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Crack is a condition indicator of the pavement’s structure. Generally, crack detection is an essential task for effective diagnosis of the road network. Moreover, evaluation of road quality is necessary to ensure traffic security. Since 2011, a periodic survey of approximately 57,500 km of Moroccan roads has been performed using an inspection vehicle (SMAC) which is equipped with high resolution cameras and GPS/DGPS receivers. Until recently, the teams of the National Center for Road Studies and Research (CNER) analyzed road surface states by visualization of pavement surface image sequences captured by the Multifunctional Pavement Assessment System (SMAC) in order to detect defects in road surfaces and classify them according to their type. However, this method involves manual processing and is complex, time consuming and subjective. In this paper, we propose an automated methodology for crack detection and classification in Moroccan flexible pavements using Convolutional Neural Networks (CNN). Transfer learning is also applied by testing a pre-trained Visual Geometry Group 19 (VGG-19) model. For the dataset used in this paper, the results indicate that good crack detection and classification are achieved using both models.

In this paper we present an unsupervised segmentation-free method for spotting and searching query, especially, for images documents in handwritten Arabic, for this, Histograms of Oriented Gradients (HOGs) are used as the feature vectors to represent the query and documents image. Then, we compress the descriptors with the product quantization method. Finally, a better representation of the query is obtained by using the Support Vector Machines (SVM).

Background: Handwriting is a component of the complex language that came about late in the history of mankind and which develops late in human beings. Numerous works have raised changes in both the graphic and kinematic characteristics of writing. Although, age does not modify the lexical and syntactic parameters of language, it can however modify its spatial structure, especially pressure and speed. Many neurodegenerative pathologies, especially Alzheimer's disease, are characterized by a progressive disorganization of writing. Depending on the cognitive stage of the dementia, the graphic gesture deteriorates as does the spatial construction. Our study aims at assessing the characteristics of Arabic writing in a healthy Moroccan population and to compare it to people with mild to moderate Alzheimer's disease. Our objective is to help health professionals detect early cognitive deterioration in neurodegenerative diseases by analyzing the graphic gesture. Material/Methods: Handwriting is captured on a graphic tablet (WACOM) and is analyzed "online" as a sequence of acquired signals (position, pressure, speed and pen inclination) in Moroccan patients with mild to moderate Alzheimer's disease and these were compared to those of normal volunteers. Results: We performed a first analysis of the results from 18 Alzheimer's patients compared to 18 control subjects. The results reveal differences between the control and Alzheimer's groups. AD subjects had lower speeds and accelerations compared to the control subjects. The time spent on paper and in the air was significantly greater in the AD subjects. Conclusions: This preliminary analysis of the results allowed us to identify distinguishing characteristics through the analysis of different handwriting parameters in order to identify the two groups studied. [ABSTRACT FROM AUTHOR]