Abstract book (pdf) - ICPR 2010

In this paper we provide a framework of detection and localization of multiple similar shapes or object instances from an
image based on shape matching. There are three challenges about the problem. The first is the basic shape matching
problem about how to find the correspondence and transformation between two shapes; second how to match shapes under
occlusion; and last how to recognize and locate all the matched shapes in the image. We solve these problems by using
both graph partition and shape matching in a global optimization framework. A Hough-like collaborative voting is adopted,
which provides a good initialization, data-driven information, and plays an important role in solving the partial matching
problem due to occlusion. Experiments demonstrate the efficiency of our method.
13:50-14:10, Paper ThBT1.2
Bag of Hierarchical Co-Occurrence Features for Image Classification
Kobayashi, Takumi, National Inst. of Advanced Industrial Science and
Otsu, Nobuyuki, National Inst. of Advanced Industrial Science and
We propose a bag-of-hierarchical-co-occurrence features method incorporating hierarchical structures for image classification.
Local co-occurrences of visual words effectively characterize the spatial alignment of objects‘ components. The
visual words are hierarchically constructed in the feature space, which helps us to extract higher-level words and to avoid
quantization error in assigning the words to descriptors. For extracting descriptors, we employ two types of features hierarchically:
narrow (local) descriptors, like SIFT [1], and broad descriptors based on co-occurrence features. The proposed
method thus captures the co-occurrences of both small and large components. We conduct an experiment on image classification
by applying the method to the Caltech 101 dataset and show the favorable performance of the proposed method.
14:10-14:30, Paper ThBT1.3
Person Detection using Temporal and Geometric Context with a Pan Tilt Zoom Camera
Del Bimbo, Alberto, Univ. of Florence
Lisanti, Giuseppe, Univ. of Florence
Masi, Iacopo, Univ. of Florence
Pernici, Federico, Univ. of Florence
In this paper we present a system that integrates automatic camera geometry estimation and object detection from a Pan
Tilt Zoom camera. We estimate camera pose with respect to a world scene plane in real-time and perform human detection
exploiting the relative space-time context. Using camera self-localization, 2D object detections are clustered in a 3D world
coordinate frame. Target scale inference is further exploited to reduce the number of false alarms and to increase also the
detection rate in the final non-maximum suppression stage. Our integrated system applied on real-world data shows superior
performance with respect to the standard detector used.
14:30-14:50, Paper ThBT1.4
Disparity Map Refinement for Video based Scene Change Detection using a Mobile Stereo Camera Platform
Haberdar, Hakan, Univ. of Houston
Shah, Shishir, Univ. of Houston
This paper presents a novel disparity map refinement method and vision based surveillance framework for the task of detecting
objects of interest in dynamic outdoor environments from two stereo video sequences taken at different times and
from different viewing angles by a mobile camera platform. The proposed framework includes several steps, the first of
which computes disparity maps of the same scene in two video sequences. Preliminary disparity images are refined based
on estimated disparities in neighboring frames. Segmentation is performed to estimate ground planes, which in turn are
used for establishing spatial registration between the two video sequences. Finally, the regions of change are detected
using the combination of texture and intensity gradient features. We present experiments on detection of objects of different
sizes and textures in real videos.
14:50-15:10, Paper ThBT1.5
Using Symmetry to Select Fixation Points for Segmentation
Kootstra, Gert, KTH
Bergström, Niklas, Royal Inst. of Tech.
Kragic, Danica, KTH
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