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Scene classifier

Lars O M Rothkegel, Hans A Trukenbrod, Heiko H Schütt, Felix A Wichmann, Ralf Engbert
During scene perception our eyes generate complex sequences of fixations. Predictors of fixation locations are bottom-up factors such as luminance contrast, top-down factors like viewing instruction, and systematic biases e.g. the tendency to place fixations near the center of an image. However, comparatively little is known about the dynamics of scanpaths after experimental manipulation of specific fixation locations. Here we investigate the influence of initial fixation position on subsequent eye-movement behavior on an image...
October 20, 2016: Vision Research
Daniel Kaiser, Nikolaas N Oosterhof, Marius V Peelen
: The human visual system can only represent a small subset of the many objects present in cluttered scenes at any given time, such that objects compete for representation. Despite these processing limitations, the detection of object categories in cluttered natural scenes is remarkably rapid. How does the brain efficiently select goal-relevant objects from cluttered scenes? In the present study, we used multivariate decoding of magneto-encephalography (MEG) data to track the neural representation of within-scene objects as a function of top-down attentional set...
October 12, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Robert A Stern, Linda M Abularach, Daniel R Seichepine, Michael L Alosco, Brandon E Gavett, Yorghos Tripodis
BACKGROUND: A multitest approach is optimal for the identification of at-risk driving among older adults. This study examined the predictive validity of a combination of office-based screening tests for on-road driving performance in older adults with and without mild cognitive impairment (MCI)/dementia. METHODS: Forty-four normal control, 20 participants with MCI, and 20 participants with dementia completed a battery of office-based assessments. On-road driving evaluation classified participants as not at-risk (n = 65) or at-risk drivers (n = 19)...
September 19, 2016: Journal of Geriatric Psychiatry and Neurology
Hyung Il Koo
Camera-based text processing has attracted considerable attention and numerous methods have been proposed. However, most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images. In this paper, we present a text-line detection algorithm for camera-captured document images, which is an essential step towards document understanding. In particular, our method is developed by incorporating state estimation (an extension of scale selection) into a connected component (CC)-based framework...
September 8, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jun Wang, Kevin Kai Xu
LiDAR scanning has become a prevalent technique for digitalizing large-scale outdoor scenes. However, the raw LiDAR data often contain imperfections, e.g., missing large regions, anisotropy of sampling density, and contamination of noise and outliers, which are the major obstacles that hinder its more ambitious and higher level applications in digital city modeling. Observing that 3D urban scenes can be locally described with several low dimensional subspaces, we propose to locally classify the neighborhoods of the scans to model the substructures of the scenes...
August 31, 2016: IEEE Transactions on Visualization and Computer Graphics
Juliana Paes, Leticia de Oliveira, Mirtes Garcia Pereira, Isabel David, Gabriela Guerra Leal Souza, Ana Paula Sobral, Walter Machado-Pinheiro, Izabela Mocaiber
It is well established that emotions are organized around two motivational systems: the defensive and the appetitive. Individual differences are relevant factors in emotional reactions, making them more flexible and less stereotyped. There is evidence that health professionals have lower emotional reactivity when viewing scenes of situations involving pain. The objective of this study was to investigate whether the rating of pictures of surgical procedure depends on their personal/occupational relevance. Fifty-two female Nursing (health discipline) and forty-eight Social Work (social science discipline) students participated in the experiment, which consisted of the presentation of 105 images of different categories (e...
2016: PloS One
Matthew Dunlop-Gray, Phillip K Poon, Dathon Golish, Esteban Vera, Michael E Gehm
Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework...
August 8, 2016: Optics Express
Steven Lawrence Fernandes, G Josemin Bala
Biomechanics based human identification is a major area of research. Biomechanics based approaches depend on accurately recognizing humans using body movements, the accuracy of these approaches is enhanced by incorporating the knee-hip angle to angle relationships. Current biomechanics based models are developed by considering the biomechanics of human walking and running. In biomechanics the joint angle characteristics, also known as gait features play a vital role in identification of humans. In general, identification of humans can be broadly classified into two approaches: biomechanics based approach, also known as Gait Recognition and biometric based Composite Sketch Matching...
September 1, 2016: Computers in Biology and Medicine
Marius Cordts, Timo Rehfeld, Markus Enzweiler, Uwe Franke, Stefan Roth
We propose a novel approach to semantic scene labeling in urban scenarios, which aims to combine excellent recognition performance with highest levels of computational efficiency. To that end, we exploit efficient tree-structured models on two levels: pixels and superpixels. At the pixel level, we propose to unify pixel labeling and the extraction of semantic texton features within a single architecture, so-called encode-and-classify trees. At the superpixel level, we put forward a multi-cue segmentation tree that groups superpixels at multiple granularities...
