Query: Text detection in nature scene images using two-stage nontext filtering

Relevant passages:
Scene Text Detection via Connected Component Clustering and Nontext Filtering <=SEP=> In this paper, we present a new scene text detection algorithm based on two machine learning classifiers: one allows us to generate candidate word regions and the other filters out nontext ones. To be precise, we extract connected components (CCs) in images by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. Unlike conventional methods relying on heuristic rules in clustering, we train an AdaBoost classifier that determines the adjacency relationship and cluster CCs by using their pairwise relations. Then we normalize candidate word regions and determine whether each region contains text or not. Since the scale, skew, and color of each candidate can be estimated from CCs, we develop a text/nontext classifier for normalized images. This classifier is based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, we extend our approach to exploit multichannel information. Experimental results on ICDAR 2005 and 2011 robust reading competition datasets show that our method yields the state-of-the-art performance both in speed and accuracy.

Random Forests <=SEP=> Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

Scene text detection using graph model built upon maximally stable extremal regions <=SEP=> Scene text detection could be formulated as a bi-label (text and non-text regions) segmentation problem. However, due to the high degree of intraclass variation of scene characters as well as the limited number of training samples, single information source or classifier is not enough to segment text from non-text background. Thus, in this paper, we propose a novel scene text detection approach using graph model built upon Maximally Stable Extremal Regions (MSERs) to incorporate various information sources into one framework. Concretely, after detecting MSERs in the original image, an irregular graph whose nodes are MSERs, is constructed to label MSERs as text regions or non-text ones. Carefully designed features contribute to the unary potential to assess the individual penalties for labeling a MSER node as text or non-text, and color and geometric features are used to define the pairwise potential to punish the likely discontinuities. By minimizing the cost function via graph cut algorithm, different information carried by the cost function could be optimally balanced to get the final MSERs labeling result. The proposed method is naturally context-relevant and scale-insensitive. Experimental results on the ICDAR 2011 competition dataset show that the proposed approach outperforms state-of-the-art methods both in recall and

Scene Text Localization and Recognition with Oriented Stroke Detection <=SEP=> An unconstrained end-to-end text localization and recognition method is presented. The method introduces a novel approach for character detection and recognition which combines the advantages of sliding-window and connected component methods. Characters are detected and recognized as image regions which contain strokes of specific orientations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters. Additionally, a novel character representation efficiently calculated from the values obtained in the stroke detection phase is introduced. The representation is robust to shift at the stroke level, which makes it less sensitive to intra-class variations and the noise induced by normalizing character size and positioning. The effectiveness of the representation is demonstrated by the results achieved in the classification of real-world characters using an euclidian nearest-neighbor classifier trained on synthetic data in a plain form. The method was evaluated on a standard dataset, where it achieves state-of-the-art results in both text localization and recognition.

A Hybrid Approach to Detect and Localize Texts in Natural Scene Images <=SEP=> Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.

Clustering by Local Gravitation <=SEP=> The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct differences between the LRFs (including magnitudes and directions) of the data points close to the cluster centers and at the boundary of the clusters. To capture these differences efficiently, two new local measures named centrality and coordination are further investigated. Based on empirical observations, two new clustering methods called local gravitation clustering and communication with local agents are designed, and several test cases are conducted to verify their effectiveness. The experiments on synthetic data sets and real-world data sets indicate that both clustering approaches achieve good performance on most of the data sets.

Priming and human memory systems. <=SEP=> Priming is a nonconscious form of human memory, which is concerned with perceptual identification of words and objects and which has only recently been recognized as separate from other forms of memory or memory systems. It is currently under intense experimental scrutiny. Evidence is converging for the proposition that priming is an expression of a perceptual representation system that operates at a pre-semantic level; it emerges early in development, and access to it lacks the kind of flexibility characteristic of other cognitive memory systems. Conceptual priming, however, seems to be based on the operations of semantic memory.

UWB antipodal vivaldi antennas with protruded dielectric rods for higher gain, symmetric patterns and minimal phase center variations <=SEP=> In this paper, the authors proposed a technique to improve the gain, narrow the H-plane beamwidth, and minimize the phase center variations with frequency by utilizing Vivaldi antenna with a protruded dielectric rod. A sample antenna was fabricated and measured, and preliminary measured results are very promising, and in good agreement with the simulation results.

