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https://www.readbyqxmd.com/read/28820891/scene-text-detection-via-extremal-region-based-double-threshold-convolutional-network-classification
#1
Wei Zhu, Jing Lou, Longtao Chen, Qingyuan Xia, Mingwu Ren
In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating saliency detection methods, which ensures a high recall rate. Given a natural image, character candidates are extracted from three channels in a perception-based illumination invariant color space by saliency-enhanced MSER algorithm. A discriminative convolutional neural network (CNN) is jointly trained with multi-level information including pixel-level and character-level information as character candidate classifier...
2017: PloS One
https://www.readbyqxmd.com/read/28814625/variations-in-cause-of-death-determination-for-sudden-unexpected-infant-deaths
#2
Carrie K Shapiro-Mendoza, Sharyn E Parks, Jennifer Brustrom, Tom Andrew, Lena Camperlengo, John Fudenberg, Betsy Payn, Dale Rhoda
OBJECTIVES: To quantify and describe variation in cause-of-death certification of sudden unexpected infant deaths (SUIDs) among US medical examiners and coroners. METHODS: From January to November 2014, we conducted a nationally representative survey of US medical examiners and coroners who certify infant deaths. Two-stage unequal probability sampling with replacement was used. Medical examiners and coroners were asked to classify SUIDs based on hypothetical scenarios and to describe the evidence considered and investigative procedures used for cause-of-death determination...
June 5, 2017: Pediatrics
https://www.readbyqxmd.com/read/28813976/megane-pro-myo-electricity-visual-and-gaze-tracking-data-acquisitions-to-improve-hand-prosthetics
#3
Francesca Giordaniello, Matteo Cognolato, Mara Graziani, Arjan Gijsberts, Valentina Gregori, Gianluca Saetta, Anne-Gabrielle Mittaz Hager, Cesare Tiengo, Franco Bassetto, Peter Brugger, Barbara Caputo, Henning Muller, Manfredo Atzori
During the past 60 years scientific research proposed many techniques to control robotic hand prostheses with surface electromyography (sEMG). Few of them have been implemented in commercial systems also due to limited robustness that may be improved with multimodal data. This paper presents the first acquisition setup, acquisition protocol and dataset including sEMG, eye tracking and computer vision to study robotic hand control. A data analysis on healthy controls gives a first idea of the capabilities and constraints of the acquisition procedure that will be applied to amputees in a next step...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813033/a-vision-based-wayfinding-system-for-visually-impaired-people-using-situation-awareness-and-activity-based-instructions
#4
Eunjeong Ko, Eun Yi Kim
A significant challenge faced by visually impaired people is 'wayfinding', which is the ability to find one's way to a destination in an unfamiliar environment. This study develops a novel wayfinding system for smartphones that can automatically recognize the situation and scene objects in real time. Through analyzing streaming images, the proposed system first classifies the current situation of a user in terms of their location. Next, based on the current situation, only the necessary context objects are found and interpreted using computer vision techniques...
August 16, 2017: Sensors
https://www.readbyqxmd.com/read/28796618/learning-based-shadow-recognition-and-removal-from-monochromatic-natural-images
#5
Mingliang Xu, Jiejie Zhu, Pei Lv, Bing Zhou, Marshall F Tappen, Rongrong Ji
This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in the literature, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning based shadow recognition and removal scheme is proposed to tackle the challenges above...
August 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28777830/multivariate-bayesian-decoding-of-single-trial-event-related-fmri-responses-for-memory-retrieval-of-voluntary-actions
#6
Dongha Lee, Sungjae Yun, Changwon Jang, Hae-Jeong Park
This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses...
2017: PloS One
https://www.readbyqxmd.com/read/28761440/fuzzy-classification-of-high-resolution-remote-sensing-scenes-using-visual-attention-features
#7
Linyi Li, Tingbao Xu, Yun Chen
In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28759406/variations-in-cause-of-death-determination-for-sudden-unexpected-infant-deaths
#8
Carrie K Shapiro-Mendoza, Sharyn E Parks, Jennifer Brustrom, Tom Andrew, Lena Camperlengo, John Fudenberg, Betsy Payn, Dale Rhoda
OBJECTIVES: To quantify and describe variation in cause-of-death certification of sudden unexpected infant deaths (SUIDs) among US medical examiners and coroners. METHODS: From January to November 2014, we conducted a nationally representative survey of US medical examiners and coroners who certify infant deaths. Two-stage unequal probability sampling with replacement was used. Medical examiners and coroners were asked to classify SUIDs based on hypothetical scenarios and to describe the evidence considered and investigative procedures used for cause-of-death determination...
