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https://www.readbyqxmd.com/read/28437797/central-and-peripheral-vision-for-scene-recognition-a-neurocomputational-modeling-exploration
#1
Panqu Wang, Garrison W Cottrell
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models...
April 1, 2017: Journal of Vision
https://www.readbyqxmd.com/read/28437623/selective-scene-perception-deficits-in-a-case-of-topographical-disorientation
#2
Jessica Robin, Matthew X Lowe, Sara Pishdadian, Josée Rivest, Jonathan S Cant, Morris Moscovitch
Topographical disorientation (TD) is a neuropsychological condition characterized by an inability to find one's way, even in familiar environments. One common contributing cause of TD is landmark agnosia, a visual recognition impairment specific to scenes and landmarks. Although many cases of TD with landmark agnosia have been documented, little is known about the perceptual mechanisms which lead to selective deficits in recognizing scenes. In the present study, we test LH, a man who exhibits TD and landmark agnosia, on measures of scene perception that require selectively attending to either the configural or surface properties of a scene...
April 2, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28436874/track-everything-limiting-prior-knowledge-in-online-multi-object-recognition
#3
Sebastien C Wong, Victor Stamatescu, Adam Gatt, David Kearney, Ivan Lee, Mark D McDonnell
This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fastlearning image classifier that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm...
April 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28436864/tracking-based-multi-orientation-scene-text-detection-a-unified-framework-with-dynamic-programming
#4
Chun Yang, Xu-Cheng Yin, Wei-Yi Pei, Shu Tian, Ze-Yu Zuo, Chao Zhu, Junchi Yan
There are a variety of grand challenges for multiorientation text detection in scene videos, where the typical issues include skew distortion, low contrast and arbitrary motion. Most conventional video text detection methods using individual frames have limited performance. In this paper, we propose a novel tracking based multi-orientation scene text detection method using multiple frames within a unified framework via dynamic programming. First, a multi-information fusion based multiorientation text detection method in each frame is proposed to extensively locate possible character candidates and extract text regions with multiple channels and scales...
April 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422661/correlated-topic-vector-for-scene-classification
#5
Pengxu Wei, Fei Qin, Fang Wan, Yi Zhu, Jianbin Jiao, Qixiang Ye
Scene images usually involve semantic correlations, particularly when considering large-scale image datasets. This paper proposes a novel generative image representation, Correlated Topic Vector, to model such semantic correlations. Oriented from the correlated topic model, Correlated Topic Vector intends to naturally utilize the correlations among topics which are seldom considered in the conventional feature encoding, e.g., Fisher Vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422537/morbidity-and-mortality-associated-with-prehospital-lift-assist-calls
#6
Lauren Leggatt, Kristine Van Aarsen, Melanie Columbus, Adam Dukelow, Michael Lewell, Matthew Davis, Shelley McLeod
INTRODUCTION: When an individual requires assistance with mobilization, emergency medical services (EMS) may be called. If a patient does not receive treatment on scene and is not transported to hospital, these are referred to as "Lift Assist" (LA) calls. It is possible this need for assistance represents a subtle onset of a disease process or decline in function. Without recognition or treatment, the patient may be at risk for recurrent falls, repeat EMS visits or worsening illness. OBJECTIVE: To examine the 14-day morbidity and mortality associated with LA calls and determine factors that are associated with increased risk of these outcomes...
April 19, 2017: Prehospital Emergency Care
https://www.readbyqxmd.com/read/28420155/new-compact-3-dimensional-shape-descriptor-for-a-depth-camera-in-indoor-environments
#7
Hyukdoo Choi, Euntai Kim
This study questions why existing local shape descriptors have high dimensionalities (up to hundreds) despite simplicity of local shapes. We derived an answer from a historical context and provided an alternative solution by proposing a new compact descriptor. Although existing descriptors can express complicated shapes and depth sensors have been improved, complex shapes are rarely observed in an ordinary environment and a depth sensor only captures a single side of a surface with noise. Therefore, we designed a new descriptor based on principal curvatures, which is compact but practically useful...
