An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.     

Unsupervised MRI segmentation of brain tissues using a local linear model and level set

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results.     

Accelerating image registration of MRI by GPU-based parallel computation

Automatic image registration for MRI applications generally requires many iteration loops and is, therefore, a time-consuming task. This drawback prolongs data analysis and delays the workflow of clinical routines. Recent advances in the massively parallel computation of graphic processing units (GPUs) may be a solution to this problem. This study proposes a method to accelerate registration calculations, especially for the popular statistical parametric mapping (SPM) system. This study reimplemented the image registration of SPM system to achieve an approximately 14-fold increase in speed in registering single-modality intrasubject data sets. The proposed program is fully compatible with SPM, allowing the user to simply replace the original image registration library of SPM to gain the benefit of the computation power provided by commodity graphic processors. In conclusion, the GPU computation method is a practical way to accelerate automatic image registration. This technology promises a broader scope of application in the field of image registration.     

Image encryption based on synchronization of fractional chaotic systems

This paper deals with a synchronization scheme for two fractional chaotic systems which is applied in image encryption. Based on Pecora and Carroll (PC) synchronization, fractional-order Lorenz-like system forms a master–slave configuration, and the sufficient conditions are derived to realize synchronization between these two systems via the Laplace transformation theory. An image encryption algorithm is introduced where the original image is encoded by a nonlinear function of a fractional chaotic state. Simulation results show that the original image is well masked in the cipher texts and recovered successfully through chaotic signals. Further, the cryptanalysis is conducted in detail through histogram, information entropy, key space and sensitivity to verify the high security.     

Face and eyes localization algorithm in thermal images for temperature measurement of the inner canthus of the eyes

In this paper, a novel algorithm for the detection and localization of the face and eyes in thermal images is presented, particularly the temperature measurement of the human body by measuring the eye corner (inner canthus) temperature. The algorithm uses a combination of the template-matching, knowledge-based and morphological methods, particularly the modified Randomized Hough Transform (RHT) in the localization process, also growing segmentation to increase accuracy of the localization algorithm. In many solutions, the localization of the face and/or eyes is made by manual selection of the regions of the face and eyes and then the average temperature in the region is measured. The paper also discusses experimental studies and the results, which allowed the evaluation of the effectiveness of the developed algorithm. The standardization of measurement, necessary for proper temperature measurement with the use of infrared thermal imaging, are also presented.     

Optical diffraction tomography microscopy with transport of intensity equation using a light-emitting diode array

Optical diffraction tomography (ODT) is an effective label-free technique for quantitatively refractive index imaging, which enables long-term monitoring of the internal three-dimensional (3D) structures and molecular composition of biological cells with minimal perturbation. However, existing optical tomographic methods generally rely on interferometric configuration for phase measurement and sophisticated mechanical systems for sample rotation or beam scanning. Thereby, the measurement is suspect to phase error coming from the coherent speckle, environmental vibrations, and mechanical error during data acquisition process. To overcome these limitations, we present a new ODT technique based on non-interferometric phase retrieval and programmable illumination emitting from a light-emitting diode (LED) array. The experimental system is built based on a traditional bright field microscope, with the light source replaced by a programmable LED array, which provides angle-variable quasi-monochromatic illumination with an angular coverage of ±37 degrees in both x and y directions (corresponding to an illumination numerical aperture of ∼0.6). Transport of intensity equation (TIE) is utilized to recover the phase at different illumination angles, and the refractive index distribution is reconstructed based on the ODT framework under first Rytov approximation. The missing-cone problem in ODT is addressed by using the iterative non-negative constraint algorithm, and the misalignment of the LED array is further numerically corrected to improve the accuracy of refractive index quantification. Experiments on polystyrene beads and thick biological specimens show that the proposed approach allows accurate refractive index reconstruction while greatly reduced the system complexity and environmental sensitivity compared to conventional interferometric ODT approaches.     

Segment-based region of interest generation for pedestrian detection in far-infrared images

We present a region of interest (ROI) generation method specialized for nighttime pedestrian detection using far-infrared (FIR) images. Because pedestrians typically appear brighter than background in FIR images, previous research efforts primarily attempted to extract ROIs based on the intensity threshold. However this approach has problems resulting from the intensity variances of pedestrians due to their clothing and, especially in urban scenarios, and other heat sources that emit more heat than the pedestrians. In this paper, we propose a novel ROI generation method that is based on combining image segments instead of using the intensity threshold. In order to minimize dependence on brightness, we utilize the low-frequency characteristics of FIR images. As a result, our proposed method generates a small number of ROIs at an acceptable miss rate and the generated ROIs provide advantages for classification because the pedestrians are satisfactorily arranged within a bounding box. Experiments conducted indicate that our proposed method performs reliably in urban scenarios.     

Raman spectroscopic investigation of film thickness

The determination of film thickness is of prime importance in the quality assurance of coated pharmaceutical preparations. The rapid measurement of this parameter is problematic for multi-particulate pellet systems. The aim of this study was to apply the Raman spectroscopic method for the determination of the thickness of polymer coating on pellets. The change of Raman intensity was compared with measured film thickness, which was calculated from the change of the geometric parameters of the pellets, measured with an image-analyzing system. The results revealed that despite some difficulties Raman spectroscopy is a suitable method for the fast and accurate determination of film thickness on multi-particulate systems.     

Real-Time Generation of Dynamic Infrared Scene

Although many models have been put forward to realize static infrared scene, they could not generate dynamic infrared scene real time in interactive way. In this paper a new method is proposed to solve the problem. We first model the targets and background of infrared scene based on the hybrid way of geometry and multi-spectral texture images. Then considering the attenuation effect of atmosphere and the noise mechanic of infrared image sensor, we present an infrared depth image model to generate dynamic images of the objects in the scene from different viewpoint. The complexity of infrared dynamic scene is thus reduced greatly and the reality of infrared scene is improved. Finally, real-time walkthrough for infrared scene is successfully realized and the average walkthrough speed is larger than 25 frames per second.