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- Backhouse, Andrew, 1978
(författare)
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Error-Resilient Video Coding over IP Networks
- 2005
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- By its very nature, compressed data is more sensitive to bit corruption and data loss than uncompressed data. Transmitting data across unreliable bandlimited channels can therefore in many cases lead to an unsatisfactory reception of the source. This thesis deals with two distinct problems related to the transmission of video sequences across the Internet. The first problem addressed by this thesis, is the design of an error-resilient video coder for use on the Internet. A video coder is proposed which is based on the use of harmonic frames and leaky prediction coding. Harmonic frames are used to insert spatial redundancy into the compressed bitstream while leaky prediction coding is used to introduce temporal redundancy. The second focus of this thesis is channel prediction for the Internet. Congestion is the main cause of packet losses in the Internet. Congestion has previously been successfully predicted from measurements of the end-to-end variations in packet arrival times. Motivated by this, an algorithm is proposed to predict the probability of packet losses from the fluctuations in packet delays. The work was funded by Vinnova as part of the IPVideo project, a collaborative research project among Chalmers University of Technology, Linköping University and TopOneTech AB.
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- Backhouse, Andrew, 1978, et al.
(författare)
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ML Nonlinear Smoothing for Image Segmentation and Its Relationship to The Mean Shift
- 2007
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Ingår i: IEEE International conf. on Image Processing (ICIP '07).
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issues of nonlinear edge-preserving image smoothing and segmentation. A ML-based approach is proposed which uses an iterative algorithm to solve the problem. First, assumptions about segments are made by describing the joint probability distribution of pixel positions and colours within segments. Based on these assumptions, an optimal smoothing algorithm is derived under the ML condition. By studying the derived algorithm, we show that the solution is related to a two-stage mean shift which is separated in space and range. This novel ML-based approach takes a new kernel function. Experiments have been conducted on a range of images to smooth and segment them. Visual results and evaluations with 2 objective criteria have shown that the proposed method has led to improved results which suffer from less over-segmentation than the standard mean-shift.
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- Backhouse, Andrew, 1978, et al.
(författare)
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Robust Object Tracking using Particle Filters and Multi-Region Mean Shift
- 2009
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642104664 ; 5879, s. 11-403
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Konferensbidrag (refereegranskat)abstract
- In this paper, we introduce a novel algorithm which buildsupon the combined anisotropic mean-shift and particle filter framework. The anisotropic mean-shift with 5 degrees of freedom, is extended to work on a partition of the object into concentric rings. This adds spatial information to the description of the object which makes the algorithm more resilient to occlusion and less susceptible to confusion with objects having similar color densities. Experiments conducted on videos containing deformable objects with long-term partial occlusion (or, short-term full occlusion) and intersection have shown robust tracking performance, especially in tracking objects with long term partial occlusion, short term full occlusion, close color background clutter, severe object deformation and fast changing motion. Comparisons with two existing methods have shown marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drifts.
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- Backhouse, Andrew, 1978
(författare)
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Video Signal Processing: Compression Segmentation and Tracking
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis considers three separate research problems within the field of video processing.The first is concerned with segmenting an image, the second with tracking an object and the third with the communication of video across an unreliable network.Segmentation is an application-specific problem. An image should be segmented to distinguish interesting regions, but it should not be segmented further. This thesis proposes an algorithm to optimally segment an image based on a maximum-likelihood criterion. The proposed algorithm is a modification of the popular mean-shift algorithm. However, unlike mean-shift, the proposed algorithmuses a model to compute the most likely image segmentation.It achieves this while preserving both the simplicity and speed of mean-shift.In this thesis two distinct methods are proposed to track objects.The first builds upon the joint anisotropic mean-shiftand particle-filter framework. This framework consists of a gradient-ascent algorithm which is seeded by a particle filter to find all likely positions, orientations and scalings of a target. We have improved this algorithm by including spatial information in the description of the target. This makes the algorithm more robust againstpartial occlusions and background clutter.The second object-tracking algorithm is an improvement to the eigenface-based tracking algorithms. These algorithms representa single appearance of a target by an interpolated NxN image and the multitude of appearances which a target can take by a linear subspace of all NxN images.It is shown that the subspace can be tracked using aKalman filter. This is a better framework for tracking as it allows motion models and appearance models to be more accurately described.The research contributions related to video communication focus on specifically on packet-based computer networks. We propose that by monitoring the variations in transmission speeds of data packets,it is possible to predict the amount of congestion in the network.This allows us to predict the probability of packet loss in such a way that adaptive compression algorithms can be designed to efficiently deal with the expected packet loss. A modified quantized frame-expansion is then proposed in this thesis for this purpose. Using a gradient-descent algorithm, optimal transforms are found for the error-resilient transmission of data. This transform has been incorporated into a novel video-compression algorithm.
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- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Edge-Preserving Segmentation and Fusion of Medical Images by using Enhanced Mean Shift
- 2008
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Ingår i: Medicinteknikdagarna 2008, 14-15 oktober, Göteborg, Sweden.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses the issue of medical image segmentation by using an enhanced spatial-range mean shift. Mean shift is a method for estimating local modes (maxima) of pdf (probability density function) using a kernel-based approach.This paper describes an enhanced spatial-range mean shift segmentation method for biomedical (MRI) image segmentation. Preliminary work and the results on fusion of segmented brain images from different sensors (e.g. MRI, CT) are presented and discussed.
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