Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT8.53
Spike-Based Convolutional Network for Real-Time Processing
Pérez-Carrasco, Jose-Antonio, Univ. de Sevilla
Serrano-Gotarredona, Carmen, Univ. de Sevilla
Acha-Piñero, Begoña, Univ. de Sevilla
Serrano-Gotarredona, Teresa, Univ. de Sevilla
Linares-Barranco, Bernabe, Univ. de Sevilla
In this paper we propose the first bio-inspired six layer convolutional network (ConvNet) non-frame based that can be implemented
with already physically available spikebased electronic devices. The system was designed to recognize people in
three different positions: standing, lying or up-side down. The inputs were spikes obtained with a motion retina chip. We
provide simulation results showing recognition delays of 16 milliseconds from stimulus onset (time-to-first spike) with a
recognition rate of 94%. The weight sharing property in ConvNets and the use of AER protocol allow a great reduction in
the number of both trainable parameters and connections (only 748 trainable parameters and 123 connections in our AER
system (out of 506998 connections that would be required in a frame-based implementation).
13:30-16:30, Paper WeBCT8.54
Learning Affordances for Categorizing Objects and Their Properties
Dag, Nilgun, Middle East Tech. Univ.
Atil, Ilkay, Middle East Tech. Univ.
Kalkan, Sinan, Middle East Tech. Univ.
Sahin, Erol, Middle East Tech. Univ.
In this paper, we demonstrate that simple interactions with objects in the environment leads to a manifestation of the perceptual
properties of objects. This is achieved by deriving a condensed representation of the effects of actions (called effect prototypes
in the paper), and investigating the relevance between perceptual features extracted from the objects and the actions that can
be applied to them. With this at hand, we show that the agent can categorize (i.e., partition) its raw sensory perceptual feature
vector, extracted from the environment, which is an important step for development of concepts and language. Moreover,
after learning how to predict the effect prototypes of objects, the agent can categorize objects based on the predicted effects
of actions that can be applied on them.
13:30-16:30, Paper WeBCT8.55
Feature Pairs Connected by Lines for Object Recognition
Awais, Muhammad, Univ. of Surrey
Mikolajczyk, Krystian, Univ. of Surrey
In this paper we exploit image edges and segmentation maps to build features for object category recognition. We build a
parametric line based image approximation to identify the dominant edge structures. Line ends are used as features described
by histograms of gradient orientations. We then form descriptors based on connected line ends to incorporate weak topological
constraints which improve their discriminative power. Using point pairs connected by an edge assures higher repeatability
than a random pair of points or edges. The results are compared with state-of-the-art, and show significant improvement on
challenging recognition benchmark Pascal VOC 2007. Kernel based fusion is performed to emphasize the complementary
nature of our descriptors with respect to the state-of-the-art features.
13:30-16:30, Paper WeBCT8.56
Using Gait Features for Improving Walking People Detection
Bouchrika, Imed, Univ. of Southampton
Carter, John, Univ. of Southampton
Nixon, Mark, Univ. of Southampton
Morzinger, Roland, Joanneum Res.
Thallinger, Georg, Joanneum Res.
In this paper, we explore a new approach for enriching the HoG method for pedestrian detection in an unconstrained outdoor
environment. The proposed algorithm is based on using gait motion since the rhythmic footprint pattern for walking people
is considered the stable and characteristic feature for the detection of walking people. The novelty of our approach is motivated
by the latest research for people identification using gait. The experimental results confirmed the robustness of our method
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