Efficient framework for mobile walkthrough application

The past few years have witnessed a dramatic growth in the number and variety of graphics intensive mobile applications, which allow users to interact and navigate through large scenes such as historical sites, museums and virtual cities. These applications support many clients and impose a heavy requirement on network resources and computational resources. One key issue in the design of cost efficient mobile walkthrough applications is the data transmission between servers and mobile client devices. In this paper, we propose an effective progressive mesh transmission framework that stores and divide scene objects into different resolutions. In this approach, each mobile device progressively receives and processes only the object’s details matching its display resolution which improves the overall system’s response time and the user’s perception. A fine grained cache mechanism is used to keep the most frequently requested objects’ details in the device memory and consequently reduce the network traffic. Experiments, in simulated and real world environment, are used to illustrate the effectiveness of the proposed framework under various settings of the virtual scene and mobile device configuration. Experimental results show that the proposed framework can improve the walkthrough system performance in mobile devices, with a relatively small overhead.     

An Object Model for Multimedia Programming

The development of multimedia applications is a complex task. Much of this complexity stems from requirements associated with programming multiple media objects and the control of dependences and inter-relationships between these media objects and the user(s). It is clearly necessary to have a basic framework on which to build multimedia applications in the face of such complexities. Such a conceptual model is what we have called an object model and it is the main subject of this paper. The MADE object model represents a novel approach to multimedia application programming that is founded on the two principal concepts of active objects and delegation. Although these concepts are not novel in themselves, we believe that their combined use in a multimedia development environment represents a substantial enhancement to more traditional approaches to programming in this area.     

一个基于对象代理模型的多表现地理信息系统

文中提出了一种基于对象代理模型的实现多表现GIS的新方法。通过地理对象的代理对象来定义多表现，因此代理对象可以用来表示对象的视角多样性和角色多样性。通过对象更新迁移可以支持动态分类和系统一致性维护。另外，对象及其代理对象之间的双向指针使得跨类查询变得非常容易，从而可以扩展查询的范围。文中实现了一个基于对象代理模型的多表现GIS原型，性能测试表明该方法比传统的方法更加有效。     

C#智能设备编程时基于消息和事件实现的对象间通信

在嵌入式智能设备编程中,为了发送通知,传递数据,对象间的通信是一个重要问题.消息是传统Windows编程所使用的重要手段,事件是.Net环境下的新机制.主要从消息处理和事件处理两种不同的方法探索了对象间通信的方法,并给出了实现的框架代码.     

Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking

While particle filters are now widely used for object tracking in videos, the case of multiple object tracking still raises a number of issues. Among them, a first, and very important, problem concerns the exponential increase of the number of particles with the number of objects to be tracked, that can make some practical applications intractable. To achieve good tracking performances, we propose to use a Partitioned Sampling method in the estimation process with an additional feature about the ordering sequence in which the objects are processed. We call it Ranked Partitioned Sampling, where the optimal order in which objects should be processed and tracked is estimated jointly with the object state. Another essential point concerns the modeling of possible interactions between objects. As another contribution, we propose to represent these interactions within a formal framework relying on fuzzy sets theory. This allows us to easily model spatial constraints between objects, in a general and formal way. The association of these two contributions was tested on typical videos exhibiting difficult situations such as partial or total occlusions, and appearance or disappearance of objects. We show the benefit of using conjointly these two contributions, in comparison to classical approaches, through multiple object tracking and articulated object tracking experiments on real video sequences. The results show that our approach provides less tracking errors than those obtained with the classical Partitioned Sampling method, without the need for increasing the number of particles.     

Programs as visual,interactive documents

We present a novel approach to combined textual and visual programming by allowing visual, interactive objects to be embedded within textual source code and segments of source code to be further embedded within those objects. We retain the strengths of text‐based source code, while enabling visual programming where it is beneficial. Additionally, embedded objects and code provide a simple object‐oriented approach to adding a visual form of LISP‐style macros to a language. The ability to freely combine source code and visual, interactive objects with one another allows for the construction of interactive programming tools and experimentation with novel programming language extensions. Our visual programming system is supported by a type coercion‐based presentation protocol that displays normal Java and Python objects in a visual, interactive form. We have implemented our system within a prototype interactive programming environment called ‘The Larch Environment’. Copyright © 2013 John Wiley & Sons, Ltd.     

