Søren I. Olsen

Estimation of noise in images: an evaluation

Abstract Six methods for estimating the standard deviation of white additive noise in images are surveyed and evaluated experimentally by application to a set of images showing different degrees of contrast, edge details, texture, etc. The results show that on average, the most reliable estimate is obtained by prefiltering the image to suppress the image structure and then computing the standard deviation value from the filtered data.

Coarse-to-fine low-rank structure-from-motion

We address the problem of deformable shape and motion recovery from point correspondences in multiple perspective images. We use the low-rank shape model, i.e. the 3D shape is represented as a linear combination of unknown shape bases. We propose a new way of looking at the low-rank shape model. Instead of considering it as a whole, we assume a coarse-to-fine ordering of the deformation modes, which can be seen as a model prior. This has several advantages. First, the high level of ambiguity of the original low-rank shape model is drastically reduced since the shape bases can not anymore be arbitrarily re-combined. Second, this allows us to propose a coarse-to-fine reconstruction algorithm which starts by computing the mean shape and iteratively adds deformation modes. It directly gives the sought after metric model, thereby avoiding the difficult upgrading step required by most of the other methods. Third, this makes it possible to automatically select the number of deformation modes as the reconstruction algorithm proceeds. We propose to incorporate two other priors, accounting for temporal and spatial smoothness, which are shown to improve the quality of the recovered model parameters. The proposed model and reconstruction algorithm are successfully demonstrated on several videos and are shown to outperform the previously proposed algorithms.

Implicit Non-Rigid Structure-from-Motion with Priors

This paper describes an approach to implicit Non-Rigid Structure-from-Motion based on the low-rank shape model. The main contributions are the use of an implicit model, of matching tensors, a rank estimation procedure, and the theory and implementation of two smoothness priors. Contrarily to most previous methods, the proposed method is fully automatic: it handles a substantial amount of missing data as well as outlier contaminated data, and it automatically estimates the degree of deformation. A major problem in many previous methods is that they generalize badly. Although the estimated model fits the visible training data well, it often predicts the missing data badly. To improve generalization a temporal smoothness prior and a surface shape prior are developed. The temporal smoothness prior constrains the camera trajectory and the configuration weights to behave smoothly. The surface shape prior constrains consistently close image point tracks to have similar implicit structure. We propose an algorithm for achieving a Maximum A Posteriori (map) solution and show experimentally that the map-solution generalizes far better than the prior-free Maximum Likelihood (ml) solution.

Epipolar Line Estimation

This paper describes a method by which the epipolar line equation for binocular stereo, i.e. the invariant relating the image coordinates of corresponding image points, can be estimated directly by analyzing the images. No camera calibration or detailed knowledge of the stereo geometry is required.

Stereo correspondence by surface reconstruction

An algorithm that solves the computational stereo correspondence and the surface reconstruction is presented. The algorithm integrates the reconstruction process in the correspondence analysis by means of multipass attribute matching and disparity refinement. In the matching process, the requirement of attribute similarity is relaxed with the pass number while the requirement for agreement between the predicted and the measured disparity is tightened. Disparity discontinuities and occluded areas are detected by analyzing the partial derivatives of the reconstructed disparity surface. Results on synthetic and on real stereo image pairs are reported. >

A batch algorithm for implicit non-rigid shape and motion recovery

The recovery of 3D shape and camera motion for non-rigid scenes from single-camera video footage is a very important problem in computer vision. The low-rank shape model consists in regarding the deformations as linear combinations of basis shapes. Most algorithms for reconstructing the parameters of this model along with camera motion are based on three main steps. Given point tracks and the rank, or equivalently the number of basis shapes, they factorize a measurement matrix containing all point tracks, from which the camera motion and basis shapes are extracted and refined in a bundle adjustment manner. There are several issues that have not been addressed yet, among which, choosing the rank automatically and dealing with erroneous point tracks and missing data. We introduce theoretical and practical contributions that address these issues. We propose an implicit imaging model for non-rigid scenes from which we derive non-rigid matching tensors and closure constraints. We give a non-rigid Structure-From-Motion algorithm based on computing matching tensors over subsequences, from which the implicit cameras are extrated. Each non-rigid matching tensor is computed, alongwith the rank of the subsequence, using a robust estimator incorporating a model selection criterion that detects erroneous image points. Preliminary experimental results on real and simulated data show that our algorithm deals with challenging video sequences.

Using Priors for Improving Generalization in Non-Rigid Structure-from-Motion

This paper describes how the generalization ability of methods for non-rigid Structure-from-Motion can be improved by using priors. Most point tracks are often visible only in some of the images; predicting the missing data can be important. Previous Maximum-Likelihood (ML)-approaches on implicit non-rigid Structure-from-Motion generalize badly. Although the estimated model fits well to the visible training data, it often predict s the missing data badly. To improve generalization we propose to add a temporal smoothness prior and a continuous surface shape prior to an ML-approach. The temporal smoothness prior constrains the camera trajectory and the configuration weights to behave smoothly. The surface shape prior constrains consistently close image point tracks to have a similar implicit structur e. We propose an algorithm for achieving a Maximum A Posteriori (MAP)-solution and show experimentally that the MAP-solution generalizes far better than the MLsolution. The proposed method is fully automatic: it handles a substantial amount of missing data as well as outlier contaminated data, and automatically estimates the rank of the measurement matrix.

Block-truncation and planar image coding

Abstract A fast image coding scheme built on block truncation, constant, and planar block coding is suggested. The model selection is made to give the best reconstruction. Multi-codebook vector quantization (VQ) of model type and gray levels is used. The selection of model codebook and gray level quantization is based on the desired compression ratio.

Can insects increase food security in developing countries? An analysis of Kenyan consumer preferences and demand for cricket flour buns

Achieving food security in an environmentally sustainable manner is one of the biggest challenges of our time. Using insects as food can serve this purpose because they are nutritionally valuable and environmentally friendly. Embracing insects as food requires information on potential consumer demand as this would determine the success of product development. In this study, we present one of the first thorough assessments of consumer demand for an insect-based food. We assessed the demand in terms of Kenyan consumer preferences and willingness to pay for buns containing varying amounts of cricket flour. We also assessed demand by predicting the market share in a presumed market scenario. The study used an incentivized discrete choice experiment integrated with sensory evaluations. This was intended to reduce any hypothetical bias and to allow participants to acquire experience by tasting the buns. We found significant and positive preferences for the cricket-flour-based buns. The bun products with medium amounts (5%) of cricket flour were preferred to no or high amounts (10%) of cricket flour. Market share predictions showed that cricket-flour-based buns were likely to obtain greater market shares than standard buns. Results also suggested that a market for breads made with cricket flour is likely in Kenya since the demand is present. This signals that insect-based food products may serve as a viable and demand-driven way to increase food security in Kenya in the future.

Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection

Huamin Ren1 hr@create.aau.dk Weifeng Liu2 weifeng.liu@nbi.ku.dk Soren Ingvor Olsen3 ingvor@di.ku.dk Sergio Escalera4 sergio@maia.ub.es Thomas B. Moeslund1 tbm@create.aau.dk 1 Department of Architecture, Design and Media Technology Aalborg University Aalborg, Denmark 2 Niels Bohr Institute University of Copenhagen Copenhagen, Denmark 3 Department of Computer Science University of Copenhagen Copenhagen, Denmark 4 Dept. Applied Mathematics, University of Barcelona, Computer Vision Center Barcelona, Spain