Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Geometric Aspects of Visual Object Recognition

This thesis presents there important results in visual object recognition based on shape. (1) A new algorithm (RAST; Recognition by Adaptive Sudivisions of Tranformation space) is presented that has lower average-case complexity than any known recognition algorithm. (2) It is shown, both theoretically and empirically, that representing 3D objects as collections of 2D views (the "View-Based Approximation") is feasible and affects the reliability of 3D recognition systems no more than other commonly made approximations. (3) The problem of recognition in cluttered scenes is considered from a Bayesian perspective; the commonly-used "bounded-error errorsmeasure" is demonstrated to correspond to an independence assumption. It is shown that by modeling the statistical properties of real-scenes better, objects can be recognized more reliably.

Memory Usage Inference for Object-Oriented Programs

We present a type-based approach to statically derive symbolic closed-form formulae that characterize the bounds of heap memory usages of programs written in object-oriented languages. Given a program with size and alias annotations, our inference system will compute the amount of memory required by the methods to execute successfully as well as the amount of memory released when methods return. The obtained analysis results are useful for networked devices with limited computational resources as well as embedded software.

GRASS GIS for the distinction of vegetation from buildings using LiDAR altimetric data

La tecnología LiDAR (Light Detection and Ranging), basada en el escaneado del territorio por un telémetro láser aerotransportado, permite la construcción de Modelos Digitales de Superficie (DSM) mediante una simple interpolación, así como de Modelos Digitales del Terreno (DTM) mediante la identificación y eliminación de los objetos existentes en el terreno (edificios, puentes o árboles). El Laboratorio de Geomática del Politécnico de Milán – Campus de Como- desarrolló un algoritmo de filtrado de datos LiDAR basado en la interpolación con splines bilineares y bicúbicas con una regularización de Tychonov en una aproximación de mínimos cuadrados. Sin embargo, en muchos casos son todavía necesarios modelos más refinados y complejos en los cuales se hace obligatorio la diferenciación entre edificios y vegetación. Este puede ser el caso de algunos modelos de prevención de riesgos hidrológicos, donde la vegetación no es necesaria; o la modelización tridimensional de centros urbanos, donde la vegetación es factor problemático. (...)

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Use of decision trees in colour feature selection: application to object recognition in outdoor scenes

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

Lecture 8 - über-Arrays

ArrayList ArrayList vs Array Declaration Insertion Access Removal Wrapper classes Iterator object

Reusable Learning Object repository at the University of Nottingham

Over 100 RLOs in subjects relevant to health, including evidence-based practice, clinical skills, basic sciences, pharmacology, physiology, genetics and study skills.

Object Orientated PHP

To replace Application Scripting and Contemporary Programming Principles

Using the Browse Object tool in MS Word 2010

Use the Browse Object tool to quickly navigate through a selected type of object in your file – pages, tables, sections, images, footnotes or headings of your document. For best viewing Download the video.

Using the Browse Object tool in MS Word 2011

Use the Browse Object tool to quickly navigate through a selected type of object in your file – pages, tables, sections, images, footnotes or headings of your document. For best viewing Download the video.

Development and/or change of the notion of permanent object and operative causality: empirical evidence in the first year of life

This study examines the notion of permanent object during the first year of life, taking into account the controversy of two approaches about the nature of change: developmental change and cognitive change. Using a longitudinal/cross-sectional design, tasks adapted of the subscale of permanent object and operative causality of the Uzgiris-Hunt Scale (Uzgiris and Hunt, 1975) (Uzgiris & Hunt, 1975) were presented to 110 infants of 0, 3, 6 and 9 months-old, which reside in three cities of Colombia. The results showed three types of strategies: (a) Not resolution; (b) Exploratory and (c) Resolution, which follow different trajectories in children’s performance. This allows affirming that adaptive conquests of the cognitive development stay together with the variety of strategies. Using strategies reveals adjustments and transformations of action programs that consolidate the notion of permanent object not necessarily with age, but with self-regulatory processes. Empirical evidence contributes to the understanding of the relations between the emergence of novelty in the development and performance variability

Biological nutrient removal in SBR technology: from floccular to granular sludge

Biological nutrient removal has been studied and applied for decades in order to remove nitrogen and phosphorus from wastewater. However, more anthropogenic uses and the continued demand for water have forced the facilities to operate at their maximum capacity. Therefore, the goal of this thesis is to obtain more compact systems for nutrient removal from domestic wastewater. In this sense, optimization and long-term stabilization of high volume exchange ratios reactors, treating higher volumes of wastewater, have been investigated. With the same target, aerobic granular sludge was proposed as a reliable alternative to reduce space and increase loading rates in treatment plants. However, the low organic loading rate from low-strength influents (less than 1 Kg COD•m-3d-1) results in slower granular formation and a longer time to reach a steady state. Because of that, different methodologies and operational conditions were investigated in order to enhance granulation and nutrient removal from domestic wastewater.

Operation and control of SBR processes for enhanced biological nutrient removal from wastewater

In the last decades, the awareness of environmental issues has increased in society considerably. There is an increasing need to improve the effluent quality of domestic wastewater treatment processes. This thesis describes the application of the Sequencing Batch Reactor (SBR) technology for Biological Nutrient Removal (BNR) from the wastewater. In particular, the work presented evolves from the nitrogen removal to the biological nutrient removal (i.e. nitrogen plus phosphorous removal) with special attention to the operational strategy design, the identification of possible reactor cycle controls or the influent composition related to the process efficiency. In such sense, also the use of ethanol as an external carbon (when low influent Carbon:Phosphorus (C:P) or Carbon:Nitrogen (C:N) ratios are presented) are studied as an alternative to maintain the BNR efficiency.