Taxonomy,technology and applications of smart objects

Deployment of embedded technologies is increasingly being examined in industrial supply chains as a means for improving efficiency through greater control over purchase orders, inventory and product related information. Central to this development has been the advent of technologies such as bar codes, Radio Frequency Identification (RFID) systems, and wireless sensors which when attached to a product, form part of the product’s embedded systems infrastructure. The increasing integration of these technologies dramatically contributes to the evolving notion of a “smart product”, a product which is capable of incorporating itself into both physical and information environments. The future of this revolution in objects equipped with smart embedded technologies is one in which objects can not only identify themselves, but can also sense and store their condition, communicate with other objects and distributed infrastructures, and take decisions related to managing their life cycle. The object can essentially “plug” itself into a compatible systems infrastructure owned by different partners in a supply chain. However, as in any development process that will involve more than one end user, the establishment of a common foundation and understanding is essential for interoperability, efficient communication among involved parties and for developing novel applications. In this paper, we contribute to creating that common ground by providing a characterization to aid the specification and construction of “smart objects” and their underlying technologies. Furthermore, our work provides an extensive set of examples and potential applications of different categories of smart objects.     

Exploiting practical limitations of UML diagrams for model validation and execution

We suggest a framework for UML diagram validation and execution that takes advantage of some of the practical restrictions induced by diagrammatic representations (as compared to Turing equivalent programming languages) by exploiting possible gains in decidability. In particular, within our framework we can prove that an object interaction comes to an end, or that one action is always performed before another. Even more appealingly, we can compute efficiently whether two models are equivalent (aiding in the redesign or refactoring of a model), and what the differences between two models are. The framework employs a simple modelling object language (called MOL) for which we present formal syntax and semantics. A first generation of tools has been implemented that allows us to collect experience with our approach, guiding its further development.     

Learning object deformation models for robot motion planning

In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot’s motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude.     

Fuzzy decision method to improve the information exchange in a vehicle sensor tracking system

Internet of Things is based on the identification of real-world objects in a unique way that interconnects them by means of communication interfaces. Such a simple idea allows the emergence of a huge number of new applications in almost any domain of knowledge. One of the most prominent areas of application is road vehicles, which currently have, on average, more than 50 sensors inside and which information can be accessed through a standard protocol. With this, vehicles have become real smart objects that can interact with other objects or any software system. To allow that, our previous work focused on proposing and developing the Vitruvius platform, where users with no programming knowledge can design and quickly generate Web applications based on the real-time data consumption from interconnected vehicles. The problem is that the sending of such information is out of control, being unable to filter out when the best time to send the information is and what information should be sent at any moment in order to minimize the resource consumption of the mobile device that acts as a bride between the vehicle and the database in which all the information is stored. Thus, in this work we propose a fuzzy algorithm that allows to optimize the resources that are used by real-time applications that constantly send data while maintaining data quality, contextualized in vehicle sensor tracking systems and the applications that can be built above them.     

Integrating Occlusion Culling and Levels of Detail through Hardly-Visible Sets

Occlusion culling and level-of-detail rendering have become two powerful tools for accelerating the handling of very large models in real-time visualization applications. We present a framework that combines both techniques to improve rendering times. Classical occlusion culling algorithms compute potentially visible sets (PVS), which are supersets of the sets of visible polygons. The novelty of our approach is to estimate the degree of visibility of each object of the PVS using synthesized coarse occluders. This allows to arrange the objects of each PVS into several Hardly-Visible Sets (HVS) with similar occlusion degree. According to image accuracy and frame rate requirements, HVS provide a way to avoid sending to the graphics pipeline those objects whose pixel contribution is low due to partial occlusion. The image error can be bounded by the user at navigation time. On the other hand, as HVS offer a tighter estimation of the pixel contribution for each scene object, it can be used for a more convenient selection of the level-of-detail at which objects are rendered. In this paper, we describe the new framework technique, provide details of its implementation using a visibility octree as the chosen occlusion culling data structure and show some experimental results on the image quality.     

