Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

Fernández-Caballero, Antonio; Fernández, Miguel Ángel; Mira, José; Delgado, Ana E.

doi:10.1016/s0031-3203(02)00116-4

Cited by 38 publications

(33 citation statements)

References 11 publications

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“…This decision is supported by the fact that these features have largely been considered as interesting in lots of applications [10][11][12][13][14][15][16] where selection and segmentation are crucial. • At object level the selection has been much more complicate.…”

Section: Selected Featuresmentioning

confidence: 99%

Dynamic visual attention model in image sequences

López

Fernández

Fernández-Caballero

et al. 2007

Image and Vision Computing

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A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer's attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user's interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest.

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Section: Selected Featuresmentioning

confidence: 99%

Dynamic visual attention model in image sequences

López

Fernández

Fernández-Caballero

et al. 2007

Image and Vision Computing

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“…This representation is also called accumulative computation, and has already been proved in applications such as moving object shape recognition in noisy environments [12] [13], moving objects classification by motion features such as velocity or acceleration [14], and in applications related to selective visual attention [15]. The more general modality of accumulative computation is the charge/discharge mode, which may be described by means of the following generic formula: Values of parameters C, D, Ch max and Ch min have to be fixed according to the applications characteristics.…”

Section: Accumulative Computation For Motion Detectionmentioning

confidence: 99%

Motion-Based Stereovision Method with Potential Utility in Robot Navigation

López-Valles

aacutendez

Fernández-Caballero

et al. 2005

Innovations in Applied Artificial Intelligence

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Abstract. Autonomous robot guidance in dynamic environments requires, on the one hand, the study of relative motion of the objects of the environment with respect to the robot, and on the other hand, the analysis of the depth towards those objects. In this paper, a stereo vision method, which combines both topics with potential utility in robot navigation, is proposed. The goal of the stereo vision model is to calculate depth of surrounding objects by measuring the disparity between the two-dimensional imaged positions of the object points in a stereo pair of images. The simulated robot guidance algorithm proposed starts from the motion analysis that occurs in the scene and then establishes correspondences and analyzes the depth of the objects. Once these steps have been performed, the next step is to induce the robot to take the direction where objects are more distant in order to avoid obstacles.

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“…Spatial contiguity makes good common sense in the human brain. Therefore, we have converted the discretely observed values into continuous surfaces, which are called movement surface slices (MSS) (Fernandez-Caballero et al 2003). MSSs continuously summarize movement data for a specified temporal interval.…”

Section: Modelling Movements Through Signaturesmentioning

confidence: 99%

Modelling uncertainties related to movements of objects through signatures

Palancioglu

2005

International Journal of Remote Sensing

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Moving objects have changing and repeating patterns due to their movements in space over time. A set of observations on a moving object can be used to derive the movement patterns by summarizing frequently visited locations and frequently used paths over time by the object. These space-and time-dependent movement patterns of an object are referred to as spatio-temporal movement signatures in this paper. There is a need to understand and identify the spatiotemporal movement signatures. Analysis of the spatio-temporal movement signatures can help improve the modelling, querying, and reasoning capabilities of the movements of objects. This paper demonstrates the extraction of movement signatures from sets of movement observations using Visualization and Resilient Neural Network methodologies. Identification of movement signatures and definition of their attributes provide summary-level information for modelling and reasoning about moving objects.

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Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

Cited by 38 publications

References 11 publications

Dynamic visual attention model in image sequences

Dynamic visual attention model in image sequences

Motion-Based Stereovision Method with Potential Utility in Robot Navigation

Modelling uncertainties related to movements of objects through signatures

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