Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of pas...

This paper is focused on determining which proteins affect the activity of Aryl Hydrocarbon Receptor (AHR) system when learning a model that can accurately predict its activity when single genes are knocked out. Experiments with results are presented when models are trained on a single source of information: abstracts from Medline (http://medline.cos.com/) that talk about the genes involved in ...

Abstract: This paper investigates the detection and classification of fighting and pre and post fighting events when viewed from a video camera. Specifically we investigate normal, pre, post and actual fighting sequences and classify them. A hierarchical AdaBoost classifier is described and results using this approach are presented. We show it is possible to classify pre-fighting situations usi...

Two alternative designs to the ensemble classifier proposed in [13] are studied in this report. First, the out-of-bag error estimation is replaced with crossvalidation. Second, we incorporate AdaBoost and modify the weights of the individual training samples as the training progresses. The final decision is formed as a weighted combination of individual predictions rather than through majority ...

SpatialBoost extends AdaBoost to incorporate spatial reasoning. We demonstrate the effectiveness of SpatialBoost on the problem of interactive image segmentation. Our application takes as input a trimap of the original image, trains SpatialBoost on the pixels of the object and the background and use the trained classifier to classify the unlabeled pixels. The spatial reasoning is introduced in ...

The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card ...

Image segmentation is the process of finding out all non-overlapping distinct regions from the given image based on certain criteria such as intensity, color, texture or shape. This paper proposes a two level hybrid non classical model for image segmentation based on pixel color and texture features of the image. The first level uses Fuzzy C-Means (FCM) unsupervised method to form a clustering ...

This paper reports the implementation of DRAPH-GP an extension of the decision graph algorithm DGRAPH-OW using the AdaBoost algorithm. This algorithm, which we call 1Stage Boosting, is shown to improve the accuracy of decision graphs, along with another technique which we combine with AdaBoost and call 2-Stage Boosting which shows greater improvement. Empirical tests demonstrate that both 1-Sta...

A face recognition algorithm based on local binary Haar feathers which represented as Kadane optimizing multi-threshold AdaBoost was proposed according to the problems of texture shape feature representation and classification algorithm accuracy in the process of facial classifying detection and recognition, First, improve the traditional expression by using image Local binary pattern of Haar f...