July 2017: Pediatrics
https://www.readbyqxmd.com/read/28749351/point-light-source-position-estimation-from-rgb-d-images-by-learning-surface-attributes
#9
Sezer Karaoglu, Yang Liu, Theo Gevers, Arnold W M Smeulders
Light source position estimation is a difficult yet an important problem in computer vision. A common approach for estimating the light source position (LSP) assumes Lambert's law. However, in real-world scenes, Lambert's law does not hold for all different types of surfaces. Instead of assuming all that surfaces follow Lambert's law, our approach classifies image surface segments based on their photometric and geometric surface attributes (i.e. glossy, matte, curved etc.) and assigns weights to image surface segments based on their suitability for LSP estimation...
July 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28663068/interpreting-the-dimensions-of-neural-feature-representations-revealed-by-dimensionality-reduction
#10
Erin Goddard, Colin Klein, Samuel G Solomon, Hinze Hogendoorn, Thomas A Carlson
Recent progress in understanding the structure of neural representations in the cerebral cortex has centred around the application of multivariate classification analyses to measurements of brain activity. These analyses have proved a sensitive test of whether given brain regions provide information about specific perceptual or cognitive processes. An exciting extension of this approach is to infer the structure of this information, thereby drawing conclusions about the underlying neural representational space...
June 26, 2017: NeuroImage
https://www.readbyqxmd.com/read/28661440/online-recognition-of-daily-activities-by-color-depth-sensing-and-knowledge-models
#11
Carlos Fernando Crispim-Junior, Alvaro Gómez Uría, Carola Strumia, Michal Koperski, Alexandra König, Farhood Negin, Serhan Cosar, Anh Tuan Nghiem, Duc Phu Chau, Guillaume Charpiat, Francois Bremond
Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect(®) sensor)...
June 29, 2017: Sensors
https://www.readbyqxmd.com/read/28644809/structured-kernel-dictionary-learning-with-correlation-constraint-for-object-recognition
#12
Zhengjue Wang, Yinghua Wang, Hongwei Liu, Hao Zhang
In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes...
June 21, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641536/overview-of-forensic-toxicology-yesterday-today-and-in-the-future
#13
Heesun Chung, Sanggil Choe
BACKGROUND: The scope of forensic toxicology has been tremendously expanded over the past 50 years. From two general sections forensic toxicology can be further classified into 8-9 sections. METHODS: The most outstanding improvement in forensic toxicology is the changes brought by instrumental development. The field of forensic toxicology was revolutionized by the development of immunoassay and bench-top GC-MS in the 1980's and LC-MS-MS in 2000's. Detection of trace amounts of analytes has allowed the use of new specimens such as hair and oral fluids, along with blood and urine...
June 22, 2017: Current Pharmaceutical Design
https://www.readbyqxmd.com/read/28604832/insects-and-associated-arthropods-analyzed-during-medicolegal-death-investigations-in-harris-county-texas-usa-january-2013-april-2016
#14
Michelle R Sanford
The application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner's office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA...
2017: PloS One
https://www.readbyqxmd.com/read/28600256/a-graphical-model-for-online-auditory-scene-modulation-using-eeg-evidence-for-attention
#15
Marzieh Haghighi, Mohammad Moghadamfalahi, Murat Akcakaya, Deniz Erdogmus
Recent findings indicate that brain interfaces have the potential to enable attention-guided auditory scene analysis and manipulation in applications such as hearing aids and augmented/ virtual environments. Specifically, noninvasively acquired electroencephalography (EEG) signals have been demonstrated to carry some evidence regarding which of multiple synchronous speech waveforms the subject attends to. In this paper we demonstrate that: (1) using data- and model-driven cross-correlation features yield competitive binary auditory attention classification results with at most 20 seconds of EEG from 16 channels or even a single well-positioned channel; (2) a model calibrated using equal-energy speech waveforms competing for attention could perform well on estimating attention in closed-loop unbalancedenergy speech waveform situations, where the speech amplitudes are modulated by the estimated attention posterior probability distribution; (3) such a model would perform even better if it is corrected (linearly, in this instance) based on EEG evidence dependency on speech weights in the mixture; (4) calibrating a model based on population EEG could result in acceptable performance for new individuals/users; therefore EEG-based auditory attention classifiers may generalize across individuals, leading to reduced or eliminated calibration time and effort...
