Abstract

The problem of recognizing text in images taken in the wild has gained significant attention from the computer vision community in recent years. Contrary to recognition of printed documents, recognizing scene text is a challenging problem. We focus on the problem of recognizing text extracted from natural scene images and the web. Significant attempts have been made to address this problem in the recent past. However, many of these works benefit from the availability of strong context, which naturally limits their applicability. In this work we present a framework that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary. We show experimental results on publicly available datasets. Furthermore, we introduce a large challenging word dataset with five thousand words to evaluate various steps of our method exhaustively. The main contributions of this work are: (1) We present a framework, which incorporates higher order statistical language models to recognize words in an unconstrained manner (i.e. we overcome the need for restricted word lists, and instead use an English dictionary to compute the priors). (2) We achieve significant improvement (more than 20%) in word recognition accuracies without using a restricted word list. (3) We introduce a large word recognition dataset (atleast 5 times larger than other public datasets) with character level annotation and benchmark it.

Abstract

Most current vision algorithms deliver their output ‘as is’, without indicating whether it is correct or not. In this paper we propose evaluator algorithms that predict if a vision algorithm has succeeded. We illustrate this idea for the case of Human Pose Estimation (HPE). We describe the stages required to learn and test an evaluator, including the use of an annotated ground truth dataset for training and testing the evaluator (and we provide a new dataset for the HPE case), and the development of auxiliary features that have not been used by the (HPE) algorithm, but can be learnt by the evaluator to predict if the output is correct or not. Then an evaluator is built for each of four recently developed HPE algorithms using their publicly available implementations: Eichner and Ferrari [5], Sapp et al. [16], Andriluka et al. [2] and Yang and Ramanan [22]. We demonstrate that in each case the evaluator is able to predict if the algorithm has correctly estimated the pose or not.

Abstract

Image analysis tasks such as classification, clustering, detection, and retrieval are only as good as the feature representation of the images they use. Much research in computer vision is focused on finding better or semantically richer image representations. Bag of visual Words (BoW) is a representation that has emerged as an effective one for a variety of computer vision tasks. BoW methods traditionally use low level features. We have devised a strategy to use these low level features to create “higher level” features by making use of the spatial context in images. In this paper, we propose a novel hierarchical feature learning framework that uses a Naive Bayes Clustering algorithm to convert a 2-D symbolic image at one level to a 2-D symbolic image at the next level with richer features. On two popular datasets, Pascal VOC 2007 and Caltech 101, we empirically show that classification accuracy obtained from the hierarchical features computed using our approach is significantly higher than the traditional SIFT based BoW representation of images even though our image representations are more compact.

Abstract

Frequent Itemset Mining (FISM) attempts to find large and frequent itemsets in bag-of-items data such as retail market baskets. Such data has two properties that are not naturally addressed by FISM: (i) a market basket might contain items from more than one customer intent (mixture property) and (ii) only a subset of items related to a customer intent are present in most market baskets (projection property). We propose a simple and robust framework called LOGICAL ITEMSET MINING (LISM) that treats each market basket as a mixture-of, projections-of, latent customer intents. LISM attempts to discover logical itemsets from such bagof-items data. Each logical itemset can be interpreted as a latent customer intent in retail or semantic concept in text tagsets. While the mixture and projection properties are easy to appreciate in retail domain, they are present in almost all types of bag-of-items data. Through experiments on two large datasets, we demonstrate the quality, novelty, and actionability of logical itemsets discovered by the simple, scalable, and aggressively noise-robust LISM framework. We conclude that while FISM discovers a large number of noisy, observed, and frequent itemsets, LISM discovers a small number of high quality, latent logical itemsets.