A theory of causal learning in children: causal maps and Bayes nets. <=SEP=> The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Investigating Personalized Search in E-Commerce <=SEP=> Personalized recommendations have become a common feature of many modern online services. In particular on ecommerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today’s sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site’s catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users’ general interests, their most recent browsing behavior, as well as the consideration of current sales trends.

Liquids permeation into HTV silicone rubber under AC/DC electric field <=SEP=> As shed and sheath materials, high temperature vulcanization (HTV) silicone rubber suffers from various types of environmental stresses in actual use. Liquids permeation, as a kind of silicone rubber aging factor, has been investigated by many researchers. However, most of the studies are conducted under the condition of no electric field. Since that composite insulators, especially the high voltage (HV) side, are under high electric field, it is worthy of attention on liquids permeation under AC/DC voltages. In this study, we utilized a new-designed liquids permeation test unit under AC/DC electric field to measure the weight of HTV silicone rubber specimens during the test process. Deionized water, as well as basic fuchsine ethanol solutions were used to verify the effects of electric field on liquids permeation. Besides, the influence of ATH content on the liquids permeation was involved.

Alternating Directions Dual Decomposition <=SEP=> We propose AD, a new algorithm for approximate maximum a posteriori (MAP) inference on factor graphs based on the alternating directions method of multipliers. Like dual decomposition algorithms, AD uses worker nodes to iteratively solve local subproblems and a controller node to combine these local solutions into a global update. The key characteristic of AD is that each local subproblem has a quadratic regularizer, leading to a faster consensus than subgradient-based dual decomposition, both theoretically and in practice. We provide closed-form solutions for these AD subproblems for binary pairwise factors and factors imposing first-order logic constraints. For arbitrary factors (large or combinatorial), we introduce an active set method which requires only an oracle for computing a local MAP configuration, making AD applicable to a wide range of problems. Experiments on synthetic and realworld problems show that AD compares favorably with the state-of-the-art.

Efficient Randomized Pattern-Matching Algorithms <=SEP=> We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find the first occurrence of X as a consecutive block within Y. The algorithms represent strings of length n by much shorter strings called fingerprints, and achieve their efficiency by manipulating fingerprints instead of longer strings. The algorithms require a constant number of storage locations, and essentially run in real time. They are conceptually simple and easy to implement. The method readily generalizes to higher-dimensional patternmatching problems.

On-Line Multi-View Video Summarization for Wireless Video Sensor Network <=SEP=> Battery lifetime is critical for wireless video sensors. To enable battery-powered wireless video sensors, low-power design is required. In this paper, we consider applying multi-view summarization to wireless video sensors to remove redundant contents such that the compression and transmission power can be reduced. A low-complexity online multi-view video summarization scheme is proposed. Experiments show that the proposed summarization method successfully reduces the video content while keeping important events. A power analysis of the system also shows that a significant amount of energy can be saved.

Publication bias in studies of an applied behavior-analytic intervention: an initial analysis. <=SEP=> Publication bias arises when studies with favorable results are more likely to be reported than are studies with null findings. If this bias occurs in studies with single-subject experimental designs(SSEDs) on applied behavior-analytic (ABA) interventions, it could lead to exaggerated estimates of intervention effects. Therefore, we conducted an initial test of bias by comparing effect sizes, measured by percentage of nonoverlapping data (PND), in published SSED studies (n=21) and unpublished dissertations (n=10) on 1 well-established intervention for children with autism, pivotal response treatment (PRT). Although published and unpublished studies had similar methodologies, the mean PND in published studies was 22% higher than in unpublished studies, 95% confidence interval (4%, 38%). Even when unpublished studies are included, PRT appeared to be effective (PNDM=62%). Nevertheless, the disparity between published and unpublished studies suggests a need for further assessment of publication bias in the ABA literature.

MLMD: Multi-Layered Visualization for Multi-Dimensional Data <=SEP=> In this paper, we propose and implement a multi-layered visualization technique in 3D space called Multi-Layered Visualization for Multi-Dimensional Data (MLMD) with its corresponding interaction techniques, for visualizing multi-dimensional data. Layers of point based plots are stacked and connected in a virtual visualization cube for comparison between different dimension settings. Viewed from the side, the plot layers intrinsically form parallel coordinates plots. MLMD integrates point based plots and parallel coordinates compactly so as to present more information at a time to help data investigation. Pertinent user interactions for MLMD method to enable convenient manipulation are designed. By using MLMD and its matching interaction techniques, proper dimension settings and in-depth data perception can be achieved.

MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks <=SEP=> Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S [1], an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT) [2] to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed [3]. Implementation aspects, open challenges and future work are also presented.