June 6, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28592372/scene-time-interval-and-good-neurological-recovery-in-out-of-hospital-cardiac-arrest
#16
Ki Hong Kim, Sang Do Shin, Kyoung Jun Song, Young Sun Ro, Yu Jin Kim, Ki Jeong Hong, Joo Jeong
OBJECTIVES: It is unclear whether scene time interval (STI) is associated with better neurological recovery in the emergency medical service (EMS) system with intermediate service level. METHODS: Adult out-of-hospital cardiac arrest (OHCA) patients with presumed cardiac etiology (2012 to 2014) were analyzed, excluding patients not-resuscitated, occurred in ambulance/medical/nursing facility, unknown STI or extremely longer STI (>60 min), and unknown outcomes...
May 29, 2017: American Journal of Emergency Medicine
https://www.readbyqxmd.com/read/28493106/human-classifier-observers-can-deduce-task-solely-from-eye-movements
#17
Brett Bahle, Mark Mills, Michael D Dodd
Computer classifiers have been successful at classifying various tasks using eye movement statistics. However, the question of human classification of task from eye movements has rarely been studied. Across two experiments, we examined whether humans could classify task based solely on the eye movements of other individuals. In Experiment 1, human classifiers were shown one of three sets of eye movements: Fixations, which were displayed as blue circles, with larger circles meaning longer fixation durations; Scanpaths, which were displayed as yellow arrows; and Videos, in which a neon green dot moved around the screen...
May 10, 2017: Attention, Perception & Psychophysics
https://www.readbyqxmd.com/read/28476562/a-novel-alignment-free-method-to-classify-protein-folding-types-by-combining-spectral-graph-clustering-with-chou-s-pseudo-amino-acid-composition
#18
Pooja Tripathi, Paras N Pandey
The present work employs pseudo amino acid composition (PseAAC) for encoding the protein sequences in their numeric form. Later this will be arranged in the similarity matrix, which serves as input for spectral graph clustering method. Spectral methods are used previously also for clustering of protein sequences, but they uses pair wise alignment scores of protein sequences, in similarity matrix. The alignment score depends on the length of sequences, so clustering short and long sequences together may not good idea...
May 3, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28475756/shared-states-using-mvpa-to-test-neural-overlap-between-self-focused-emotion-imagery-and-other-focused-emotion-understanding
#19
Suzanne Oosterwijk, Lukas Snoek, Mark Rotteveel, Lisa F Barrett, H Steven Scholte
The present study tested whether the neural patterns that support imagining "performing an action", "feeling a bodily sensation" or "being in a situation" are directly involved in understanding other people's actions, bodily sensations and situations. Subjects imagined the content of short sentences describing emotional actions, interoceptive sensations and situations (self-focused task), and processed scenes and focused on how the target person was expressing an emotion, what this person was feeling, and why this person was feeling an emotion (other-focused task)...
May 5, 2017: Social Cognitive and Affective Neuroscience
https://www.readbyqxmd.com/read/28454031/automatic-and-adaptive-paddy-rice-mapping-using-landsat-images-case-study-in-songnen-plain-in-northeast-china
#20
Bingwen Qiu, Difei Lu, Zhenghong Tang, Chongcheng Chen, Fengli Zou
Spatiotemporal explicit information on paddy rice distribution is essential for ensuring food security and sustainable environmental management. Paddy rice mapping algorithm through the Combined Consideration of Vegetation phenology and Surface water variations (CCVS) has been efficiently applied based on the 8day composites time series datasets. However, the great challenge for phenology-based algorithms introduced by unpromising data availability in middle/high spatial resolution imagery, such as frequent cloud cover and coarse temporal resolution, remained unsolved...
November 15, 2017: Science of the Total Environment
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