Abstract book (pdf) - ICPR 2010

Gurcan, Metin, The Ohio State Univ.
The gold standard in follicular lymphoma (FL) diagnosis and prognosis is histopathological examination of tumor tissue
samples. However, the qualitative manual evaluation is tedious and subject to considerable inter- and intra-reader variations.
In this study, we propose an image analysis system for quantitative evaluation of digitized FL tissue slides. The developed
system uses a robust feature space analysis method, namely the mean shift algorithm followed by a hierarchical grouping
to segment a given tissue image into basic cytological components. We then apply further morphological operations to
achieve the segmentation of individual cells. Finally, we generate a likelihood measure to detect candidate cancer cells
using a set of clinically driven features. The proposed approach has been evaluated on a dataset consisting of 100 region
of interest (ROI) images and achieves a promising 89% average accuracy in detecting target malignant cells.
16:50-17:10, Paper MoBT7.5
Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary Learning
Zhang, Bob, Univ. of Waterloo
Zhang, Lei, The Hong Kong Pol. Univ.
You, Jane, The Hong Kong Pol. Univ.
Karray, Fakhri, Univ. of Waterloo
Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated
late. In its earliest stage, DR can be diagnosed by micro aneurysm (MA). Although some algorithms have been developed,
the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method
to detect MA based on Sparse Representation Classifier (SRC). We first roughly locate MA candidates by using multiscale
Gaussian correlation filtering, and then classify these candidates with SRC. Particularly, two dictionaries, one for
MA and one for non-MA, are learned from example MA and non-MA structures, and are used in the SRC process. Experimental
results on the ROC database show that the proposed method can well distinguish MA from non-MA objects.
MoBT8 Lower Foyer
Object Detection and Recognition; Performance Evaluation of Computer Vision Algorithms; Computer Vision
Applications Poster Session
Session chair: Chen, Chu-Song (Academia Sinica)
15:00-17:10, Paper MoBT8.1
A Neurobiologically Motivated Stochastic Method for Analysis of Human Activities in Video
Sethi, Ricky, Univ. of California, Riverside
Roy-Chowdhury, Amit, Univ. of California, Riverside
In this paper, we develop a neurobiologically-motivated statistical method for video analysis that simultaneously searches
the combined motion and form space in a concerted and efficient manner using well-known Markov chain Monte Carlo
(MCMC) techniques. Specifically, we leverage upon an MCMC variant called the Hamiltonian Monte Carlo (HMC),
which we extend to utilize data-based proposals rather than the blind proposals in a traditional HMC, thus creating the
Data-Driven HMC (DDHMC). We demonstrate the efficacy of our system on real-life video sequences.
15:00-17:10, Paper MoBT8.2
Arbitrary Stereoscopic View Generation using Multiple Omnidirectional Image Sequences
Hori, Maiya, Nara Inst. of Science and Tech.
Kanbara, Masayuki, Nara Inst. of Science and Tech.
Yokoya, Naokazu, Nara Inst. of Science and Tech.
This paper proposes a novel method for generating arbitrary stereoscopic view from multiple omni directional image sequences.
Although conventional methods for arbitrary view generation with an image-based rendering approach can create
binocular views, positions and directions of viewpoints for stereoscopic vision are limited to a small range. In this research,
we attempt to generate arbitrary stereoscopic views from omni directional image sequences that are captured in various
multiple paths. To generate a high-quality stereoscopic view from a number of images captured at various viewpoints, appropriate
ray information needs to be selected. In this paper, appropriate ray information is selected from a number of
omni directional images using a penalty function expressed as ray similarity. In experiments, we show the validity of this
penalty function by generating stereoscopic view from multiple real image sequences.
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