In this paper, we propose a two-stage labeling method of large biomedical datasets through a parallel approach in a single GPU. Diagnostic methods, structures volume measurements, and visualization systems are of major importance for surgery planning, intra-operative imaging and image-guided surgery. In all cases, to provide an automatic and interactive method to label or to tag different struc...

To ensure the long-term safe and reliable operation of mechanical equipment, it is necessary to carry out fault diagnosis key components in system. A rotor method based on non-fuzzy solution weighted BP-AdaBoost (NFSW-BP-AdaBoost) proposed this paper. First, vibration signal transformed into a high-dimensional feature set. Then, VC value used obtain sequence. Finally, NFSW-BP-AdaBoost diagnose ...

Segmentation plays a vital role in determining the tumor in brain MR Images. The analysis is done using multifractional Brownian motion (mBm) to devise the tumor in brain MR images. The spatially varying feature is extracted using mBm and corresponding algorithm. Then segmentation is carried out based on multifractal features. An algorithm for segmentation is proposed by modifying the well-know...

automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. it has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. in this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and hum...

We provide an analysis of AdaBoost within the framework of algorithmic stability. In particular, we show that AdaBoost is a stabilitypreserving operation: if the “input” (the weak learner) to AdaBoost is stable, then the “output” (the strong learner) is almost-everywhere stable. Because classifier combination schemes such as AdaBoost have greatest effect when the weak learner is weak, we discus...

This paper explores how multi-armed bandits (MABs) can be applied to accelerate AdaBoost. AdaBoost constructs a strong classifier in a stepwise fashion by adding simple base classifiers to a pool and using their weighted “vote” to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs. Each arm represents ...

Object detection using AdaBoost cascade classifier was introduced by Viola and Jones in December 2001. This paper presents a modification of their method which allows to obtain even 4-fold decrease in false rejection rate, keeping false acceptance rate – as well as the classifier size and training time – at the same level. Such an improvement is achieved by extending original family of weak cla...

This paper describes an improved boosting algorithm, named RegionBoost, and its application in developing a fast and robust invariant Local Binary Pattern histogram based face recognition system. We propose to use a multi-classifier where each classifier, an AdaBoost of feed-forward back-propagation network, is trained using a single Sub-Window of the whole image, the classifiers are finally co...

Si Qing Zhang Run Zheng 1 Zhengzhou College of Science &Technology, Zhengzhou China 450064 a [email protected] Abstract—In the traditional content-based image retrieval system, for a given query image, the number of relevant images in the database are not far outnumber correlation image. Therefore, a number of negative samples and the number of positive sample is unbalanced, the two class classifier...