Swarm contours: A fast self-organization approach for snake initialization

Mobahi, Hossein; Ahmadabadi, Majid Nili; Araabi, Babak Nadjar

doi:10.1002/cplx.20147

Cited by 8 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain computational modelling with multiple agents is not a new idea (Minsky 1988;Chialvo and Millonas 1995). Although the first realisations for computer vision related problems are also not new (Poli and Valli 1993;Liu et al 1997), only more recently it has received considerable attention (Ramos and Almeida 2000;Owechko and Medasani 2005;Antón-Canalís et al 2006;Mobahi et al 2006;Broggi and Cattani 2006;Mazouzi et al 2007;Zhang et al 2008). In general, these models exploit the metaphor of swarming behaviour on social insects.…”

Section: Agent Abstraction Of Covert Attentionmentioning

confidence: 97%

Swarm cognition on off-road autonomous robots

Santana

Correia

2011

Swarm Intell

View full text Add to dashboard Cite

This paper contributes with the first validation of swarm cognition as a useful framework for the design of autonomous robots controllers. The proposed model is built upon the authors' previous work validated on a simulated robot performing local navigation on a 2-D deterministic world. Based on the ant foraging metaphor and motivated by the multiple covert attention hypothesis, the model consists of a set of simple virtual agents inhabiting the robot's visual input, searching in a collectively coordinated way for obstacles. Parsimonious and accurate visual attention, operating on a by-need basis, is attained by making the activity of these agents modulated by the robot's action selection process. A by-product of the system is the maintenance of active, parallel and sparse spatial working memories. In short, the model exhibits the self-organisation of a relevant set of features composing a cognitive system. To show its robustness, the model is extended in this paper to handle the challenges of physical off-road robots equipped with noisy stereoscopic vision sensors. Furthermore, an extensive aggregate of biological arguments sustaining the model is provided. Experimental results show the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot's visual input being analysed. Hence, with this system the computational cost of perception is considerably reduced, thus fostering robot miniaturisation and energetic efficiency. This confirms the advantages of using a swarm-based system, operating in an intricate way with action selection, to judiciously Swarm Intell (2011) 5: 45-72 control visual attention and maintain sparse spatial memories, constituting a basic form of swarm cognition.

show abstract

Section: Agent Abstraction Of Covert Attentionmentioning

confidence: 97%

Swarm cognition on off-road autonomous robots

Santana

Correia

2011

Swarm Intell

View full text Add to dashboard Cite

show abstract

“…Since those initial pioneering results, genetic algorithms have been the type of metaheuristic which has been used most frequently. In recent years, swarm intelligence has also attracted the attention of researchers in classic contour models which resulted in several publications [78][79][80][81][82][83][84][85][86]. Finally, a few proposals have been based on simulated annealing [76,[87][88][89][90] and basic memetic approaches [90][91][92].…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…Finally, there are few approaches where the metaheuristic plays an alternative role. In [81,99,103] metaheuristics are used to set the initial location of the snake, the number of required snakes, and the appropriate number of control points. Far from other approaches, but still using a metaheuristic inside the deformable model-based image segmentation process, the authors in [89] use simulated annealing to set the state of each neuron of a Hopfield neural network which optimizes the energy function.…”

Section: Active Contour Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Mesejo

Ibáñez

Cordón

et al. 2016

Applied Soft Computing

View full text Add to dashboard Cite

Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires that an optimization framework be defined whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms' parameters; choosing the representation for encoding prior knowledge on the image domain of interest; initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different metaheuristic-based approaches to image segmentation based on deformable models, and conclude with a general discussion about methodological and design issues as well as future research and application trends.

show abstract

“…Seeking robustness and parsimony, we follow the long line of research on the use of the social insects metaphor for the design of computer vision systems (Antón‐Canalís et al, ; Broggi & Cattani, ; Liu et al, ; Mazouzi et al 2007; Mobahi et al, ; Owechko & Medasani, ; Poli and Valli, ; Ramos and Almeida, ; Zhang et al 2008). The work that is most related to ours is that of Broggi and Cattani (), which detects the edges of poorly structured desert roads using a swarm‐based system.…”

Section: Related Workmentioning

confidence: 99%

Tracking natural trails with swarm‐based visual saliency

Santana

Correia

Mendonca

et al. 2012

Journal of Field Robotics

View full text Add to dashboard Cite

This paper proposes a model for trail detection and tracking that builds upon the observation that trails are salient structures in the robot's visual field. Due to the complexity of natural environments, the straightforward application of bottom‐up visual saliency models is not sufficiently robust to predict the location of trails. As for other detection tasks, robustness can be increased by modulating the saliency computation based on a priori knowledge about which pixel‐wise visual features are most representative of the object being sought. This paper proposes the use of the object's overall layout as the primary cue instead, as it is more stable and predictable in natural trails. Bearing in mind computational parsimony and detection robustness, this knowledge is specified in terms of perception‐action rules, which control the behavior of simple agents performing as a swarm to compute the saliency map of the input image. For the purpose of tracking, multiframe evidence about the trail location is obtained with a motion‐compensated dynamic neural field. In addition, to reduce ambiguity between the trail and trail‐like distractors, a simple appearance model is learned online and used to influence the agents' activity. Experimental results on a large data set reveal the ability of the model to produce a success rate on the order of 97% at 20 Hz. The model is shown to be robust in situations where previous models would fail, such as when the trail does not emerge from the lower part of the image or when it is considerably interrupted. © 2012 Wiley Periodicals, Inc.

show abstract

Swarm contours: A fast self-organization approach for snake initialization

Abstract: A major obstacle in real-time performance of a visual tracking system is its initialization

Cited by 8 publications

References 23 publications

Swarm cognition on off-road autonomous robots

Swarm cognition on off-road autonomous robots

A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Tracking natural trails with swarm‐based visual saliency

Contact Info

Product

Resources

About