Publications by Martin Vetterli

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We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a con- sistent estimator of texture model parameters for the FE step followed by computing the Kullback–Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-b

In this paper, we propose kinetic Euclidean distance matrices (KEDMs) a new algebraic tool for localization of moving points from spatio temporal distance measurements. KEDMs are inspired by the well-known Euclidean distance matrices (EDM) which model stat ...

For a class of sensor networks, the task is to monitor an underlying physical phenomenon over space and time through an imperfect observation process. The sensors can communicate back to a central data collector over a noisy channel. The key pa- rameters i ...

A multiresolution representation for video signals is introduced. A three-dimensional spatiotemporal pyramid algorithms for high-quality compression of advanced television sequences is presented. The scheme utilizes a finite memory structure and is robust to channel errors, provides compatible subchannels, and can handle different scan formats, making it well suited for the broadcast environment. Additional features such as fast random access and reverse playback make it suitable for digital storage as well

The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to capture efficiently one-dimensional (1-D) disco ...

In this study, we determine the scalar roughness for sensible heat in the temperature profile (z0,h) based on direct skin temperature measurements and profiles of wind and temperature in a complex urban environment. Classically, skin temperatures are inferred from remotely sensed data (e.g. satellite). However, in complex settings like cities, strong heterogeneities exist in the temperature fields at scales that cannot be resolved by remote sensing. Because they are more precise, in situ measurements, when

Recent results in compressed sensing or compressive sampling suggest that a relatively small set of measurements taken as the inner product with universal random measurement vectors can well represent a source that is sparse in some fixed basis. By adapting a deterministic, non-universal and structured sensing device, this paper presents results on using the annihilating filter to decode the information taken in this new compressed sensing environment. The information is the minimum amount of nonadaptive kn

Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several information-theoretic bounds on the scaling law of the required number of measurements for exact support recovery have been derived, where the focus is mainly on random measurement matrices. In this paper, we investigate the support recovery problem from an

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

We present a new method for signal reconstruction from multiple sets of samples with unknown offsets. We rewrite the reconstruction problem as a set of polynomial equations in the unknown signal parameters and the offsets between the sets of samples. Then, ...

It often happens that we are interested in reconstructing an unknown signal from partial measurements. Also, it is typically assumed that the location (temporal or spatial) of the samples is known and that the only distortion present in the observations is ...

The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictio- nary of time–frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In this paper, we apply matching pursuit to the derivation of signal expansions based on damped sinusoids. It is shown that expansions in terms of complex damped sinusoids can be efficiently derived using simple recursive filter banks. We discuss a subspace e

We study a new image device called gigavision camera or the gigapixel digital film camera. The main differences between a conventional and a gigavision camera are that the pixels of the latter are binary and orders of magnitude smaller. A gigavision camera can be built using standard memory chip technology, where each memory bit is designed to be light sensitive. A conventional gray level image can be obtained from the binary gigavision image by low-pass filtering and sampling. The main advantage of the gig

Consider the task of sampling and reconstructing a bandlimited spatial field in

\Re^2

using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to incre ...

Hearing aids are electronic, battery-operated sensing devices which aim at compensating various kinds of hearing impairments. Recent advances in low-power electronics coupled with progresses made in digital signal processing offer the potential for substan ...

For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although robots are often equipped with microphones and speakers, the audio modality ...

Achieving perfect scale-invariance is usually not possible using classical color image features. This is mostly because of the fact that a traditional image is a two-dimensional projection of the real world. In contrast, light field imaging makes available ...

The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson’s Theorem that give more insight into the effect of routing algorithms on the queue size distribu- tions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different commun

We present sampling results for certain classes of two-dimensional signals that are not bandlimited, but have a parametric representation with a finite number of degrees of freedom. While there are many such parametric signals, it is often difficult to propose practical sampling schemes, therefore, we will concentrate on those classes for which we are able to give exact sampling algorithms and reconstruction formulas. We analyze in detail a set of 2-D Diracs and extend the results to more complex objects su

We study the spatio-temporal sampling of physical fields representing the dispersion of a substance in the atmosphere. We consider the following setup: N sensors are deployed at ground level and measure the concentration of a particular substance, while M ...

Martin Vetterli

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Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

On The Move: Localization With Kinetic Euclidean Distance Matrices

Power, Spatio-Temporal Bandwidth, and Distortion in Large Sensor Networks

Interpolative multiresolution coding of advanced television with compatible subchannels

Low-Rate Reduced Complexity Image Compression using Directionlets

Determination of Thermal Roughness Length for Complex Urban Areas with High Resolution Meteorological Stations

Annihilating filter-based decoding in the compressed sensing framework

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Signal Reconstruction from Multiple Unregistered Sets of Samples using Groebner Bases

Unlabeled Sensing: Reconstruction Algorithm and Theoretical Guarantees

Matching pursuit and atomic signal models based on recursive filter banks

The Gigavision Camera

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

Rate-Constrained Collaborative Noise Reduction for Wireless Hearing Aids

Blind as a Bat: Audible Echolocation on Small Robots

Scale-invariant representation of light field images for object recognition and tracking

Efficient routing with small buffers in dense networks

Exact Sampling Results for Some Classes of Parametric Non-Bandlimited 2-D Signals

Sampling and Reconstruction of Time-Varying Atmospheric Emissions