3D sparse feature model using short baseline stereo and multiple view registration

This paper outlines a methodology to generate a distinctive object representation offline, using short-baseline stereo fundamentals to triangulate highly descriptive object features in multiple pairs of stereo images. A group of sparse 2.5D perspective views are built and the multiple views are then fused into a single sparse 3D model using a common 3D shape registration technique. Having prior knowledge, such as the proposed sparse feature model, is useful when detecting an object and estimating its pose for real-time systems like augmented reality.

Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.

A novel monotonic fixed-point algorithm for l1-regularized least square vector and matrix problem

Least square problem with l₁ regularization has been proposed as a promising method for sparse signal reconstruction (e.g., basis pursuit de-noising and compressed sensing) and feature selection (e.g., the Lasso algorithm) in signal processing, statistics, and related fields. These problems can be cast as l₁-regularized least-square program (LSP). In this paper, we propose a novel monotonic fixed point method to solve large-scale l₁-regularized LSP. And we also prove the stability and convergence of the proposed method. Furthermore we generalize this method to least square matrix problem and apply it in nonnegative matrix factorization (NMF). The method is illustrated on sparse signal reconstruction, partner recognition and blind source separation problems, and the method tends to convergent faster and sparser than other l₁-regularized algorithms.

Photoinduced shape conversion and reconstruction of silver nanoprisms

A series of shapes of silver nanoplates were achieved by adjusting the concentration of citrate in the colloid in the photoinduced process. The local surface plasmon resonance (LSPR) of the silver nanoplates could be tuned gradually in a range from 740 to 440 nm. In contrast, the LSPR band can be photomediated again to the long wavelength region within 620-690 nm only by adding more citrate to the colloidal system. The initial silver nanoprisms converted to the discal shape under the light effect. In this conversion, the coupling effect of the plasmon resonance and the light source speeds up the photothermal reaction. Subsequently, the reconstruction of silver nanoprisms from the nanodisks took place in the presence of more citrate through the photoconversion.

Computable social patterns from sparse sensor data

We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social footprint and present a method to construct a codebook, enabling the transformation of raw sensor data into a collection of social pages. Each page captures social activities of a user over regular time period, and represented as a sequence of encoded footprints. Computable patterns are then defined as repeated structures found in these sequences. To do so, we appeal to modeling tools in document analysis and propose a Latent Social theme Dirichlet Allocation (LSDA) model - a version of the Ngram topic model in [6] with extra modeling of personal context. This model can be viewed as a Bayesian clustering method, jointly discovering temporal collocation of footprints and exploiting statistical strength across social pages, to automatically discovery high-order patterns. Alternatively, it can be viewed as a dimensionality reduction method where the reduced latent space can be interpreted as the hidden social 'theme' - a more abstract perception of user's daily activities. Applying this framework to a real-world noisy dataset collected over 1.5 years, we show that many useful and interesting patterns can be computed. Interpretable social themes can also be deduced from the discovered patterns.

Global and threshold-free transcriptional regulatory networks reconstruction through integrating ChIP-Chip and expression data

Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.

The intriguing and forgotten international exchanges in the master plan for the reconstruction of Skopje

At 5:17AM on Friday 26 July 1963, Skopje was struck by an earthquake, which in 17 seconds destroyed approximately 75% of the urban fabric, and changed the course of its history from an unknown town to a city of international focus. Immediate actions and a formidable level of local organization; an unprecedented pouring in of aid from the other Yugoslav republics and from individual nations and organisations; and a monumental role for the United Nations in the co-ordination of international , architectural and urban planning expertise for the city's large-scale and long-term reconstruction, laid the foundation for what has been called 'a precise Marxist revolutionary situation' .1 The detail of the paper concerns Stage 4, Replanning 1963 to 1966 of the planning strategy that was organised into five stages, and has a major interest in the invited United Nations (UN) international competition ~o redesign approximately two square kilometres of the city centre.2 Action is associated with activity as productivity, but in Skopje added to this were symbolic human acts and heroic action such that its inhabitants regained their city and importantly a new position as city in the world.

Image reconstruction using Loop subdivision

It is an ultimate objective to reconstruct an image with high quality using a compact representation, which is the basic step in image-manipulation fields. We propose an effective vectorization based approach to reconstruct an image using a triangular mesh associated with Loop subdivision scheme in the present paper. With an initial control mesh obtained by simplifying a dense mesh from a quality-preserved triangulation, we produce the final optimal control mesh by optimizing a mesh over topologies and colors to approximate the given image. The main advantages of the approach include: (1) the reconstruction of an image is not restricted to be aligned with image coordinate axes; (2) a high order continuous function is defined over a triangle instead of a bilinear interpolation; (3) it is a compact and vector-based representation easy to edit and transmit. Experimental results are presented to confirm the effectiveness of the method. Comparisons with the bi-cubic spline and the mesh simplification methods demonstrate the merits of our method in reconstruction quality and representation size.