A new efficient similarity metric and generic computation strategy for pattern-based very low bit-rate video coding

In the context of very low bit-rate video coding, pattern representations of a moving region (MR) in block-based motion estimation and compensation has become increasingly attractive. Generally, all existing pattern-matching algorithms apply a similarity metric, involving elementary operations, to compute the mismatch between an MR and a particular fixed pattern in order to select the best-matching pattern from a fixed-size codebook of predefined patterns. An efficient similarity metric, together with a new generic computation strategy, is presented by considering only the mismatch areas of MRs. It is theoretically proven that for a specific MR in a macroblock, the new similarity metric selects exactly the same pattern as existing metrics, while the resulting computational coding efficiency is improved by between 21% and 58% compared with the H.263 low bit-rate coding standard

The Role of Regulated mRNA Stability in Establishing Bicoid Morphogen Gradient in Drosophila Embryonic Development

The Bicoid morphogen is amongst the earliest triggers of differential spatial pattern of gene expression and subsequent cell fate determination in the embryonic development of Drosophila. This maternally deposited morphogen is thought to diffuse in the embryo, establishing a concentration gradient which is sensed by downstream genes. In most model based analyses of this process, the translation of the bicoid mRNA is thought to take place at a fixed rate from the anterior pole of the embryo and a supply of the resulting protein at a constant rate is assumed. Is this process of morphogen generation a passive one as assumed in the modelling literature so far, or would available data support an alternate hypothesis that the stability of the mRNA is regulated by active processes? We introduce a model in which the stability of the maternal mRNA is regulated by being held constant for a length of time, followed by rapid degradation. With this more realistic model of the source, we have analysed three computational models of spatial morphogen propagation along the anterior-posterior axis: (a) passive diffusion modelled as a deterministic differential equation, (b) diffusion enhanced by a cytoplasmic flow term; and (c) diffusion modelled by stochastic simulation of the corresponding chemical reactions. Parameter estimation on these models by matching to publicly available data on spatio-temporal Bicoid profiles suggests strong support for regulated stability over either a constant supply rate or one where the maternal mRNA is permitted to degrade in a passive manner

THE INVESTIGATION ON ARABIC WORD POSE ESTIMATION ALGORITHM AS MARKER FOR AUGMENTED REALITY APPLICATION

This study investigates which combination of matching technique with Infinitesimal Plane-Based Pose Estimation (IPPE) that suits better in estimating the pose of Arabic text images without character segmentation. The pattern matching technique involves are Speeded-Up Robust Features (SURF) and Affine Scale Invariant Feature Transform (ASIFT). The experiment is demonstrated in Arabic word images from different angles of viewpoints. The algorithms are tested on a dataset chosen from a few words within Surah Al-Fatihah in the Quran. The total of 260 images was taken from left and right side of the image. Then, a set of sub-words were recognized and tested the performance. This study will focus on comparing the performance of the technique against Arabic words in two sub-words or one sub-word form. We will evaluate the performance through analyzing the matching accuracy rate and how it affects the pose estimation. Based on results obtained for the pattern matching technique performance on Arabic scripts, SURF shows a better accuracy rate and execution time compared to another algorithm. This experiment result is used as a guide in estimating a pose of the target images in different sub-words. The overall results of the study signify that good IPPE pose does not rely on the accuracy rate of matching inliers with original interest points. The study also demonstrates that one sub-words shows a better accuracy rate than with two sub-words cause by unnecessary interest points detected

High frame rate vector flow imaging of stenotic carotid bifurcation: computational modeling and analysis

Poster Session Session P1Aa. Beam Formation: Computational Aspects And Artifact Reduction: no. P1Ab-1Analysis of the complex blood flow pattern in the carotid bifurcation is clinically important to the diagnosis of carotid stenoses. We hypothesize that the use of high frame rate imaging methods such as plane wave excitation, together with vector flow estimators like block matching, may potentially be a suitable imaging problem to this problem. This paper presents our team’s initial efforts in developing a high frame rate vector flow imaging framework that is based on plane wave excitation principles and a high dynamic range block matching algorithm that incorporates least squares fitting principles. We have conducted a series of Field II simulations on straight tubes and carotid bifurcation to evaluate the estimation accuracy and imaging performance of our framework. Results indicate that high-frame-rate vector flow imaging is capable of visualizing complex blood flow. It has potential to be further developed into a new clinical technique for vascular diagnoses.published_or_final_versionThe 2011 IEEE International Ultrasonics Symposium (IUS), Orlando, FL., 18-21 October 2011. In IEEE International Ultrasonics Symposium Proceedings, 2011, p. 409-41

Extracting protein-protein interaction based on discriminative training of the Hidden Vctor State model

The knowledge about gene clusters and protein interactions is important for biological researchers to unveil the mechanism of life. However, large quantity of the knowledge often hides in the literature, such as journal articles, reports, books and so on. Many approaches focusing on extracting information from unstructured text, such as pattern matching, shallow and deep parsing, have been proposed especially for extracting protein-protein interactions (Zhou and He, 2008). A semantic parser based on the Hidden Vector State (HVS) model for extracting protein-protein interactions is presented in (Zhou et al., 2008). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. Maximum Likelihood estimation (MLE) is used to derive the parameters of the HVS model. In this paper, we propose a discriminative approach based on parse error measure to train the HVS model. To adjust the HVS model to achieve minimum parse error rate, the generalized probabilistic descent (GPD) algorithm (Kuo et al., 2002) is used. Experiments have been conducted on the GENIA corpus. The results demonstrate modest improvements when the discriminatively trained HVS model outperforms its MLE trained counterpart by 2.5% in F-measure on the GENIA corpus

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie