Image Acquisition

Fast imaging methods and the availability of required hardware for magnetic resonance tomography (MRT) have significantly reduced acquisition times from about an hour down to several minutes or seconds. With this development over the last 20 years, magnetic resonance imaging (MRI) has become one of the most important instruments in clinical diagnosis. In recent years, the greatest progress in further increasing imaging speed has been the development of parallel MRI (pMRI). Within the last 3 years, parallel imaging methods have become commercially available, and therefore are now available for a broad clinical use. The basic feature of pMRI is a scan time reduction, applicable to nearly any available MRI method, while maintaining the contrast behavior without requiring higher gradient system performance. Because of its faster image acquisition, pMRI can in some cases even significantly improve image quality. In the last 10 years of pMRI development, several different pMRI reconstruct...

Abstract: Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The commonest approaches use the so-called Content-Based Image Retrieval (CBIR) systems. Basically, these systems try to retrieve images similar to a user-defined specification or pattern (eg, shape sketch, image example). Their goal is to support image retrieval based on content properties (eg, ...

9
th International Conference on Signal and Image Processing (Signal 2022) is a forum for
presenting new advances and research results in the fields of Digital Image Processing. The
conference will bring together leading researchers, engineers and scientists in the domain of
interest from around the world. The scope of the conference covers all theoretical and practical
aspects of the Signal, Image Processing & Pattern Recognition.

Digital cameras are convenient image acquisition devices: they are fast, versatile, mobile, do not touch the object, and are relatively cheap. In OCR applications, however, digital cameras suffer from a number of limitations, like geometrical distortions. In this paper, we deal with the preprocessing step before text recognition, specifically with images from a digital camera. Experiments, performed with the FineReader 7.0 software as the back-end recognition tool, confirm importance of image preprocessing in OCR applications.

Vesicles in volcanic rocks are frozen records of degassing processes in magmas. For this reason, their sizes, spatial arrangements, numbers and shapes can be linked to physical processes that drive magma ascent and eruption. Although numerous techniques have been derived to describe vesicle textures, there is no standard approach for collecting, analyzing, and interpreting vesicular samples. Here we describe a methodology for techniques that encompass the entire data acquisition process, from sample collection to quantitative analysis of vesicle size and number. Carefully chosen samples from the lower, mean and higher density/vesicularity endmembers are characterized using image nesting strategies. We show that the texture of even microvesicular samples can be fully described using less than 20 images acquired at several magnifications to cover efficiently the range of existing vesicle sizes. A new program (FOAMS) was designed to perform the quantification stage, from vesicle measurement to distribution plots. Altogether, this approach allows substantial reduction of image acquisition and processing time, while preserving enough user control to ensure the validity of obtained results. We present three cameo investigations — on basaltic lava flows, scoria deposits and pumice layers — to show that this methodology can be used to quantify a wide range of vesicle textures, which preserve information on a wide range of eruptive conditions.

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.

Plant disease detection system has been a necessity for cultivators as detection of a correct disease might lead to a proper yield and effective growth of the plant. The proposed plant disease detection methodology aims at identifying tomato plant diseases from digital images of affected plant leaves. This proposed technique will help farmers or any tomato cultivators to detect plant diseases digitally as the same process manually might lead to incorrect detection. In the proposed project, various image processing steps are performed on the leaf images along with classification methods such as K-Nearest Neighbor Algorithm and Naïve Bayes algorithm to distinguish between different plant diseases.

The state-of-the-art diagnostic tools in oral and maxillofacial surgery and preoperative orthodontic treatment are mainly two-dimensional, and consequently reveal limitations in describing the three-dimensional (3D) structures of a patient's face. New 3D imaging techniques, such as 3D stereophotogrammetry (3D photograph) and cone-beam computed tomography (CBCT), have been introduced. Image fusion, i.e. registration of a 3D photograph upon a CBCT, results in an accurate and photorealistic digital 3D data set of a patient's face. The purpose of this study was to determine the accuracy of three different matching procedures. For 15 individuals the textured skin surface (3D photograph) and untextured skin surface (CBCT) were matched by two observers using three different methods to determine the accuracy of registration. The registration error was computed as the difference (mm) between all points of both surfaces. The registration errors were relatively large at the lateral nec...

In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM have been compared. This comparison was conducted to seek a robust method that would produce good classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG) signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder with SVM has been proposed. The EEG dataset used in this research was created by the University of Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature engineering. However, our prosed method of autoencoder in combination with SVM produced a similar accuracy of 65% without using any feature engineering technique. This research shows that this system of classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.