Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Research in interactive scene analysis

Cooperative (man-machine) scene analysis techniques were developed whereby humans can provide a computer with guidance when completely automated processing is infeasible. An interactive approach promises significant near-term payoffs in analyzing various types of high volume satellite imagery, as well as vehicle-based imagery used in robot planetary exploration. This report summarizes the work accomplished over the duration of the project and describes in detail three major accomplishments: (1) the interactive design of texture classifiers; (2) a new approach for integrating the segmentation and interpretation phases of scene analysis; and (3) the application of interactive scene analysis techniques to cartography

Traffic sign recognition based on human visual perception.

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions

Using adaptive thresholding and skewness correction to detect gray areas in melanoma \u3ci\u3ein situ\u3c/i\u3e images

The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms --Abstract, page iv

Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas

Background: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results. Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points. Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases. Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid.Yamaguchi S., Satake K., Yamaji Y., et al. Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of locoregional histogram and probabilistic atlas. Computers in Biology and Medicine 55, 79 (2014); https://doi.org/10.1016/j.compbiomed.2014.10.003

AN OVERVIEW OF IMAGE SEGMENTATION ALGORITHMS

Image segmentation is a puzzled problem even after four decades of research. Research on image segmentation is currently conducted in three levels. Development of image segmentation methods, evaluation of segmentation algorithms and performance and study of these evaluation methods. Hundreds of techniques have been proposed for segmentation of natural images, noisy images, medical images etc. Currently most of the researchers are evaluating the segmentation algorithms using ground truth evaluation of (Berkeley segmentation database) BSD images. In this paper an overview of various segmentation algorithms is discussed. The discussion is mainly based on the soft computing approaches used for segmentation of images without noise and noisy images and the parameters used for evaluating these algorithms. Some of these techniques used are Markov Random Field (MRF) model, Neural Network, Clustering, Particle Swarm optimization, Fuzzy Logic approach and different combinations of these soft techniques

Mo.Se.: Segmentación de mosaico de imágenes basado en aprendizaje profundo en cascada

[EN] Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.Highlights:A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.[ES] El mosaico es un tipo de arte antiguo utilizado para crear imágenes decorativas o patrones de pequeños componentes. Una versión digital de un mosaico puede ser útil a los arqueólogos, estudiosos y restauradores que están interesados en el estudio, la comparación y la preservación de los mosaicos. Hoy en día, los arqueólogos basan sus estudios principalmente en la operación manual y la observación visual que, aunque sigue siendo fundamental, debe ser apoyada con la ayuda de un procedimiento automatizado de extracción de la información. En este contexto, esta investigación tiene la intención de superar el procedimiento manual y lento del dibujo de teselas en mosaico proponiendo Mo.Se. (Mosaic Segmentation), un algoritmo que explota técnicas de aprendizaje profundo y segmentación de imagen; específicamente, la metodología combina la red U-Net 3 con el algoritmo Watershed. El propósito final es definir un flujo de trabajo que establezca los pasos para realizar una segmentación robusta y obtener una representación digital (vectorial) de un mosaico. Se presenta el procedimiento detallado y se proporcionan justificaciones teóricas, construyendo varias conexiones con otros modelos, haciendo que el flujo de trabajo sea teóricamente valioso y prácticamente escalable en conjuntos de datos medianos o grandes. El proceso de segmentación automática se probó con la ortoimagen de alta resolución de un mosaico antiguo, siguiendo un procedimiento de fotogrametría de objeto cercano. Nuestro enfoque se ha probado en el pavimento de la Iglesia de San Esteban en Umm ar-Rasas, un sitio arqueológico de Jordania, ubicado a 30 km al sureste de la ciudad de Madaba (Jordania). Los resultados experimentales muestran que este marco generalizado produce buenos rendimientos, obteniendo una mayor precisión en comparación con otros enfoques de vanguardia. Mo.Se. se ha validado utilizando conjuntos de datos disponibles públicamente como punto de referencia, lo que demuestra que la combinación de métodos basadosen el aprendizaje con métodos procedimentales mejora el rendimiento de la segmentación en casi un 10% en términos de exactitud en general. El ambicioso objetivo de este estudio es proporcionar a los arqueólogos una herramienta que acelere su trabajo de extracción automática de mosaicos geométricos antiguos.This work was partially found within the framework of the project Innovative technologies and training activities for the conservation and enhancement of the archaeological site of Umm er-Rasas (Jordan) funded by Ministero degli Affari Esteri e della Cooperazione Internazionale. The authors would like to express their gratitude to the ISPC CNR and in particular to Dott. Roberto Gabrielli (project leader) and Alessandra Albiero for providing the dataset.Felicetti, A.; Paolanti, M.; Zingaretti, P.; Pierdicca, R.; Malinverni, ES. 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