Mohammad Karimi

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.

Simulating urban growth under planning policies through parcel-based cellular automata ParCA model

ABSTRACT In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios.

Modeling land use interaction using linguistic variables

Abstract One of the main factors of land use change (LUC) modeling is the land use intersection (LUI) or neighborhood effect, which is normally modeled using cellular automata (CA) concept. The effects of LUI over distance are represented in terms of CA transition rules. In this paper, a new model for LUI process is developed that makes use of expert knowledge to define the transition rules. In this model, the region of influence is defined using a new radial structure; the transition rules are described by expert knowledge and spatial metrics in the form of linguistic variables; and finally, the neighborhood effect is classified into three groups of compactness, dependency and incompatibility. The model is implemented and evaluated using the data of Borkhar and Meymeh township, in Esfahan, Iran, for the two periods of 1986–1998 and 1998–2005. The results show that the model and its related concept are performing rather well.

Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)

Abstract This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.

Developing a methodology for modelling land use change in space and time

Abstract The complex, dynamic and non-linear characteristics of land use change (LUC) modelling necessitate the development of advanced models and methods. In this research, a novel and stepwise approach is developed for modelling integrated LUC at the regional level based on land suitability, land demand and land use conversion rules. The aim of this model is to use different models (i.e. geographic information systems, remote sensing, fuzzy logic, cellular automata and multi-criteria decision analysis) in a logical way to improve model development, model evaluation and prediction of LUC. The proposed model was implemented in ‘Borkhar and Meymeh’ township, Iran, for two periods (1986–1998 and 1998–2005). Results for producer accuracy, user accuracy, overall accuracy and figure of merit were 0.345, 0.357, 0.351, 0.234 respectively in the first period (1986–1998) and 0.302, 0.287, 0.0.294, 0.202 respectively in the second period (1998–2005). Our results suggest that the developed model is accurate, when compared with the results of existing models.

An integrated framework for linear pattern extraction in the building group generalization process

Abstract Building pattern extraction is an essential step in building generalization. Although many studies have already been conducted, there is a lack of a framework for extracting building patterns. To overcome this problem, an integrated framework for extracting building linear patterns is presented. First, an aggregation function is presented based on the TOPSIS method, which determines the similarity index in terms of area, shape, rectangularity and distance similarities. This results in the extraction of straight and perpendicular patterns using the similarity index and orientation difference criteria. Second, a refinement strategy is proposed, which refines the extracted patterns using a novel definition of the pattern interaction index. To evaluate the proposed model, the complete building group generalization process is implemented using a data-set at 1:25 k scale. The evaluation results allowed us to conclude that the proposed model produces meaningful results, and therefore it would be beneficial in the generalization process.

A Local Adaptive Density Based Algorithm for Clustering Polygonal Buildings in Urban Block Polygons

Abstract Building clustering is an important task that should be performed prior to building generalization operations. One of the most common approaches for building clustering is the use of density-based algorithms. Current density-based algorithms encounter problems in detecting accurate clusters in a region with varying density. To overcome this problem, a new density-based spatial clustering algorithm, local-adaptive DBSCAN (LA-DBSCAN), which can cluster polygonal buildings in urban blocks with noise and non-uniform density, is developed. The advantage of LA-DBSCAN is that it can select parameters that are adaptive to different local situations. To evaluate the performance of the proposed model, the complete building generalization process is implemented using four datasets at 1:25k scale. An evaluation of the results allowed us to conclude that the LA-DBSCAN algorithm yields more homogeneous and accurate results than the DBSCAN algorithm. Thus, the presented approach is beneficial for the detection of building patterns and the generalization.

Simulation of Skin Cancer Spatial Dispersion Using Geo-Statistical Analyses

The occurrence of skin cancer is related to many parameters which are spatially distributed. GIS can be used for analysing such spatial relationships. On the other hand, the occurrence of skin cancer is a probabilistic phenomenon. In other words, even in presence of all causing factors, skin cancer does not necessarily occur. In this research, a geostatistical model is developed to simulate the spatial distribution of skin cancer. First, the data framework is designed and proper data are gathered. Then, the data are processed in GIS and prepared to enter the model. Finally, by creation of the geostatistical model, the relations between the disease and its factors are studied. As a result, the effecting factors and their importance degrees are extracted. The developed model can be used both as simulator tool and as a decision support tool. In the other words, by changing the values of one or more parameters, the probability of occurring changes in the occurrence of disease can be studied. This can help the managers in evaluating the result of any possible course of action, when challenging the disease.