Abstract

In one aspect, the invention relates to methods for enhancing a Raman signal comprising the steps of providing a sample comprising a metal surface, an analyte adhered to the surface, and a metallic nanoparticle coupled to the surface, wherein the nanoparticle has a plasmon resonance band; exposing the sample to incident energy of an excitation wavelength; and detecting the Raman signal of the analyte. In a further aspect, the invention relates to a compositiion comprising a metal surface, a functionalized self-assembled monolayer adhered to the surface, wherin the self-assembled monolayer comprises an analyte, and a cetyltrimethylammonium halide-capped metallic nanoparticle coupled to the surface. In a further aspect, the invention relates to a cetyltrimethylammonium bromide-capped gold nanoparticle and a method for preparing same. This abstract is intended as a scanning tool for purposes of …

Abstract

Gold nanostars, possessing multiple sharp spikes, have emerged as promising plasmonic particles in the field of ultrasensitive sensing. We have developed a water-based method for high-yield synthesis of size-tunable anisotropic gold nanoparticles with a varying number of spiky surface protrusions, and performed systematic experimental and theoretical analyses of the optical properties of the single gold nanostars by characterizing them simultaneously with scanning electron microscopy and dark-field scattering spectroscopy. The morphologies and corresponding scattering spectra of the individual gold nanostars have been compared with electromagnetic simulations of the plasmonic resonances utilizing the finite-difference time-domain (FDTD) method. The study provides a correlation between the experimental and calculated scattering spectra and charge distributions of the different plasmon modes in the …

Abstract

The invention provides a method for manufacturing supported noble metal based alloy catalysts with a high degree of alloying and a small crystallite size. The method involves using polyol solvents as reaction medium and comprises a two-step reduction process in the presence of a support material. In the first step, the first metal (transition metal; eg Co, Cr, Ru) is activated by increasing the reaction temperature to 80 to 160 C. In the second step, the second metal (noble metal; eg Pt, Pd, Au) is added and the slurry is heated to the boiling point of the polyol solvent in a range of 160 to 300 C. The catalysts manufactured according to the method are used as electrocatalysts for polymer electrolyte membrane fuel cells (PEMFC), direct-methanol fuel cells (DMFC) or as gas phase catalysts for CO oxidation or exhaust gas purification.

Abstract

The combination of molecular dynamics simulation and neutron scattering techniques has emerged as a highly synergistic approach to elucidate the atomistic details of the structure, dynamics, and functions of biological systems. Simulation models can be tested by calculating neutron scattering structure factors and comparing the results directly with experiments. If the scattering profiles agree, the simulations can be used to provide a detailed decomposition and interpretation of the experiments, and if not, the models can be rationally adjusted. Comparison with neutron experiment can be made at the level of the scattering functions or, less directly, of structural and dynamical quantities derived from them. Here, we examine the combination of simulation and experiment in the interpretation of SANS and inelastic scattering experiments on the structure and dynamics of proteins and other biopolymers.

Abstract

An analytical approach is developed for reconstructing site-specific methyl-bearing protein side-chain conformational energy surfaces from NMR methyl axis order parameters (Oaxis2). Application of an enhanced sampling algorithm (adaptive biasing force) to molecular dynamics simulation of a protein, calcium-bound calmodulin, reveals a nonlinear correlation between Oaxis2 and the populations of rotamer states of protein side-chains, permitting the rotamer populations to be extracted directly from Oaxis2. The analytical approach yields side-chain conformational distributions that are in excellent agreement with those obtained from the enhanced-sampling MD results.

Abstract

A model of crop specific virtual labs is proposed to improve practical agricultural education by considering the agricultural educa-tion system in India. In agricultural education, the theoretical concepts are being imparted through class room lectures and laboratory skills are imparted in the dedicated laboratories. Further, practical agricultural education is being imparted by exposing the students to the field prob-lems through Rural Agricultural Work Experience Program (RAWEP),experiential learning and internships. In spite of these efforts, there isa feeling that the level of practical skills exposed to the students is not up to the desired level. So we have to devise the new ways and means to enhance the practical knowledge and skills of agricultural students to understand the real-time crop problems and provide the corrective stepsat the field level. Recent developments in ICTs, thus, provide an oppor-tunity to improve practical education by developing virtual crop labs.The virtual crop labs contain a well organized, indexed and summarized digital data (text, photograph, and video). The digital data corresponds to farm situations reflecting life cycles of several farms of different crops cultivated under diverse farming conditions. The practical knowledge of the students could be improved, if we systematically expose them to virtual crop labs along with course teaching. We can employ cloud com-puting platform to store huge amounts of data and render to students and other stakeholders in an online manner