An analysis of review content and reviewer variables that contribute to review helpfulness <=SEP=> Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

SAM: Semantic Attribute Modulated Language Modeling <=SEP=> As a fundamental task in the natural language processing field, language modeling aims to estimate the distribution of the word sequences. However, the most existing algorithms have focused on the main texts while often ignoring the vastly-accessible semantic attributes of the documents, e.g., titles, authors, sentiments and tags. To address this issue, we propose Semantic Attribute Modulated (SAM) language modeling, a novel language modeling framework that incorporates the various semantic attributes. Attributes are selected automatically with an attribute attention mechanism. We build three text datasets with a diversity of semantic attributes. On the three text datasets, we empirically examine the language model perplexities of several typical corpora, and then demonstrate the superiority of our model with the different combinations of the attributes. Extensive qualitative results, including word semantic analysis, attention values and an interesting lyric generation, further demonstrate the effectiveness of our SAMmethod.

COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints <=SEP=> Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to obtain a good clustering by querying the most informative pairs first. Ideally, a user should be able to answer a couple of these queries, inspect the resulting clustering, and repeat these two steps until a satisfactory result is obtained. We present COBRAS, an approach to active clustering with pairwise constraints that is suited for such an interactive clustering process. A core concept in COBRAS is that of a super-instance: a local region in the data in which all instances are assumed to belong to the same cluster. COBRAS constructs such superinstances in a top-down manner to produce highquality results early on in the clustering process, and keeps refining these super-instances as more pairwise queries are given to get more detailed clusterings later on. We experimentally demonstrate that COBRAS produces good clusterings at fast run times, making it an excellent candidate for the iterative clustering scenario outlined above.

TSDF Manifolds: A Scalable and Consistent Dense Mapping Approach <=SEP=> In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a manifold map, comprised of overlapping Truncated Signed Distance Field (TSDF) volumes. These volumes are localized through featurebased bundle adjustment. Our main contribution is to use a probabilistic measure to identify stable regions in the map, and fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on publicly available datasets, as well as on a number of our own datasets, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps.

The DRAM Latency PUF: Quickly Evaluating Physical Unclonable Functions by Exploiting the Latency-Reliability Tradeoff in Modern Commodity DRAM Devices <=SEP=> Physically Unclonable Functions (PUFs) are commonly used in cryptography to identify devices based on the uniqueness of their physical microstructures. DRAM-based PUFs have numerous advantages over PUF designs that exploit alternative substrates: DRAM is a major component of many modern systems, and a DRAM-based PUF can generate many unique identiers. However, none of the prior DRAM PUF proposals provide implementations suitable for runtime-accessible PUF evaluation on commodity DRAM devices. Prior DRAM PUFs exhibit unacceptably high latencies, especially at low temperatures (e.g., >125.8s on average for a 64KiB memory segment below 55C), and they cause high system interference by keeping part of DRAM unavailable during PUF evaluation. In this paper, we introduce the DRAM latency PUF, a new class of fast, reliable DRAM PUFs. The key idea is to reduce DRAM read access latency below the reliable datasheet specications using software-only system calls. Doing so results in error patterns that reect the compound eects of manufacturing variations in various DRAM structures (e.g., capacitors, wires, sense ampli- ers). Based on a rigorous experimental characterization of 223 modern LPDDR4 DRAM chips, we demonstrate that these error patterns 1) satisfy runtime-accessible PUF requirements, and 2) are quickly generated (i.e., at 88.2ms) irrespective of operating temperature using a real system with no additional hardware modications. We show that, for a constant DRAM capacity overhead of 64KiB, our implementation of the DRAM latency PUF enables an average (minimum, maximum) PUF evaluation time speedup of 152x (109x, 181x) at 70C and 1426x (868x, 1783x) at 55C when compared to a DRAM retention PUF and achieves greater speedups at even lower temperatures.

Local Rademacher Complexity for Multi-Label Learning <=SEP=> We analyze the local Rademacher complexity of empirical risk minimization-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound. Compared with methods that minimize over all singular values, concentrating on the tail singular values results in better recovery of the low-rank structure of the multi-label predictor, which plays an important role in exploiting label correlations. We propose a new conditional singular value thresholding algorithm to solve the resulting objective function. Moreover, a variance control strategy is employed to reduce the variance of variables in optimization. Empirical studies on real-world data sets validate our theoretical results and demonstrate the effectiveness of the proposed algorithm for multi-label learning.

