Active Tuning of Intrinsic Camera Parameters

In the last years, the research effort of the scientific community to study systems for ambient intelligence has been really strong. Usually, the systems developed so far base their analysis on images acquired by automatic cameras. In this paper, we propose a way to develop new smart systems that are able to actively decide both what to see and how to see it. In particular, the main idea is to tune the acquisition parameters on the basis of what the system desires to acquire. The regulation strategy is based on two camera parameters, focus and iris. It aims to identify an optimal sequence of steps to enhance the acquisition quality of an object of interest. To this end, a hierarchy of neural networks has been employed first to select which parameter must be regulated then to adjust it. The proposed solution can be applied to both static and moving cameras. The results show how the proposed technique can be applied to images acquired by a moving camera with zoom capabilities for surveillance purposes.     

High-voltage management in single-supply CHE NOR-type flash memories

In a flash memory, a number of voltage levels different from V/sub DD/ are needed to perform the required operations (read, program, and erase) on the array cells. In the case of single-supply memory devices, voltages higher than V/sub DD/ as well as negative voltages, which are referred to as high voltages (HVs), must be produced on-chip. This paper aims at giving the reader an overview of how HVs are generated and managed in single-supply NOR-type flash memories programmed by channel hot-electron injection. Both schemes used for conventional (i.e., bilevel) memory devices and schemes designed to meet multilevel memory requirements are addressed.     

Video security for ambient intelligence

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.     

Deep interactive encoding with capsule networks for image classification

With new architectures providing astonishing performance on many vision tasks, the interest in Convolutional Neural Networks (CNNs) has grown exponentially in the recent past. Such architectures, however, are not problem-free. For instance, one of the many issues is that they require a huge amount of labeled data and are not able to encode pose and deformation information. Capsule Networks (CapsNets) have been recently proposed as a solution to the issues related to CNNs. CapsNet achieved interesting results in images recognition by addressing pose and deformation encoding challenges. Despite their success, CapsNets are still an under-investigated architecture with respect to the more classical CNNs. Following the ideas of CapsNet, we propose to introduce Residual Capsule Network (ResNetCaps) and Dense Capsule Network (DenseNetCaps) to tackle the image recognition problem. With these two architectures, we expand the encoding phase of CapsNet by adding residual convolutional and densely connected convolutional blocks. In addition to this, we investigate the application of feature interaction methods between capsules to promote their cooperation while dealing with complex data. Experiments on four benchmark datasets demonstrate that the proposed approach performs better than existing solutions.

     

Active video-based surveillance system: the low-level image and video processing techniques needed for implementation

The importance of video surveillance techniques has considerably increased since the latest terrorist incidents. Safety and security have become critical in many public areas, and there is a specific need to enable human operators to remotely monitor the activity across large environments. For these reasons, multicamera systems are needed to provide surveillance coverage across a wide area, ensuring object visibility over a large range of depths. In the development of advanced visual-based surveillance systems, a number of key issues critical to its successful operation must be addressed. This article describes the low-level image and video processing techniques needed to implement a modern surveillance system. In particular, the change detection methods for both fixed and mobile cameras (pan and tilt) are introduced and the registration methods for multicamera systems with overlapping and nonoverlapping views are discussed.     

The flash memory read path: building blocks and critical aspects

The ever-increasing demand for portable equipment is leading to the use of flash memory devices for nonvolatile storage. Fast access time and low power consumption are obviously key requirements. Moreover, multilevel storage techniques are mandatory to reduce the cost per megabit. As a consequence, the READ operation becomes more and more critical, and optimized solutions are needed for almost all circuits involved in the read path. This paper examines the whole read path from addresses to data output, and describes several schemes developed to achieve the required high performances.     

An Autonomous Vehicle for Video Surveillance of Indoor Environments

In this paper, the problem of the surveillance and the security of indoor environments is addressed through the development of an autonomous surveillance vehicle (ASV). The ASV has been designed to perform, in addition to the classical robotic tasks (e.g., navigation and obstacle avoiding), the tracking of objects (e.g., persons) moving in indoor environments. The selection of the target object to be tracked can be decided by a remote operator or autonomously by the ASV itself in the case that a suspicious behavior has been detected (e.g., a person entering a forbidden area, etc.). The tracking procedure allows the ASV to maintain the interesting objects in the center of the image and in specific cases to localize particular parts of the object (e.g., face of a person, etc.) in order to recognize it. Experimental results have been performed on different real scenarios where no objects move inside the monitored scene and where a group of people move in a hallway     

Playing to play: a piano-based user interface for music education video-games

Multimedia Tools and Applications - This paper presents and discusses a case-study where a video-game is the means through which the user is induced to learn to use a complex and not-intuitive...