July 19, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Stefan Taubenböck, Wolfgang Lederer, Marc Kaufmann, Gunnar Kroesen
OBJECTIVE: The objective of this study was to describe the pediatric emergencies encountered by the Christophorus-1 helicopter emergency medical service (HEMS) during a period of 2 years. METHODS: Emergency treatment of pediatric casualties by HEMS was evaluated at a helicopter base. Children up to 14 years who were treated by HEMS emergency physicians from Christophorus-1 during primary missions in the alpine region were retrospectively enrolled. RESULTS: Of the 1314 HEMS operations conducted during a 2-year investigation period, pediatric emergencies accounted for 114 (8...
September 2016: Wilderness & Environmental Medicine
Anja C Groth, James H Barnes, Cris Lewis, Cynthia K Murray, Fakhrildeen Albahadily, Thomas H Jourdan
The information inherent in cigarette ash in the form of trace-metal concentrations may be of use in a forensic context as it can indicate the brand from which the ash originated. This knowledge might help place suspects at crime scenes or determine how many people may have been present. To develop and test statistical models capable of classifying ash samples according to brand, commercial cigarettes procured in the U.S. and overseas were "smoked" using a peristaltic pump, mimicking the range of human smoking habits...
July 2016: Journal of Forensic Sciences
Alex E Hadjinicolaou, Shaun L Cloherty, Yu-Shan Hung, Tatiana Kameneva, Michael R Ibbotson
There are 15-20 different types of retinal ganglion cells (RGC) in the mammalian retina, each encoding different aspects of the visual scene. The mechanism by which post-synaptic signals from the retinal network generate spikes is determined by each cell's intrinsic electrical properties. Here we investigate the frequency responses of morphologically identified rat RGCs using intracellular injection of sinusoidal current waveforms, to assess their intrinsic capabilities with minimal contributions from the retinal network...
2016: PloS One
Ewelina Mistek, Lenka Halámková, Kyle C Doty, Claire K Muro, Igor K Lednev
Bearing in mind forensic purposes, a nondestructive and rapid method was developed for race differentiation of peripheral blood donors. Blood is an extremely valuable form of evidence in forensic investigations so proper analysis is critical. Because potentially miniscule amounts of blood traces can be found at a crime scene, having a method that is nondestructive, and provides a substantial amount of information about the sample, is ideal. In this study Raman spectroscopy was applied with advanced statistical analysis to discriminate between Caucasian (CA) and African American (AA) donors based on dried peripheral blood traces...
August 2, 2016: Analytical Chemistry
Qingfeng Liu, Chengjun Liu
A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition...
June 8, 2016: IEEE Transactions on Neural Networks and Learning Systems
Omid Kardan, John M Henderson, Grigori Yourganov, Marc G Berman
Previous research has shown that eye-movements change depending on both the visual features of our environment, and the viewer's top-down knowledge. One important question that is unclear is the degree to which the visual goals of the viewer modulate how visual features of scenes guide eye-movements. Here, we propose a systematic framework to investigate this question. In our study, participants performed 3 different visual tasks on 135 scenes: search, memorization, and aesthetic judgment, while their eye-movements were tracked...
September 2016: Journal of Experimental Psychology. Human Perception and Performance
Zhongbin Wang, Xihua Xu, Lei Si, Rui Ji, Xinhua Liu, Chao Tan
In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided...
2016: Computational Intelligence and Neuroscience
Ellen M Jesmok, James M Hopkins, David R Foran
Soil has the potential to be valuable forensic evidence linking a person or item to a crime scene; however, there is no established soil individualization technique. In this study, the utility of soil bacterial profiling via next-generation sequencing of the 16S rRNA gene was examined for associating soils with their place of origin. Soil samples were collected from ten diverse and nine similar habitats over time, and within three habitats at various horizontal and vertical distances. Bacterial profiles were analyzed using four methods: abundance charts and nonmetric multidimensional scaling provided simplification and visualization of the massive datasets, potentially aiding in expert testimony, while analysis of similarities and k-nearest neighbor offered objective statistical comparisons...
May 2016: Journal of Forensic Sciences
Tong He, Weilin Huang, Yu Qiao, Jian Yao
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components...
June 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Robert J Lee, Hannah E Smithson
We tested whether surface specularity alone supports operational color constancy-the ability to discriminate changes in illumination or reflectance. Observers viewed short animations of illuminant or reflectance changes in rendered scenes containing a single spherical surface and were asked to classify the change. Performance improved with increasing specularity, as predicted from regularities in chromatic statistics. Peak performance was impaired by spatial rearrangements of image pixels that disrupted the perception of illuminated surfaces but was maintained with increased surface complexity...
March 2016: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Bing Shuai, Zhen Zuo, Gang Wang, Bing Wang
We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity...
May 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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