Image matching using TI multi-wavelet transform

A multi-resolution image matching technique based on multiwavelets followed by a coarse to fine strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed by a geometric topological refining approach along with the uniqueness constraint whereas the illuminative variation is dealt by using windowed normalized correlation.

Disparity estimation using TI multi-wavelet transform

A multi-resolution image matching technique based on translation invariant discrete multi-wavelet transform followed by a coarse to fine matching strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed explicitly by a geometric optimization approach along with the uniqueness constraint whereas the illuminative variation is dealt with by using windowed normalized correlation on the discrete multi-wavelet coefficients.

Ecommerce strategy in a multi-sector trading environment - quandaries for SMEs

For nearly a decade the potential benefits of Business-to-Business electronic commerce for business efficiency and competitiveness have been vigorously promoted by business, industry groups and governments. The belief underpinning policy is that from a small initial step, eCommerce will become a central part of their business strategies. This paper considers the use of B-2-B electronic transactions by SME suppliers who trade with buyer companies across diverse industry sectors in Australia. We investigate the links between their business strategies and their views of electronic trading. A survey of 240 crosssector suppliers nationwide found little evidence that electronic trading was integrated with their overall business strategy. We suggest an approach to the understanding of cross-sector electronic trading strategies that emphasises the complex, inter-connected but fragmented trading milieu rather than describing the balance between drivers and barriers that operate for the individual firm.

Multi-sectoral health promotion and public health : the role of evidence

Background Evidence-informed health promotion and public health is an emerging and ever-changing theme in research and practice. A collaborative approach to gathering and applying evidence is crucial to implementing effective multi-sectoral health promotion and public health interventions for improved population outcomes. This paper presents an argument for the development of multi-sector evidence and discusses both facilitators and challenges to this process.

Methods Sector-specific contacts familiar with decision-making processes were selected from referrals gained through academic, government and non-government networks and interviewed (in-person or via telephone) as part of a small scale study to scope the use of evidence within non-health sectors where decisions are likely to impact on public health.

Results The views gathered are preliminary, and this analysis would benefit from more extensive consultation. Nonetheless, information gathered from the interviews and literature search provide valuable insights into evidence-related decision-making paradigms which demonstrate similarities with, and differences from, those found in the health sector.

Conclusions Decisions in health promotion and public may benefit from consideration of the ways in which disciplines and sectors can work together to inform policy and practice.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Multi-view subspace clustering for face images

In many real-world computer vision applications, such as multi-camera surveillance, the objects of interest are captured by visual sensors concurrently, resulting in multi-view data. These views usually provide complementary information to each other. One recent and powerful computer vision method for clustering is sparse subspace clustering (SSC); however, it was not designed for multi-view data, which break down its linear separability assumption. To integrate complementary information between views, multi-view clustering algorithms are required to improve the clustering performance. In this paper, we propose a novel multi-view subspace clustering by searching for an unified latent structure as a global affinity matrix in subspace clustering. Due to the integration of affinity matrices for each view, this global affinity matrix can best represent the relationship between clusters. This could help us achieve better performance on face clustering. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other alternatives based on state-of-The-Arts on challenging multi-view face datasets.