Abstract
We propose a denoising and segmentation technique for the second heart sound (S2). To denoise, Matching Pursuit (MP) was applied using a set of non-linear chirp signals as atoms. We show that the proposed method can be used to segment the phonocardiogram of the second heart sound into its two clinically meaningful components: the aortic (A2) and pulmonary (P2) components.

Abstract
Video encoding standards, namely MPEG-2, store large amounts of information obtained for compression purposes that can be accessed with minimal decoding. This paper shows that, with proper filtering of motion vectors and DCT coefficients, accurate segmentation results can be achieved by combining both reliable motion estimation and background subtraction. We further present a fine segmentation step that exploits specific blob characteristics to reduce segmentation noise and solve some occlusion problems. Examples using real videos from underground station CCTV cameras show that compressed domain information can be the key for successful surveillance applications where very fast algorithms with high accuracy are required.

Abstract
The endoscopic capsule is a recent medical technology with important clinical benefits but suffering from a practical handicap: long exam annotation times. This paper shows how support vector machines can be used to segment the gastrointestinal tract into its four major topographic areas, allowing the automatic estimation of the clinically relevant gastric and intestinal transit times. According to medical specialists, this can reduce exam annotation times by up to 12%.

Abstract
The DigiScope project aims at developing a digitally enhanced stethoscope capable of using state of the art technology in order to help physicians in their daily medical routine. One of the main tasks of DigiScope is to build a repository of auscultations (sound and medical related data). In this work, we present a preliminary analysis and study of the first auscultations performed on children of a Brazilian hospital. Results indicate that classifiers can be obtained that distinguish reasonably well patients with cardiac pathologies from those that do not have pathologies.

Abstract
MPEG-2 compressed domain information, namely motion vectors and DCT coefficients, is filtered and manipulated to obtain a motion field using a two-dimensional (2-D) translational model. The results are compared to a popular optical flow method, more specifically the one presented by Lucas and Kanade, revealing very good results. Our method provides a very fast motion estimation tool that can be useful for applications where algorithmic cost is critical, such as surveillance systems. All methods are theoretically explained and their efficiency confirmed on real-world data.

Abstract
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MITBIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.