must be used. Currently, there are several commercially available indoor localization systems. However, most of these systems rely on specialized hardware which must be installed in the mobile device as well as the building of operation. The deployment of this additional infrastructure may be unfeasible or costly. This work addresses the problem of indoor self-localization of mobile devices without the use of specialized infrastructure. We aim to leverage existing assets rather than deploy new infrastructure. The problem of self-localization utilizing single and dual sensor systems has been well studied. Typically, dual sensor systems are used when the limitations of a single sensor prevent it from functioning with the required level of performance and accuracy. A second sensor is often used to complement and improve the measurements of the first one. Sometimes it is better to use more than two sensors. In this work the use of three sensors with complementary characteristics was explored. The three sensor system that was developed included a positional sensor, an inertial sensor and a visual sensor. Positional information was obtained via radio localization. Acceleration information was obtained via an accelerometer and visual object identification was performed with a video camera. This system was selected as representative of typical ubiquitous computing devices that will be capable of developing an awareness of their environment in order to provide users with contextually relevant information. As a part of this research a prototype system consisting of a video camera, accelerometer and an 802.11g receiver was built. The specific sensors were chosen for their low cost and ubiquitous nature and by their ability to complement each other in a self-localization task using existing infrastructure. A Discrete Kalman filter was designed to fuse the sensor information in an effort to get the best possible estimate of the system position. Experimental results showed that the system could, when provided with a reasonable initial position estimate, determine its position with an average error of 8.26 meters

    A Wavelet Packet-Based Noise Reduction Algorithm of NTSC Images Using CVBS Characteristics

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    This paper proposes a wavelet packet-based noise reduction algorithm for national television system committee (NTSC) images, in which the characteristics of a composite video burst signal (CVBS) are utilized. Most of conventional noise reduction algorithms apply spatial or spatio-temporal filtering only to the luminance signal, noting that the human eye is less sensitive to color than to luminance. Such noise reduction algorithms do not consider the real-world situation where a TV signal is transmitted over a noisy channel and decoded at a receiver. In this paper, it is assumed that an NTSVC signal is transmitted as a CVBS and corrupted with white Gaussian noise (WGN) by the channel. A CVBS has characteristics different from those of speech or image signals in a sense that encoded color information is modulated onto a high frequency color subcarrier. The wavelet packet-based approach is suitable for noise reduction of the CVBS because decomposing a one-dimensional CVBS into eight subbands provides a chance to process each subband separately. In the proposed wavelet packet-based noise reduction algorithm, wavelet packet filtering is employed in subbands containing the color information whereas Wiener filtering is used in the other subbands. The separate treatment of each wavelet subband depending on the characteristics of CVBS leads to effective color and edge preserving noise reduction. The performance of the proposed method is validated by experiments with generated and corrupted CVBS images. Experimental results with various test images show that the proposed algorithm is effective in terms of the noise reduction efficiency and edge and color preservation.close1
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