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https://www.readbyqxmd.com/read/29047710/intrinsic-decomposition-from-a-single-spectral-image
#1
Xi Chen, Weixin Zhu, Yang Zhao, Yao Yu, Yu Zhou, Tao Yue, Sidan Du, Xun Cao
In this paper, we present a spectral intrinsic image decomposition (SIID) model, which is dedicated to resolve a natural scene into its purely independent intrinsic components: illumination, shading, and reflectance. By introducing spectral information, our work can solve many challenging cases, such as scenes with metameric effects, which are hard to tackle for trichromatic intrinsic image decomposition (IID), and thus offers potential benefits to many higher-level vision tasks, e.g., materials classification and recognition, shape-from-shading, and spectral image relighting...
July 10, 2017: Applied Optics
https://www.readbyqxmd.com/read/29020848/concussion-alters-the-functional-brain-processes-of-visual-attention-and-working-memory
#2
Priyanka Shah-Basak, Charline Urbain, Simeon Wong, Leodante Da Costa, Elizabeth W Pang, Benjamin Dunkley, Margot Taylor
Millions of North Americans suffer a concussion or a mild traumatic brain injury annually and are at risk of cognitive, emotional and physical sequelae. While fMRI studies have provided an initial framework for examining functional deficits induced by concussion, particularly working memory and attention, the temporal dynamics underlying these deficits are not well understood. We used magnetoencephalography (MEG), a modality with millisecond temporal resolution, in conjunction with a 1-back visual working memory (VWM) paradigm using scenes from everyday life to characterize spatiotemporal functional differences at specific VWM stages, in adults who did or did not suffer a recent concussion...
October 12, 2017: Journal of Neurotrauma
https://www.readbyqxmd.com/read/28984525/brain-networks-related-to-beta-oscillatory-activity-during-episodic-memory-retrieval
#3
Erika Nyhus
Evidence from fMRI has consistently located a widespread network of frontal, parietal, and temporal lobe regions during episodic retrieval. However, the temporal limitations of the fMRI methodology have made it difficult to assess the transient network dynamics by which these distributed regions coordinate activity. Recent evidence suggests that beta oscillations (17-20 Hz) are important for top-down control for memory suppression. However, the spatial limitations of the EEG methodology make it difficult to assess the relationship between these oscillatory signals and the distributed networks identified with fMRI...
October 6, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/28961443/eye-tracking-to-evaluate-evidence-recognition-in-crime-scene-investigations
#4
Renuka Devi Watalingam, Nicole Richetelli, Jeff B Pelz, Jacqueline A Speir
Crime scene analysts are the core of criminal investigations; decisions made at the scene greatly affect the speed of analysis and the quality of conclusions, thereby directly impacting the successful resolution of a case. If an examiner fails to recognize the pertinence of an item on scene, the analyst's theory regarding the crime will be limited. Conversely, unselective evidence collection will most likely include irrelevant material, thus increasing a forensic laboratory's backlog and potentially sending the investigation into an unproductive and costly direction...
August 23, 2017: Forensic Science International
https://www.readbyqxmd.com/read/28961163/object-classification-in-semi-structured-enviroment-using-forward-looking-sonar
#5
Matheus Dos Santos, Pedro Otávio Ribeiro, Pedro Núñez, Paulo Drews-Jr, Silvia Botelho
The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot's environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques...
September 29, 2017: Sensors
https://www.readbyqxmd.com/read/28912699/effects-of-scene-properties-and-emotional-valence-on-brain-activations-a-fixation-related-fmri-study
#6
Michał Kuniecki, Kinga B Wołoszyn, Aleksandra Domagalik, Joanna Pilarczyk
Temporal and spatial characteristics of fixations are affected by image properties, including high-level scene characteristics, such as object-background composition, and low-level physical characteristics, such as image clarity. The influence of these factors is modulated by the emotional content of an image. Here, we aimed to establish whether brain correlates of fixations reflect these modulatory effects. To this end, we simultaneously scanned participants and measured their eye movements, while presenting negative and neutral images in various image clarity conditions, with controlled object-background composition...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28878956/object-recognition-and-localization-from-3d-point-clouds-by-maximum-likelihood-estimation
#7
Harshana G Dantanarayana, Jonathan M Huntley
We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike 'interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions...
