Abstract book (pdf) - ICPR 2010

14:10-14:30, Paper TuBT7.3
Retinal Blood Vessels Segmentation using the Radial Projection and Supervised Classification
Peng, Qinmu, Huazhong Univ. of Science and Tech.
You, Xinge, Huazhong Univ. of Science and Tech.
Zhou, Long, Wuhan Pol. Univ.
Cheung, Yiu-Ming, Hong Kong Baptist Univ.
The low-contrast and narrow blood vessels in retinal images are difficult to be extracted but useful in revealing certain
systemic disease. Motivated by the goals of improving detection of such vessels, we propose the radial projection method
to locate the vessel centerlines. Then the supervised classification is used for extracting the major structures of vessels.
The final segmentation is obtained by the union of the two types of vessels after removal schemes. Our approach is tested
on the STARE database, the results demonstrate that our algorithm can yield better segmentation.
14:30-14:50, Paper TuBT7.4
Deep Belief Networks for Real-Time Extraction of Tongue Contours from Ultrasound During Speech
Fasel, Ian, Univ. of Arizona
Berry, Jeff, Univ. of Arizona
Ultrasound has become a useful tool for speech scientists studying mechanisms of language sound production. State-ofthe-art
methods for extracting tongue contours from ultrasound images of the mouth, typically based on active contour
snakes, require considerable manual interaction by an expert linguist. In this paper we describe a novel method for fully
automatic extraction of tongue contours based on a hierarchy of restricted Boltzmann machines (RBMs), i.e. deep belief
networks (DBNs). Usually, DBNs are first trained generatively on sensor data, then discriminatively to predict humanprovided
labels of the data. In this paper we introduce the translational RBM (tRBM), which allows the DBN to make use
of both human labels and raw sensor data at all stages of learning. This method yields performance in contour extraction
comparable to human labelers, without any temporal smoothing or human intervention, and runs in real-time.
14:50-15:10, Paper TuBT7.5
Automated Gland Segmentation and Classification for Gleason Grading of Prostate Tissue Images
Nguyen, Kien, Michigan State Univ.
Jain, Anil, Michigan State Univ.
Allen, Ronald, BioImagene
The well-known Gleason grading method for an H&E prostatic carcinoma tissue image uses morphological features of histology
patterns within a tissue slide to classify it into 5 grades. We have developed an automated gland segmentation and
classification method that will be used for automated Gleason grading of a prostatic carcinoma tissue image. We demonstrate
the performance of the proposed classification system for a three-class classification problem (benign, grade 3 carcinoma
and grade 4 carcinoma) on a dataset containing 78 tissue images and achieve a classification accuracy of 88.84%. In comparison
to the other segmentation-based methods, our approach combines the similarity of morphological patterns associated
with a grade with the domain knowledge such as the appearance of nuclei and blue mucin for the grading task.
TuCT1 Topkapı Hall B
Face Recognition – II Regular Session
Session chair: Tistarelli, Massimo (Univ. of Sassari)
15:40-16:00, Paper TuCT1.1
Multi-Resolution Local Appearance-Based Face Verification
Gao, Hua, Karlsruhe Inst. of Tech.
Ekenel, Hazim Kemal, Karlsruhe Inst. of Tech.
Fischer, Mika, Karlsruhe Inst. of Tech.
Stiefelhagen, Rainer, Karlsruhe Inst. of Tech. & Fraunhofer IITB
Facial analysis based on local regions/blocks usually outperforms holistic approaches because it is less sensitive to local
deformations and occlusions. Moreover, modeling local features enables us to avoid the problem of high dimensionality
of feature space. In this paper, we model the local face blocks with Gabor features and project them into a discriminant
identity space. The similarity score of a face pair is determined by fusion of the local classifiers. To acquire complementary
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