Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

A study of methods to predict and measure the transmission of sound through the walls of light aircraft. A survey of techniques for visualization of noise fields

A survey of the most widely used methods for visualizing acoustic phenomena is presented. Emphasis is placed on acoustic processes in the audible frequencies. Many visual problems are analyzed on computer graphic systems. A brief description of the current technology in computer graphics is included. The visualization technique survey will serve as basis for recommending an optimum scheme for displaying acoustic fields on computer graphic systems

Libraries in transition: evolving the information ecology of the Learning Commons: a sabbatical report

This sabbatical report studied various models in order to determine best practices for design, implementation and service of Leaning Commons, a library service model which functionally and spatially integrates library services, information technology services, and media services to provide a continuum of services to the user

Side-looking radar in urban research - A case study

Capabilities of side-looking radar in urban researc

Automatic Color Inspection for Colored Wires in Electric Cables

In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions

Requirements for education meterial in color reproduction for photojournalists

This thesis project examines a skills gap involving new technologies within photographic departments of many newspaper organizations. Traditional film based photography is now used in conjunction with digital photography. After images are acquired, photojournalists create electronic color separations within color imaging software. Quite often these separations are created without an understanding of the rules and concepts that govern quality color reproduction in newsprint. As advancements continue in digital imaging and prepress environments, skills must be acquired to ensure optimum color reproduction. This thesis project examines the educational theories of Walter Dick, Lou Carey and Charles Layne as they relate to a systematic design of instruction. An analysis of these theories provides an appropriate learning module for obtaining the required skills in color separation techniques. These theories include the recognition and identification of the following: an instructional goal, an instructional analysis, identification of entry behaviors and subordinate skills, and the design of instructional content. Results of this examination have been used for the creation of an instructional guide for the photojournalist. This guide has been designed and written for the photojournalist working in the digital prepress environment who encompasses the identified entry behaviors and subordinate skills required for quality learning. The photojournalist may be a veteran within the industry or a student of the trade. An evaluation of this thesis project will be based on the following: 1. A proposed workflow based upon the identification of various color electronic separation techniques used by photojournalists. This workflow will be incorporated into educational material that facilitates an optimum learning of concepts and procedures inherent to quality color reproduction in newsprint. 2. The creation of printed educational material based upon the theories derived from instruction designers

MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convolutional neural network (DCNN) facial attribute extraction, multi-task optimization is better. Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels. This paper addresses the multi-label imbalance problem by introducing a novel mixed objective optimization network (MOON) with a loss function that mixes multiple task objectives with domain adaptive re-weighting of propagated loss. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. When using facial attributes for the LFW face recognition task, we show that our balanced (domain adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016 http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_

The usefulness of thermal infrared and related imagery in the evaluation of agricultural resources. An exploratory study, volume I Final report

Multispectral imagery for aerial inventory and analysis of cultivated crop resource