A Novel UWB Monopole Antenna With Tunable Notched Behavior Using Varactor Diode <=SEP=> This letter presents a novel ultrawideband (UWB) antenna with tunable notched band. The antenna is assembled on an FR4 substrate with thickness of 0.8 mm and εr = 4.4. By inserting a π-shaped slot on the radiating patch, band-notch function is achieved. By loading the slot with lumped varactor, tunability of the created notch would be possible. Based on this technique, an electronically controlled notched-band antenna is designed and fabricated including a single varactor diode with a varying capacitance value of 0.63-2.67 pF. Using the cited varactor, notched band tunability of 2.7-7.2 GHz is obtained.

A multilevel relaxation algorithm for simultaneous localization and mapping <=SEP=> This paper addresses the problem of simultaneous localization and mapping (SLAM) by a mobile robot. An incremental SLAM algorithm is introduced that is derived from multigrid methods used for solving partial differential equations. The approach improves on the performance of previous relaxation methods for robot mapping, because it optimizes the map at multiple levels of resolution. The resulting algorithm has an update time that is linear in the number of estimated features for typical indoor environments, even when closing very large loops, and offers advantages in handling nonlinearities compared with other SLAM algorithms. Experimental comparisons with alternative algorithms using two well-known data sets and mapping results on a real robot are also presented.

Tree-Based Batch Mode Reinforcement Learning <=SEP=> Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.

Efficient Learning for Undirected Topic Models <=SEP=> Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.

Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. <=SEP=> Multiple sclerosis (MS) is a disease with profound heterogeneity in clinical course, neuroradiological appearance of the lesions, involvement of susceptibility gene loci, and response to therapy. These features are supported by experimental evidence, which demonstrates that fundamentally different processes, such as autoimmunity or virus infection, may induce MS-like inflammatory demyelinating plaques and suggest that MS may be a disease with heterogeneous pathogenetic mechanisms. From a large pathology sample of MS, collected in three international centers, we selected 51 biopsies and 32 autopsies that contained actively demyelinating lesions defined by stringent criteria. The pathology of the lesions was analyzed using a broad spectrum of immunological and neurobiological markers. Four fundamentally different patterns of demyelination were found, defined on the basis of myelin protein loss, the geography and extension of plaques, the patterns of oligodendrocyte destruction, and the immunopathological evidence of complement activation. Two patterns (I and II) showed close similarities to T-cell-mediated or T-cell plus antibody-mediated autoimmune encephalomyelitis, respectively. The other patterns (III and IV) were highly suggestive of a primary oligodendrocyte dystrophy, reminiscent of virus- or toxin-induced demyelination rather than autoimmunity. At a given time point of the disease--as reflected in autopsy cases--the patterns of demyelination were heterogeneous between patients, but were homogenous within multiple active lesions from the same patient. This pathogenetic heterogeneity of plaques from different MS patients may have fundamental implications for the diagnosis and therapy of this disease.

Augmenting web pages and search results to support credibility assessment <=SEP=> The presence (and, sometimes, prominence) of incorrect and misleading content on the Web can have serious consequences for people who increasingly rely on the internet as their information source for topics such as health, politics, and financial advice. In this paper, we identify and collect several page features (such as popularity among specialized user groups) that are currently difficult or impossible for end users to assess, yet provide valuable signals regarding credibility. We then present visualizations designed to augment search results and Web pages with the most promising of these features. Our lab evaluation finds that our augmented search results are particularly effective at increasing the accuracy of users'" credibility assessments, highlighting the potential of data aggregation and simple interventions to help people make more informed decisions as they search for information online.

High-Frequency Behavior of Graphene-Based Interconnects—Part I: Impedance Modeling <=SEP=> This paper presents the first detailed methodology for the accurate evaluation of high-frequency impedance of graphene-based structures relevant to on-chip interconnect and inductor applications. Going beyond the simplifying assumptions of Ohm's law, the effects of electric-field variation within a mean free path and current dependency on the nonlocal electric-field are taken into account to accurately capture the high-frequency behavior of graphene ribbons (GRs). At the same time, a simplified approach that may be adopted at lower frequencies is also explained. Starting from the basic Boltzmann equation and combining with the unique dispersion relation for graphene in its hexagonal Brillouin zone, the current density across the GR structure is derived. First, a semi-infinite slab of GR is analyzed using the theory of Fourier integrals, which is followed by the development of a rigorous methodology for practical finite structures based on a self-consistent numerical calculation of the derived current density using the Green's function approach.