Image Sensor

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have a lot of successful applications. Spiking neuron networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation and edge detection. Results obtained confirm the validity of the approach.

A per-pixel dark current spectroscopy measurement and analysis technique for identifying deep-level traps in CMOS imagers is presented. The short integration time transfer gate subtraction experimental technique used to obtain accurate results is described and discussed. The activation energies obtained for molybdenum (≈0.3 eV), tungsten (≈0.37 eV), and the phosphorus-vacancy (E-center) (≈0.44 eV) trap levels in silicon match published results

We describe a simple method of gluing fibre-optic faceplates to complementary metal oxide semiconductor (CMOS) active pixel and charge coupled device (CCD) image sensors and report on their performance. Cross-sectional cuts reveal that the bonding layer has a thickness close to the diameter of the individual fibres and is uniform over the whole sensor area. Our method requires no special tools or alignment equipment and gives reproducible and high-quality results. The method maintains a uniform bond layer thickness even if sensor dies are mounted at slight angles with their package. These fibre-coupled sensors are of particular interest to X-ray imaging applications but also provide a solution for compact optical imaging systems.

Analogue near-infrared (NIR) photography has already been used a lot in both scientific and medical photography, in which cases near-infrared (NIR) radiation was mostly captured by InfraRed (IR) sensitive plates or film emulsions. However, its use in archaeology has remained rather restricted, most likely due to some ignorance and/or lack of knowledge about this kind of photography, while the critical imaging process also severely limited its use. This situation could be, however, changed completely, as the image sensors used in digital still cameras (DSCs) are very sensitive to NIR wavelengths, making the quite lengthy and error-prone film-based NIR imaging process obsolete. Moreover, modifying off-the-shelf DSCs even simplifies this digital acquisition of NIR photographs to a very large extent. By starting with a general outline of the ElectroMagnetic (EM) spectrum and the specificities of NIR radiation, the base is laid out to tackle the possibilities and practicalities of archaeological NIR imaging, subsequently comparing the earlier film-based approach with the digital way of NIR shooting, showing how the latter can greatly benefit from modified compact, hybrid and small-format Single Lens Reflex (SLR) DSCs. Besides in-depth information on the technique of digital NIR photography, examples will illustrate its archaeological potential.

ABSTRACT, The design and characterization of an imaging,sensor based on single,photon avalanche,diodes is presented. The sensor was fully integrated in a 0.35µm CMOS technology. The core of the imager,is an array of 4x112 pixels that independently,and simultaneously detect the arrival time of photons,with picosecond,accuracy. A novel event-driven readout scheme,allows parallel column-wise and non-sequential, on-demand row-wise operation. Both time-correlated and time-uncorrelated

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

In tropical areas, mass movements are common phenomena, especially during periods of heavy rainfall, which frequently take place in the summer season. These phenomena have caused loss of life and serious damage to infrastructure and properties. The most prominent of these phenomena are landslides that can produce debris flows. Thus, this article aims at determining affected areas using a model to predict landslide prone areas (SHALSTAB) combined with an empirical model designed to define the debris flow travel distance and area of deposition. The methodology of this work consists of the following steps: (a) elaboration of a digital elevation model (DEM), (b) application of the deterministic SHALSTAB model to locate the landslide prone areas, (c) identification of the debris flow travel distance and area of deposition, and (d) mapping of the affected areas (landslides and debris flows). This work was developed in an area in which many mass movements occurred after intense rainfall during the summer season (February 1996) in the state of Rio de Janeiro, southeast Brazil. All of the scars produced by that event were mapped, allowing for validation of the applied models. The model results show that the mapped landslide locations can adequately be simulated by the model.