Biologically-inspired robotics vision monte-carlo localization in the outdoor environment

Siagian, Christian; Itti, Laurent

doi:10.1109/iros.2007.4399349

Cited by 45 publications

(42 citation statements)

References 46 publications

(61 reference statements)

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“…Two broad methods can be discerned -those that model local features [2] and distinctive parts of images [26], and those that extract global representations of images and learn from them [22][10] [3]. The latter is closer to this work and includes the CENTRISTbased VPC system [33], and Spatial Pyramid Matching [15].…”

Section: Related Workmentioning

confidence: 99%

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

Ranganathan¹

2010

Robotics: Science and Systems VI

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Abstract-We present PLISS (Place Labeling through Image Sequence Segmentation), a novel technique for place recognition and categorization from visual cues. PLISS operates on video or image streams and works by segmenting it into pieces corresponding to distinct places in the environment. An online Bayesian change-point detection framework that detects changes to model parameters is used to segment the image stream. Unlike current place recognition methods, in addition to using previously learned place models for labeling, PLISS can also detect and learn a previously unknown place or place category in an online manner. Moreover, since both the inferred boundaries of places (change-points) and the place labels are fully probabilistic, they can indicate when the inference is uncertain. New places and categories are detected using a systematic statistical hypothesis testing framework. We present extensive experiments on a large and difficult image dataset. We validate our claims by comparing results obtained using different types of features and by comparing results from PLISS against the state of the art.

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Section: Related Workmentioning

confidence: 99%

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

Ranganathan¹

2010

Robotics: Science and Systems VI

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“…Visual attention is used to limit the amount of visual input and focus on the most informative parts of the scene. VOCUS (Visual Object detection with a Computational attention System) Siagian and Itti 2007) is a successful example that includes a bottom-up and top-down process, while incorporating further sensory input (laser, etc.) .…”

Section: Robotic Visionmentioning

confidence: 99%

Vision, Attention Control, and Goals Creation System

Rapantzikos

Avrithis²,

Kolias³

2010

Perception-Action Cycle

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Biological visual attention has been long studied by experts in the field of cognitive psychology. The Holy Grail of this study is the exact modeling of the interaction between the visual sensory and the process of perception. It seems that there is an informal agreement on the four important functions of the attention process: (a) the bottom-up process, which is responsible for the saliency of the input stimuli; (b) the top-down process that bias attention toward known areas or regions of predefined characteristics; (c) the attentional selection that fuses information derived from the two previous processes and enables focus; and (d) the dynamic evolution of the attentional selection process. In the following, we will outline established computational solutions for each of the four functions. OverviewMost of our impressions and memories are based on vision. Nevertheless vision mechanisms and functionalities are still not apparent. How do we perceive shape, color, or motion and how do we automatically focus on the most informative parts of the visual input? It has been long established that primates, including human, use focused attention and fast saccades to analyze visual stimuli based on the current situation or the desired goal. Neuroscientists have proven that neural information related to shape, motion, and color is transmitted through, at least, three parallel and interconnected channels to the brain rather than a single one. Hence a second question arises related to how these channels are "linked" in order to provide useful information to the brain.The Human Visual System (HVS) creates a perceptual representation of the world that is quite different than the two dimensional depiction of the retina.K. Rapantzikos ( ) Image, Video and

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“…Looking further, other original recent works could be cited addressing applications related to super resolution [25], user interface design [26], augmented reality [27], automated guidance of robots [28]. All this activity is witnessing the current enthusiasm of the community.…”

Section: Applicationsmentioning

confidence: 99%

What we see is most likely to be what matters: Visual attention and applications

Meur

Callet

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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The computational modeling of the visual attention is receiving increasing attention from the computer vision community. Several bottom-up models have been proposed. In spite of their complexities, these models are still a basic description of our visual system. Review of resulting approaches of these efforts are presented in the first part of this paper. Limitations of these approaches are introduced and several research trends are given. Among them, the most important one might be the use of prior knowledge, conjointly with the low-level visual features. Concomitantly with visual attention (VA) modeling progress, the image and video processing community is increasingly considering VA models in different fields or services. Current and future applications of VA models are discussed in the second part.

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Biologically-inspired robotics vision monte-carlo localization in the outdoor environment

Cited by 45 publications

References 46 publications

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

PLISS: Detecting and Labeling Places Using Online Change-Point Detection

Vision, Attention Control, and Goals Creation System

What we see is most likely to be what matters: Visual attention and applications

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