Mobile digcovery: discovering and interacting with the world through the Internet of things

The application of Internet-enabled devices in the real world for the development of Smart Cities, environmental monitoring, bus tracking, and parking requires scalability, extensibility, and integration of emerging resources to reach a suitable ecosystem for data acquisition and interaction with citizens. Internet of things needs to offer efficient support for global communications and access to services and information. It needs to enable homogeneous and seamless machine-to-machine communication for different solutions and applications. This work presents an homogeneous and suitable mechanism for global resource discovery, device access for deployed smart objects in different scenarios, and sensors and devices from end users (participative sensing). The integration of legacy and sensors already available from smart buildings and smart objects is presented. For this purpose, a resolution infrastructure called “digcovery” is defined for maximizing efficiency and sustainability of deployments. Digcovery architecture offers the framework to allow users to register/include their own sensors into a common infrastructure and access/discover the available resources through mobile digcovery. Mobile digcovery exploits the context-awareness, geo-location, and identification technologies available in mobile platforms such as smartphones to discover, interact, and access the resources through its ElasticSearch engine.     

Real‐time smart surveillance using motion analysis

Abstract: In the last years, smart surveillance has been one of the most active research topics in computer vision because of the wide spectrum of promising applications. Its main point is about the use of automatic video analysis technologies for surveillance purposes. In general, a processing framework for smart surveillance consists of a preliminary motion detection step in combination with high‐level reasoning that allows automatic understanding of evolutions of observed scenes. In this paper, we propose a surveillance framework based on a set of reliable visual algorithms that perform different tasks: a motion analysis approach that segments foreground regions is followed by three procedures, which perform object tracking, homographic transformations and edge matching, in order to achieve the real‐time monitoring of forbidden areas and the detection of abandoned or removed objects. Several experiments have been performed on different real image sequences acquired from a Messapic museum (indoor context) and the nearby archaeological site (outdoor context) to demonstrate the effectiveness and the flexibility of the proposed approach.     

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.      

Robust Object Detection with Interleaved Categorization and Segmentation

This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.     

BDL: a specialized language for per-object reactive control

The problem of describing the concurrent behavior of objects in object oriented languages is addressed. The approach taken is to let methods be the behavior units whose synchronization is controlled separate from their specification. Our proposal is a domain-specific language called BDL for expressing constraints on this control and actually implementing its enforcement. We propose a model where each object includes a so-called “execution controller”, programmed in BDL. This separates cleanly the concepts of what the methods do, the object processes, from the circumstances in which they are allowed to do it, the control. The object controller ensures that scheduling constraints between the object's methods are met. Aggregate objects can be controlled in terms of their components. This language has a convenient formal base. Thus, using BDL expressions, behavioral properties of objects or groups of interesting objects can be verified. Our approach allows, for example, deadlock detection or verification of safety properties, while maintaining a reasonable code size for the running controller. A compiler from BDL has been implemented, automatically generating controller code in an Esterel program, i.e., in a reactive programming language. From this code, the Esterel compiler, in turn, generates an automaton on which verifications are done. Then this automaton is translated into a C code to be executed. This multistage process typifies the method for successful use of a domain-specific language. This also allows high level concurrent programming     

A framework for incremental construction of real global smart space applications

This article describes a standardised way to build context-aware global smart space applications using information that is distributed across independent (legacy, sensor-enabled, and embedded) systems by exploiting the overlapping spatial and temporal attributes of the information maintained by these systems. The framework supports a spatial programming model based on a topographical approach to modelling space that enables systems to independently define and use potentially overlapping spatial context in a consistent manner and in contrast to topological approaches, in which geographical relationships between objects are described explicitly. This approach is supported by an extensible data model that implicitly captures the relationships between information provided by separate underlying systems and facilitates the incremental construction of global smart spaces since the underlying systems to be incorporated are largely decoupled. The framework has been evaluated using a prototype that integrates legacy systems and context-aware services for multi-modal urban journey planning and for visualising traffic congestion.     

Taking an object-centric view on dynamic information with object flow analysis

A large body of research analyzes the runtime execution of a system to extract abstract behavioral views. Those approaches primarily analyze control flow by tracing method execution events or they analyze object graphs of heap memory snapshots. However, they do not capture how objects are passed through the system at runtime. We refer to the exchange of objects as the object flow, and we claim that it is necessary to analyze object flows if we are to understand the runtime of an object-oriented application. We propose and detail object flow analysis, a novel dynamic analysis technique that takes this new information into account. To evaluate its usefulness, we present a visual approach that allows a developer to study classes and components in terms of how they exchange objects at runtime. We illustrate our approach on three case studies.