April 16, 2017: Sensors
https://www.readbyqxmd.com/read/28413566/a-hierarchical-predictive-coding-model-of-object-recognition-in-natural-images
#8
M W Spratling
Predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article proposes a hierarchical neural network based on predictive coding for performing visual object recognition. This network is applied to the tasks of categorising hand-written digits, identifying faces, and locating cars in images of street scenes...
2017: Cognitive Computation
https://www.readbyqxmd.com/read/28410105/dynamic-textures-modeling-via-joint-video-dictionary-learning
#9
Xian Wei, Yuanxiang Li, Hao Shen, Fang Chen, Martin Kleinsteuber, Zhongfeng Wang
Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28382195/superordinate-level-processing-has-priority-over-basic-level-processing-in-scene-gist-recognition
#10
Qi Sun, Yanju Ren, Yang Zheng, Mingxia Sun, Yuanjie Zheng
By combining a perceptual discrimination task and a visuospatial working memory task, the present study examined the effects of visuospatial working memory load on the hierarchical processing of scene gist. In the perceptual discrimination task, two scene images from the same (manmade-manmade pairing or natural-natural pairing) or different superordinate level categories (manmade-natural pairing) were presented simultaneously, and participants were asked to judge whether these two images belonged to the same basic-level category (e...
November 2016: I-Perception
https://www.readbyqxmd.com/read/28377161/category-selectivity-in-human-visual-cortex-beyond-visual-object-recognition
#11
Marius V Peelen, Paul E Downing
Human ventral temporal cortex shows a categorical organization, with regions responding selectively to faces, bodies, tools, scenes, words, and other categories. Why is this? Traditional accounts explain category selectivity as arising within a hierarchical system dedicated to visual object recognition. For example, it has been proposed that category selectivity reflects the clustering of category-associated visual feature representations, or that it reflects category-specific computational algorithms needed to achieve view invariance...
April 1, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28375371/three-dimensional-profilometric-reconstruction-using-flexible-sensing-integral-imaging-and-occlusion-removal
#12
Xin Shen, Adam Markman, Bahram Javidi
We present a method for three-dimensional (3D) profilometric reconstruction using flexible sensing integral imaging with object recognition and automatic occlusion removal. Two-dimensional images, known as elemental images (EIs), of a scene containing an object behind occlusion are captured by flexible sensing integral imaging using a moving camera randomly placed on a non-planar surface with unknown camera position and orientation. After 3D image acquisition, the unknown camera poses are estimated using the EIs and 3D reconstruction is performed based on flexible sensing integral imaging...
March 20, 2017: Applied Optics
https://www.readbyqxmd.com/read/28368817/single-view-3d-scene-reconstruction-and-parsing-by-attribute-grammar
#13
Xiaobai Liu, Yibiao Zhao, Song-Chun Zhu
In this paper, we present an attribute grammar for solving two coupled tasks: i) parsing an 2D image into semantic regions; and ii) recovering the 3D scene structures of all regions. The proposed grammar consists of a set of production rules, each describing a kind of spatial relation between planar surfaces in 3D scenes. These production rules are used to decompose an input image into a hierarchical parse graph representation where each graph node indicates a planar surface or a composite surface. Different from other stochastic image grammars, the proposed grammar augments each graph node with a set of attribute variables to depict scene-level global geometry, e...
March 29, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28345505/international-recognition-of-the-chronic-otitis-media-questionnaire-12
#14
S I Kosyakov, J V Minavnina, J S Phillips, M W Yung
OBJECTIVE: The Chronic Otitis Media Questionnaire 12 was developed initially in the UK to assess patient-reported health-related quality of life associated with chronic otitis media. This study aimed to determine whether this tool is applicable to the Russian population, which has a materially different healthcare system. METHOD: A total of 108 patients with different forms of chronic otitis media completed the Russian Chronic Otitis Media Questionnaire 12. RESULTS: The average Russian Chronic Otitis Media Questionnaire 12 score was 19...
