scholarly journals Infant visual attention and object recognition

2015 ◽  
Vol 285 ◽  
pp. 34-43 ◽  
Author(s):  
Greg D. Reynolds
Keyword(s):  
Object Recognition ◽  
Visual Attention

Visual Attention Guided Object Detection and Tracking

2016 ◽  
pp. 99-114
Author(s):  
Debi Prosad Dogra
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Attention ◽  
Visual Saliency ◽  
Video Object ◽  
Salient Region Detection ◽  
Region Detection ◽  
Video Object Tracking ◽  
Detection And Tracking ◽  
Pros And Cons

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

scholarly journals An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1534 ◽  
Author(s):  
Ghazal Rouhafzay ◽  
Ana-Maria Cretu
Keyword(s):  
Object Recognition ◽  
Visual Attention ◽  
Visual Information ◽  
Support Vector ◽  
Sequential Data ◽  
K Nearest Neighbors ◽  
Object A ◽  
Haptic Exploration ◽  
Virtual Force ◽  
Neuroscience Research

Drawing inspiration from haptic exploration of objects by humans, the current work proposes a novel framework for robotic tactile object recognition, where visual information in the form of a set of visually interesting points is employed to guide the process of tactile data acquisition. Neuroscience research confirms the integration of cutaneous data as a response to surface changes sensed by humans with data from joints, muscles, and bones (kinesthetic cues) for object recognition. On the other hand, psychological studies demonstrate that humans tend to follow object contours to perceive their global shape, which leads to object recognition. In compliance with these findings, a series of contours are determined around a set of 24 virtual objects from which bimodal tactile data (kinesthetic and cutaneous) are obtained sequentially and by adaptively changing the size of the sensor surface according to the object geometry for each object. A virtual Force Sensing Resistor array (FSR) is employed to capture cutaneous cues. Two different methods for sequential data classification are then implemented using Convolutional Neural Networks (CNN) and conventional classifiers, including support vector machines and k-nearest neighbors. In the case of conventional classifiers, we exploit contourlet transformation to extract features from tactile images. In the case of CNN, two networks are trained for cutaneous and kinesthetic data and a novel hybrid decision-making strategy is proposed for object recognition. The proposed framework is tested both for contours determined blindly (randomly determined contours of objects) and contours determined using a model of visual attention. Trained classifiers are tested on 4560 new sequential tactile data and the CNN trained over tactile data from object contours selected by the model of visual attention yields an accuracy of 98.97% which is the highest accuracy among other implemented approaches.

scholarly journals Infant Visual Attention and Stimulus Repetition Effects on Object Recognition

2017 ◽  
Vol 90 (4) ◽  
pp. 1027-1042 ◽  
Author(s):  
Greg D. Reynolds ◽  
John E. Richards
Keyword(s):  
Object Recognition ◽  
Visual Attention ◽  
Stimulus Repetition ◽  
Repetition Effects

Coordination of what and where in Visual Attention

Perception ◽  
1993 ◽  
Vol 22 (11) ◽  
pp. 1261-1270 ◽  
Author(s):  
John Duncan
Keyword(s):  
Object Recognition ◽  
Visual Attention ◽  
Divided Attention ◽  
Visual Information ◽  
Functional Relationship ◽  
Human Subjects ◽  
Visual Display ◽  
Spatial Relations ◽  
Cortical Areas ◽  
Cortical System

Performance often suffers when two visual discriminations must be made concurrently (‘divided attention’). In the modular primate visual system, different cortical areas analyse different kinds of visual information. Especially important is a distinction between an occipitoparietal ‘where?’ system, analysing spatial relations, and an occipitotemporal ‘what?’ system responsible for object recognition. Though such visual subsystems are anatomically parallel, their functional relationship when ‘what?’ and ‘where?’ discriminations are made concurrently is unknown. In the present experiments, human subjects made concurrent discriminations concerning a brief visual display. Discriminations were either similar (two ‘what?’ or two ‘where?’ discriminations) or dissimilar (one of each), and concerned the same or different objects. When discriminations concerned different objects, there was strong interference between them. This was equally severe whether discriminations were similar—and therefore dependent on the same cortical system—or dissimilar. When concurrent ‘what?’ and ‘where?’ discriminations concerned the same object, however, all interference disappeared. Such results suggest that ‘what?’ and ‘where?’ systems are coordinated in visual attention: their separate outputs can be used simultaneously without cost, but only when they concern one object.

Familiarity based unified visual attention model for fast and robust object recognition

2010 ◽  
Vol 43 (3) ◽  
pp. 1116-1128 ◽  
Author(s):  
Seungjin Lee ◽  
Kwanho Kim ◽  
Joo-Young Kim ◽  
Minsu Kim ◽  
Hoi-Jun Yoo
Keyword(s):  
Object Recognition ◽  
Visual Attention ◽  
Visual Attention Model ◽  
Attention Model
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