Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

Characterization of surface morphology and its correlation with friction performance of brake pads

The present work presents the morphology evolution of a brake material surface submitted to braking tests through a laboratory-scale tribometer. Optical microscope images of the material’s surface were obtained for every 10 braking operations. These images were post-processed in appropriate computational software. By means of the image segmentation technique, morphological parameters related to the brake material surface were estimated. The wear rate and also the coefficient of friction resulting from the tests were measured. For the NAO material used in this study, the friction behaviour revealed to be strongly associated with the amount of contact plateaus. Besides, the mean area of the contact plateaus was the main factor responsible for increasing the real contact area of the friction material. The higher wear rate observed in the first braking operations can be mainly attributed to the higher surface roughness measured in this condition. As the braking operations progress, the plateaus becomes flatter and wear rate is reduced. Finally, the image segmentation technique proved adequate for investigating morphological aspects in friction material surface

A novel palmprint segmentation technique

Recent paradigm shift from the conventional contact based palmprint recognition to contactless based systems (CBS) has necessitated the development of a variety of these systems. A major challenge of these systems is it robustness to illumination variation in unconstrained environment, thus making segmentation difficult. In this paper, the acquired image undergoes color space conversion and the output is filtered using coefficients obtained from the training of an artificial neural network (ANN) based model coefficient determination technique. Performance analysis of the proposed technique shows better performance in term of mean square error, true positive rate and accuracy when compared with two other techniques. Furthermore, it has also been observed that the proposed method is illumination invariant hence its suitability for deployment in contactless palmprint recognition systems

Phonemic Segmentation and Labelling using the MAUS Technique

We describe the pronunciation model of the automatic segmentation technique MAUS based on a data-driven Markov process and a new evaluation measure for phonemic transcripts relative symmetric accuracy; results are given for the MAUS segmentation and labelling on German dialog speech. MAUS is currently distributed as a freeware package by the Bavarian Archive for Speech Signals and will also be implemented as a web-service in the near future

Analysis of fuzzy clustering and a generic fuzzy rule-based image segmentation technique

Many fuzzy clustering based techniques when applied to image segmentation do not incorporate spatial relationships of the pixels, while fuzzy rule-based image segmentation techniques are generally application dependent. Also for most of these techniques, the structure of the membership functions is predefined and parameters have to either automatically or manually derived. This paper addresses some of these issues by introducing a new generic fuzzy rule based image segmentation (GFRIS) technique, which is both application independent and can incorporate the spatial relationships of the pixels as well. A qualitative comparison is presented between the segmentation results obtained using this method and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms using an empirical discrepancy method. The results demonstrate this approach exhibits significant improvements over these popular fuzzy clustering algorithms for a wide range of differing image types

Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs

This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98