177 Chapters
Medium 9781601322548

LURID: A Heuristically Based System for Automated Image Safety Determination

Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; and Vladimir Volkov (Editors) Mercury Learning and Information PDF

334

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

LURID: A Heuristically Based System for

Automated Image Safety Determination

1

Daniel S. Rosen1

Director, Imaging Technology, GumGum, Inc., 12407 4th St., Suite 400, Santa Monica, CA, 90401

Abstract - Much research has been devoted to the automatic detection of objectionable imagery. In those situations where image safety is directly tied to revenues, the minimization of false alarms rates is of primary importance. Skin detection algorithms that seek to classify images as being safe or unsafe based upon apriori skin content thresholds, have been found to have unacceptably high false alarm rates in classifying images as being objectionable. While improved trained classifiers have been introduced which provide good results, they require large training sets and great care must be exercised in the selection of the classification parameters in order to provide the best performance. LURID is a system that utilizes heuristics based on anthropometry to create a robust system for determining image safety with very low false alarm rates.

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Medium 9781601322548

Session - Biometrics: Gait, Iris, Fingerprint, ... + Identification + Handwriting Analysis

Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; and Vladimir Volkov (Editors) Mercury Learning and Information PDF
Medium 9781601322548

Vision-Based Localization and Text Chunking of Nutrition Fact Tables on Android Smartphones

Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; and Vladimir Volkov (Editors) Mercury Learning and Information PDF

314

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

Vision-Based Localization and Text Chunking of Nutrition Fact

Tables on Android Smartphones

Vladimir Kulyukin1, Aliasgar Kutiyanawala1, Tanwir Zaman1, and Stephen Clyde2

1

Department of Computer Science, Utah State University, Logan, UT, USA

2

MDSC Corporation, Salt Lake City, UT, USA

Abstract—Proactive nutrition management is considered by many nutritionists and dieticians as a key factor in reducing and controlling cancer, diabetes, and other illnesses related to or caused by mismanaged diets. As more and more individuals manage their daily activities with smartphones, smartphones have the potential to become proactive diet management tools.

While there are many vision-based mobile applications to process barcodes, there is a relative dearth of vision-based applications for extracting other useful nutrition information items from product packages, e.g., nutrition facts, caloric contens, and ingredients. In this paper, we present a visionbased algorithm to localize aligned nutrition fact tables

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Medium 9781601322548

Denoising Time-of-Flight Depth Maps Using Temporal Median Filter

Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; and Vladimir Volkov (Editors) Mercury Learning and Information PDF

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

DENOISING TIME-OF-FLIGHT DEPTH MAPS USING TEMPORAL

MEDIAN FILTER

1

Fang-Yu Lin, 1,†Yi-Leh Wu, 1Wei-Chih Hung

1

Department of Computer Science and Information Engineering

National Taiwan University of Science and Technology, Taiwan

E-mail: ywu@csie.ntust.edu.tw

ABSTRACT

In many types of 3D cameras, The Time-of-Flight

(TOF) cameras have the advantages of simplicity for use and lower price for general public. The TOF cameras can obtain depth maps at video speed. However, the

TOF cameras suffer from low resolution and high random noise. In this paper, we propose methods to reduce the random noise in depth maps captured by the

TOF cameras. For each point in the noisy TOF depth map, we substitute the depth value with the median depth value of its corresponding points in temporally consecutive depth maps captured by the TOF cameras.

The proposed methods require only the depth data captured by the TOF cameras without any extra information, such as illumination, geometric shape, or complex parameters. Experiments results suggest that the proposed temporal denoising methods can effective reduce the noise in TOF depth maps for up to 44 percent.

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Medium 9781601322548

Automatic Thresholding Techniques for Alzheimer's Disease Diagnosis

Hamid R. Arabnia; Leonidas Deligiannidis; Joan Lu; Fernando G. Tinetti; Jane You; George Jandieri; Gerald Schaefer; Ashu M. G. Solo; and Vladimir Volkov (Editors) Mercury Learning and Information PDF

262

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

Automatic Thresholding Techniques for Alzheimer’s

Disease Diagnosis

Moumena Al-Bayati, and Ali El-Zaart

Department of Mathematics and Computer Science, Beirut Arab University

Beirut, Lebanon

Abstract - Images has become an essential role in diagnosis the diseases especially the Magnetic Resonance

Imaging(MRI). However, used(MRI)that diagnosis Alzheimer's disease is still remains a challenge, especially in the early stages, when the disease offers more chances to be treated. In this paper we present medical images diagnosis for

Alzheimer's disease using different thresholding techniques.

The used method is Otsu method ,because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) atrophy neurons in the brain, and in the future these techniques can be very useful in detection other diseases .

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