Abstract
Video surveillance systems must process and manage a growing amount of data captured over a network of cameras for various recognition tasks. In order to limit human labour and error, this paper presents a spatial-temporal fusion approach to accurately combine information from Region of Interest (RoI) batches captured in a multi-camera surveillance scenario. In this paper, feature-level and score-level approaches are proposed for spatial-temporal fusion of information to combine information over frames, in a framework based on ensembles of GMM-UBM (Universal Background Models). At the feature-level, features in a batch of multiple frames are combined and fed to the ensemble, whereas at the score-level the outcome of ensemble for individual frames are combined. Results indicate that feature-level fusion provides higher level of accuracy in a very efficient way.

Abstract
Group behaviour characterisation is a topic not so well studied in the video surveillance community due to its difficulty and large variety of topics involved, but mainly because the lack of valid semantic concepts that relate collective activity to social context. In this work, our proposal is three-fold: a new definition of semantic concepts for social group analysis considering environment context, a novel video surveillance dataset that conveys a sociological perspective, and a descriptor that emphasises social interactions cues within a group. Promising results were revealed in order to deal with such complex problem.

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. Digital colposcopy is a promising and inexpensive technology for the detection of cervical intraepithelial neoplasia. However, diagnostic sensitivity varies widely depending on the doctor expertise. Therefore, automation of this process is needed in both, detection and visualization. Colposcopies cover four steps: macroscopic view with magnifier white light, observation under green light, Hinselmann and Schiller. Also, there are transition intervals where the specialist manipulates the observed area. In this paper, we focus on the temporal segmentation of the video in these steps. Using our solution, physicians may focus on the step of interest and lesion detection tools can determine the interval to diagnose. We solved the temporal segmentation problem using Weighted Automata. Images were described by their chromacity histograms and labeled using a KNN classifier with a precision of 97%. Transition frames were recognized with a precision of 91 %.

Abstract

Abstract
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The Data Replication Method was proposed as tool for solving the ODC problem using a single binary classifier. Due to its characteristics, the Data Replication Method is straightforwardly mapped into methods that optimize the decision function globally. However, the mapping process is not applicable when the methods construct the decision function locally and iteratively, like decision trees and ADABOOST (with decision stumps). In this paper we adapt the Data Replication Method for ADABOOST, by softening the constraints resulting from the data replication process. Experimental comparison with state-of-the-art ADABOOST variants in synthetic and real data show the advantages of our proposal.

Abstract
Micro-solder joint (MSJ) lifetime prediction methodology and failure analysis (FA) are to assess reliability by fatigue model with a series of theoretical calculations, numerical simulation and experimental method. Due to shortened time of solder joints on high-temperature, high-frequency sampling error that is not allowed in productions may exist in various models, including round-off error. Combining intermetallic compound (IMC) growth theory and the FA technology for the magnetic head in actual production, this thesis puts forward a new growth model to predict life expectancy for solder joint of the magnetic head. And the impact of IMC, generating from interface reaction between slider (magnetic head, usually be called slider) and bonding pad, on mechanical performance during aging process is analyzed in it. By further researching on FA of solder ball bonding, thesis chooses AuSn4 growth model that affects least to solder joint mechanical property to indicate that the IMC methodology is suitable to forecast the solder lifetime. And the diffusion constant under work condition 60 A degrees C is 0.015354; the solder lifetime t is 14.46 years .