August 2017: Royal Society Open Science
https://www.readbyqxmd.com/read/28866483/a-multi-modal-discriminative-and-spatially-invariant-cnn-for-rgb-d-object-labeling
#8
Umar Asif, Mohammed Bennamoun, Ferdous Sohel
While deep convolutional neural networks have shown a remarkable success in image classification, the problems of inter-class similarities, intra-class variances, the effective combination of multimodal data, and the spatial variability in images of objects remain to be major challenges. To address these problems, this paper proposes a novel framework to learn a discriminative and spatially invariant classification model for object and indoor scene recognition using multimodal RGB-D imagery. This is achieved through three postulates: 1) spatial invariance - this is achieved by combining a spatial transformer network with a deep convolutional neural network to learn features which are invariant to spatial translations, rotations, and scale changes, 2) high discriminative capability - this is achieved by introducing Fisher encoding within the CNN architecture to learn features which have small inter-class similarities and large intra-class compactness, and 3) multimodal hierarchical fusion - this is achieved through the regularization of semantic segmentation to a multi-modal CNN architecture, where class probabilities are estimated at different hierarchical levels (i...
August 30, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28842854/pisces-pictures-with-social-context-and-emotional-scenes-with-norms-for-emotional-valence-intensity-and-social-engagement
#9
Elizabeth J Teh, Melvin J Yap, Susan J Rickard Liow
Picture databases are commonly used in experimental work on various aspects of emotion processing. However, existing standardized facial databases, typically used to explore emotion recognition, can be augmented with more contextual information for studying emotion and social perception. Moreover, the perception of social engagement, i.e., the degree of interaction or engagement inferred between the people in target pictures, has not been measured. In this paper, we describe the development of a database comprising 203 black-and-white line drawings depicting people within various situational contexts, and normed on perceived emotional valence, intensity, and social engagement, a new construct...
August 25, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/28813564/multi-inducer-grouping-for-curve-completion-perceptual-and-computational-exploration
#10
Nir-Cohen Gal, Arad Boaz, Ben-Shahar Ohad
The human visual system excels in object recognition and scene interpretation even in scenes in which some (or even all) observed objects are partially occluded or fragmented. This highly efficient capacity is facilitated by constructive processes of contour completion between inducers to yield the perception of whole objects across gaps. A fundamental problem of the process is when and how the visual system groups different inducers in the visual scene between which completion occurs. Previous studies on this grouping problem, inspired mostly by relatability theory (Kellman & Shipley, 1991), focused on one good continuation condition that dictates whether a given pair of inducers would group together or not...
August 1, 2017: Journal of Vision
https://www.readbyqxmd.com/read/28808817/electrophysiological-modulation-in-an-effort-to-complete-illusory-figures-configuration-illusory-contour-and-closure-effects
#11
Tommaso Poscoliero, Massimo Girelli
Figure recognition process: From the coarse configuration standing from the background to the closure of a meaningful shape, was investigated by ERP technique. ERP components at different latencies from stimulus onset allowed to tap into the figure recognition process at discrete time-points when different cognitive operations take place. In this study, we present two experiments where the support-ratio (SR) of illusory figures was manipulated to vary continuously the recognition of geometrical figures. In the first experiment three shapes were used to vary the SR and the P1 component (80-130 ms) was modulated by the configuration-effect explained, in part for the first time, with the unbalanced physical stimulation between upper and lower visual field...
August 14, 2017: Brain Topography
https://www.readbyqxmd.com/read/28796618/learning-based-shadow-recognition-and-removal-from-monochromatic-natural-images
#12
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/28795834/contrasting-gist-based-and-template-based-guidance-during-real-world-visual-search
#13
Brett Bahle, Michi Matsukura, Andrew Hollingworth
Visual search through real-world scenes is guided both by a representation of target features and by knowledge of the sematic properties of the scene (derived from scene gist recognition). In 3 experiments, we compared the relative roles of these 2 sources of guidance. Participants searched for a target object in the presence of a critical distractor object. The color of the critical distractor either matched or mismatched (a) the color of an item maintained in visual working memory for a secondary task (Experiment 1), or (b) the color of the target, cued by a picture before search commenced (Experiments 2 and 3)...