March 27, 2017: Journal of Laryngology and Otology
https://www.readbyqxmd.com/read/28344913/indoor-localisation-through-object-detection-within-multiple-environments-utilising-a-single-wearable-camera
#15
Colin Shewell, Chris Nugent, Mark Donnelly, Haiying Wang, Macarena Espinilla
The recent growth in the wearable sensor market has stimulated new opportunities within the domain of Ambient Assisted Living, providing unique methods of collecting occupant information. This approach leverages contemporary wearable technology, Google Glass, to facilitate a unique first-person view of the occupants immediate environment. Machine vision techniques are employed to determine an occupant's location via environmental object detection. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location...
2017: Health and Technology
https://www.readbyqxmd.com/read/28343958/simultanagnosia-does-not-affect-processes-of-auditory-gestalt-perception
#16
Johannes Rennig, Anna Lena Bleyer, Hans-Otto Karnath
Simultanagnosia is a neuropsychological deficit of higher visual processes caused by temporo-parietal brain damage. It is characterized by a specific failure of recognition of a global visual Gestalt, like a visual scene or complex objects, consisting of local elements. In this study we investigated to what extend this deficit should be understood as a deficit related to specifically the visual domain or whether it should be seen as defective Gestalt processing per se. To examine if simultanagnosia occurs across sensory domains, we designed several auditory experiments sharing typical characteristics of visual tasks that are known to be particularly demanding for patients suffering from simultanagnosia...
March 23, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28343023/classification-of-footwear-outsole-patterns-using-fourier-transform-and-local-interest-points
#17
Nicole Richetelli, Mackenzie C Lee, Carleen A Lasky, Madison E Gump, Jacqueline A Speir
Successful classification of questioned footwear has tremendous evidentiary value; the result can minimize the potential suspect pool and link a suspect to a victim, a crime scene, or even multiple crime scenes to each other. With this in mind, several different automated and semi-automated classification models have been applied to the forensic footwear recognition problem, with superior performance commonly associated with two different approaches: correlation of image power (magnitude) or phase, and the use of local interest points transformed using the Scale Invariant Feature Transform (SIFT) and compared using Random Sample Consensus (RANSAC)...
March 4, 2017: Forensic Science International
https://www.readbyqxmd.com/read/28342164/attempting-to-increase-intake-from-the-input-attention-and-word-learning-in-children-with-autism
#18
Elena J Tenenbaum, Dima Amso, Giulia Righi, Stephen J Sheinkopf
Previous work has demonstrated that social attention is related to early language abilities. We explored whether we can facilitate word learning among children with autism by directing attention to areas of the scene that have been demonstrated as relevant for successful word learning. We tracked eye movements to faces and objects while children watched videos of a woman teaching them new words. Test trials measured participants' recognition of these novel word-object pairings. Results indicate that for children with autism and typically developing children, pointing to the speaker's mouth while labeling a novel object impaired performance, likely because it distracted participants from the target object...
March 24, 2017: Journal of Autism and Developmental Disorders
https://www.readbyqxmd.com/read/28335510/gender-recognition-from-human-body-images-using-visible-light-and-thermal-camera-videos-based-on-a-convolutional-neural-network-for-image-feature-extraction
#19
Dat Tien Nguyen, Ki Wan Kim, Hyung Gil Hong, Ja Hyung Koo, Min Cheol Kim, Kang Ryoung Park
Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications...
March 20, 2017: Sensors
https://www.readbyqxmd.com/read/28333644/random-forest-classifier-for-zero-shot-learning-based-on-relative-attribute
#20
Yuhu Cheng, Xue Qiao, Xuesong Wang, Qiang Yu
For the zero-shot image classification with relative attributes (RAs), the traditional method requires that not only all seen and unseen images obey Gaussian distribution, but also the classifications on testing samples are made by maximum likelihood estimation. We therefore propose a novel zero-shot image classifier called random forest based on relative attribute. First, based on the ordered and unordered pairs of images from the seen classes, the idea of ranking support vector machine is used to learn ranking functions for attributes...
March 21, 2017: IEEE Transactions on Neural Networks and Learning Systems
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