Real time vision for robotics using a moving fovea approach with multi resolution

Gomes, Rafael Beserra; Gonalves, Luiz M. G.; Carvalho, Bruno M.

doi:10.1109/robot.2008.4543573

Cited by 11 publications

(16 citation statements)

References 16 publications

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“…As an interesting application of our implementation, an experiment was performed to test a moving fovea approach (Gomes et al, 2008). In this case, a hand holding a ball appears in front the camera mount and the system should track it without moving resources, in principle, by only changing the position of the fovea in the current viewing position by software.…”

Section: Resultsmentioning

confidence: 99%

Towards Real Time Data Reduction and Feature Abstraction for Robotics Vision

Gomes¹,

Gardiman²,

Leite³

et al. 2010

Robot Vision

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Section: Resultsmentioning

confidence: 99%

Towards Real Time Data Reduction and Feature Abstraction for Robotics Vision

Gomes¹,

Gardiman²,

Leite³

et al. 2010

Robot Vision

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“…Thus, using some technique for diminishing the amount of input data has proven to be a good idea [5,13]. Many types of methods optimize image processing tasks.…”

Section: Background Theorymentioning

confidence: 99%

“…Representation of the application of the foveation in multi-resolution: with centered fovea (MCF) [13] ( a ); and with moving fovea (MMF) [5] ( b ).…”

Section: Figurementioning

confidence: 99%

“…The advantages of point cloud over 2D images and the interest to extend the approach of multi-resolution with Moving Fovea (MMF) [5,6] has led Gomes et al to import the characteristics of MMF into 3D point cloud resulting in a model that they called Foveated Point Cloud (FPC) [7]. In fact, in the paper on Efficient 3D Object Recognition Using Foveated Point Clouds , Gomes et al [7] use a foveated structure (FS) to perform the detection of objects in a scene represented by a point cloud, to decrease the number of data during the processing.…”

Section: Introductionmentioning

confidence: 99%

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Efficient 3D Objects Recognition Using Multifoveated Point Clouds

Oliveira

Souza

Fernandes

et al. 2018

Sensors

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Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods.

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“…For digital foveas, a similar effect is achieved by effectively decreasing the resolution with increasing dis- tance from fixation. In practice this is achieved by the mechanism of a pyramid of IPs [7].…”

Section: The Multinomial I-pomdp Modelmentioning

confidence: 99%

Optimal scanning for faster object detection

Butko

Movellan²

2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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Recent years have seen the development of fast and accurate algorithms for detecting objects in images. However, as the size of the scene grows, so do the running-times of these algorithms. If a 128 × 102 pixel image requires 20ms to process, searching for objects in a 1280 × 1024 image will take 2s. This is unsuitable under real-time operating constraints: by the time a frame has been processed, the object may have moved. An analogous problem occurs when controlling robot camera that need to scan scenes in search of target objects. In this paper, we consider a method for improving the run-time of general-purpose object-detection algorithms. Our method is based on a model of visual search in humans, which schedules eye fixations to maximize the long-term information accrued about the location of the target of interest. The approach can be used to drive robot cameras that physically scan scenes or to improve the scanning speed for very large high resolution images. We consider the latter application in this work by simulating a "digital fovea" and sequentially placing it in various regions of an image in a way that maximizes the expected information gain. We evaluate the approach using the OpenCV version of the Viola-Jones face detector. After accounting for all computational overhead introduced by the fixation controller, the approach doubles the speed of the standard Viola-Jones detector at little cost in accuracy.

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Real time vision for robotics using a moving fovea approach with multi resolution

Cited by 11 publications

References 16 publications

Towards Real Time Data Reduction and Feature Abstraction for Robotics Vision

Towards Real Time Data Reduction and Feature Abstraction for Robotics Vision

Efficient 3D Objects Recognition Using Multifoveated Point Clouds

Optimal scanning for faster object detection

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