Fuzzy shape Classification exploiting Geometrical and Moments Descriptors

In the era of data intensive management and discovery, the volume of images repositories requires effective means for mining and classifying digital image collections. Recent studies have evidenced great interest in image processing by "mining" visual information for objects recognition and retrieval. Particularly, image disambiguation based on the shape produces better results than traditional features such as color or texture. On the other hand, the classification of objects extracted from images appears more intuitively formulated as a shape classification task. This work introduces an approach for 2D shapes classification, based on the combined use of geometrical and moments features extracted by a given collection of images. It achieves a shape based classification exploiting fuzzy clustering techniques, which enable also a query-by-image

GPU Accelerated Multi-agent Path Planning Based on Grid Space Decomposition

In this work, we describe a simple and powerful method to implement real-time multi-agent path-finding on Graphics Processor Units (GPUs). The technique aims to find potential paths for many thousands of agents, using the A* algorithm and an input grid map partitioned into blocks. We propose an implementation for the GPU that uses a search space decomposition approach to break down the forward search A* algorithm into parallel independently forward sub-searches. We show that this approach fits well with the programming model of GPUs, enabling planning for many thousands of agents in parallel in real-time applications such as computer games and robotics. The paper describes this implementation using the Compute Unified Device Architecture programming environment, and demonstrates its advantages in GPU performance compared to GPU implementation of Real-Time Adaptive A*

Hand-draw sketching for image retrieval through fuzzy clustering techniques

Nowadays, the growing of digital media such as images represents an important issue for niultimedia mining applications. Since the traditional information retrieval techniques developed for textual documents do not support adequately these media, new approaches for indexing and retrieval of images are needed. In this paper, we propose an approach for retrieving image by hand-drawn object sketch. For this purpose. we address the classification of images based on shape recognition. The classification is based on the combined use of geometrical and moments features extracted by a given collection of images and achieves shape-based classification through fuzzy clustering techniques. Then, the retrieval is obtained using a hand-draw shape that becomes a query to submit to the system and get ranked similar images

GPU Cost Estimation for Load Balancing in Parallel Ray Tracing

Interactive ray tracing has seen enormous progress in recent years. However, advanced rendering techniques requiring many million rays per second are still not feasible at interactive speed, and are only possible by means of highly parallel ray tracing. When using compute clusters, good load balancing is crucial in order to fully exploit the available computational power, and to not suffer from the overhead involved by synchronization barriers. In this paper, we present a novel GPU method to compute a costmap: a per-pixel cost estimate of the ray tracing rendering process. We show that the cost map is a powerful tool to improve load balancing in parallel ray tracing, and it can be used for adaptive task partitioning and enhanced dynamic load balancing. Its effectiveness has been proven in a parallel ray tracer implementation tailored for a cluster of workstations

SLAM map application for tracking lights on car dashboards

Recent studies conducted by some insurance companies highlighted that the most part of drivers do not know the meaning of the dashboard lights. This leads drivers to be dangerous for others and themselves. Hence, the need to provide drivers with tools that support them to always be aware of the state of their cars. This paper proposes a system for mobile devices that uses augmented reality to be able to give information on a particular dashboard lights. The system is implemented by combining the use of Simultaneous Localization and Mapping (SLAM) maps with central moment computation widely used in computer vision. Preliminary research results show that the proposed system achieves its goal enables a a real-time visual feedback

Exploring the effectiveness of an augmented reality dressing room

In this paper, we describe our experience with the design of an augmented reality dressing room in which 3D models of a dress are overlaid with a color image from a camera to provide the function of a sort of virtual mirror. In such a way, the customer can move around to understand if a dress suits and fits them well. The project is implemented in Unity 4 Pro in combination with the Microsoft Kinect 2 for the tracking process. Design issues and technical implementation as well as the prospects for further development of the techniques are discussed. To assess the validity of our proposal, we have conducted a user study using 47 participants with different levels of experience with video games and devices used to play them. The empirical method used is qualitative. To this end, we used questionnaire-based surveys. The obtained results suggest that our solution represents a viable means to simulate dressing rooms, and participants in the study found the interaction with our 3D models to be natural for understanding if a dress suits and fits them well. Overall, the participants found our application very useful from a practical point of view

An Interactive Bio-inspired Approach to Clustering and Visualizing Datasets

In this work, we present an interactive visual clustering approach for the exploration and analysis of datasets using the computational power of Graphics Processor Units (GPUs). The visualization is based on a collective behavioral model that enables cognitive amplification of information visualization. In this way, the workload of understanding the representation of information moves from the cognitive to the perceptual system. The results enable a more intuitive, interactive approach to the discovery of knowledge. The paper illustrates this behavioral model for clustering data, and applies it to the visualization of a number of real and synthetic datasets

Implementation of a Coin Recognition System for Mobile Devices with Deep Learning

This paper examines the application of a deep learning approach to automatic coin recognition, via a mobile device and client-server architecture. We show that a convolutional neural network is effective for coin identification. During the training phase, we determine the optimum size of the training dataset necessary to achieve high classification accuracy with low variance. In addition, we propose a client-server architecture that enables a user to identify coins by photographing it with a smartphone. The image provided by the user is matched with the neural network on a remote server. A high correlation suggests that the image is a match. The application is a first step towards the automatic identification of coins and may help coin experts in their study of coins and reduce the associated expense of numismatic applications

A client-server framework for the design of geo-location based augmented reality applications

We present a client-server framework for the development of mobile applications that use Augmented Reality (AR) to visualize geolocated data. Geo-information displays allow users to understand and respond effectively to the context in which the application is deployed. We provide a scalable and flexible architecture for the development and management of the client, the server and the data that are used by the applications. This architecture is based on the display of connected layers that represent structured information. The approach has been implemented in two case studies: the management of failures in electrical power lines, and to support hydrogeological monitoring

On the effect of exploiting gpus for a more Eco-sustainable lease of life

It has been estimated that about 2% of global carbon dioxide emissions can be attributed to IT systems. Green (or sustainable) computing refers to supporting business critical computing needs with the least possible amount of power. This phenomenon changes the priorities in the design of new software systems and in the way companies handle existing ones. In this paper, we present the results of a research project aimed to develop a migration strategy to give an existing software system a new and more eco-sustainable lease of life. We applied a strategy for migrating a subject system that performs intensive and massive computation to a target architecture based on a Graphics Processing Unit (GPU). We validated our solution on a system for path finding robot simulations. An analysis on execution time and energy consumption indicated that: (i) the execution time of the migrated system is less than the execution time of the original system; and (ii) the migrated system reduces energy waste, so suggesting that it is more eco-sustainable than its original version. Our findings improve the body of knowledge on the effect of using the GPU in green computing. © 2015 World Scientific Publishing Company