August 10, 2017: Journal of Experimental Psychology. Human Perception and Performance
https://www.readbyqxmd.com/read/28780400/visual-cortical-networks-align-with-behavioral-measures-of-context-sensitivity-in-early-childhood
#14
Moritz Köster, Johanna Castel, Thomas Gruber, Joscha Kärtner
This study investigates how visual cortical networks align with context-sensitivity, namely the relative focus on the object versus the background of a visual scene, in early childhood. Context-sensitivity was assessed by a picture description and a recognition memory task. To segregate object and background processing in the visual cortex in 5- and 7-year-old children, object and background were presented at different frequencies (12 Hz or 15 Hz), evoking disparate neuronal responses (steady state visually evoked potentials, SSVEPs) in the electroencephalogram...
August 2, 2017: NeuroImage
https://www.readbyqxmd.com/read/28764452/modeling-speech-localization-talker-identification-and-word-recognition-in-a-multi-talker-setting
#15
Angela Josupeit, Volker Hohmann
This study introduces a model for solving three different auditory tasks in a multi-talker setting: target localization, target identification, and word recognition. The model was used to simulate psychoacoustic data from a call-sign-based listening test involving multiple spatially separated talkers [Brungart and Simpson (2007). Percept. Psychophys. 69(1), 79-91]. The main characteristics of the model are (i) the extraction of salient auditory features ("glimpses") from the multi-talker signal and (ii) the use of a classification method that finds the best target hypothesis by comparing feature templates from clean target signals to the glimpses derived from the multi-talker mixture...
July 2017: Journal of the Acoustical Society of America
https://www.readbyqxmd.com/read/28760565/familiarity-and-recollection-vs-representational-models-of-medial-temporal-lobe-structures-a-single-case-study
#16
Emilie Lacot, Stéphane Vautier, Stefan Kőhler, Jérémie Pariente, Chris B Martin, Michèle Puel, Jean-Albert Lotterie, Emmanuel J Barbeau
Although it is known that medial temporal lobe (MTL) structures support declarative memory, the fact these structures have different architectonics and circuitry suggests they may also play different functional roles. Selective lesions of MTL structures offer an opportunity to understand these roles. We report, in this study, on JMG, a patient who presents highly unusual lesions that completely affected all MTL structures except for the right hippocampus and parts of neighbouring medial parahippocampal cortex...
September 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28738074/adversity-emotion-recognition-and-empathic-concern-in-high-risk-youth
#17
Jodi A Quas, Kelli L Dickerson, Richard Matthew, Connor Harron, Catherine M Quas
Little is known about how emotion recognition and empathy jointly operate in youth growing up in contexts defined by persistent adversity. We investigated whether adversity exposure in two groups of youth was associated with reduced empathy and whether deficits in emotion recognition mediated this association. Foster, rural poor, and comparison youth from Swaziland, Africa identified emotional expressions and rated their empathic concern for characters depicted in images showing positive, ambiguous, and negative scenes...
2017: PloS One
https://www.readbyqxmd.com/read/28708559/texture-characterization-using-shape-co-occurrence-patterns
#18
Gui-Song Xia, Gang Liu, Xiang Bai, Liangpei Zhang
Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector...
July 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28707123/visual-properties-and-memorising-scenes-effects-of-image-space-sparseness-and-uniformity
#19
Jiří Lukavský, Filip Děchtěrenko
Previous studies have demonstrated that humans have a remarkable capacity to memorise a large number of scenes. The research on memorability has shown that memory performance can be predicted by the content of an image. We explored how remembering an image is affected by the image properties within the context of the reference set, including the extent to which it is different from its neighbours (image-space sparseness) and if it belongs to the same category as its neighbours (uniformity). We used a reference set of 2,048 scenes (64 categories), evaluated pairwise scene similarity using deep features from a pretrained convolutional neural network (CNN), and calculated the image-space sparseness and uniformity for each image...
July 13, 2017: Attention, Perception & Psychophysics
https://www.readbyqxmd.com/read/28692990/discriminative-block-diagonal-representation-learning-for-image-recognition
#20
Zheng Zhang, Yong Xu, Ling Shao, Jian Yang
Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR...
July 4, 2017: IEEE Transactions on Neural Networks and Learning Systems
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