Bardia Yousefi

Automated assessment and tracking of human body thermal variations using unsupervised clustering

The presented approach addresses a review of the overheating that occurs during radiological examinations, such as magnetic resonance imaging, and a series of thermal experiments to determine a thermally suitable fabric material that should be used for radiological gowns. Moreover, an automatic system for detecting and tracking of the thermal fluctuation is presented. It applies hue-saturated-value-based kernelled k-means clustering, which initializes and controls the points that lie on the region-of-interest (ROI) boundary. Afterward, a particle filter tracks the targeted ROI during the video sequence independently of previous locations of overheating spots. The proposed approach was tested during experiments and under conditions very similar to those used during real radiology exams. Six subjects have voluntarily participated in these experiments. To simulate the hot spots occurring during radiology, a controllable heat source was utilized near the subjects body. The results indicate promising accuracy for the proposed approach to track hot spots. Some approximations were used regarding the transmittance of the atmosphere, and emissivity of the fabric could be neglected because of the independence of the proposed approach for these parameters. The approach can track the heating spots continuously and correctly, even for moving subjects, and provides considerable robustness against motion artifact, which occurs during most medical radiology procedures.

Biological inspired human action recognition

Computational neuroscience studies through Functional magnetic resonance imaging (fMRI) claimed that human action recognition in the brain of mammalian pursues two separated pathways in the model, which are specialized for the analysis of motion (optic flow) and form information[3]. For analysis of the form information active basis model is used by different scales and orientations of Gabor wavelets to form a dictionary regarding object recognition (human). In motion pathway, biological movements are recognized by analyzing optic-flow patterns and entering motion information for form pathway adjustment. A synergetic neural network is utilized to generate prototype templates, representing general characteristic of every class. By having predefined templates, classifying performs based on multi-template matching. We successfully apply proposed model on the human action video obtained from KTH human action database as largest human action database. The attained results using proposed methods were promising.

Classification of remote sensing images from urban areas using Laplacian image and Bayesian theory

This paper presents the methodology of urban area classification in high resolution satellite IKONOS imagery. The strategies include building extraction by Bayesian theory and laplacian criterion, labeling and size filtering, intensity threshold and etc which are applied to IKONOS image in tandem to make this algorithm as an effective strategy to save processing time and improve robustness. To realize the strategy, First, vegetation are extracted in attend to green layer of RGB image then buildings are detected by Bayesian decision theory in regard to laplacian probability density function, then shadows which have low intensity are detected. In the next step a special intensity level is calculated as a threshold level to discern roads. Finally open areas are extracted from remained of image as regions with low laplacian intensity and large size. Meanwhile morphological operations are applied to remove redundant images particles. Experimental result indicates that this approach has a high efficiency especially in extraction of large roads and streets from dense urban area IKNOS images.

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility.

Hierarchical segmentation of urban satellite imagery

Abstract This paper proposes a method to combine contextual, structural, and spectral information for classification. The method is an integrated method for automatically classifying urban-area objects in very high-resolution satellite imagery. The approach addresses three aspects. First, the Gabor wavelet is applied to the image along with morphological operations, with the sparsity of the outcome considered. A Bayesian classifier then categorizes the different classes, such as buildings, roads, open areas, and shadows. There are some false positives (wrong classification), and false negatives (non-classification) in the initial results. These results can be corrected by the relaxation labeling categorization of the unknown regions. The novelty of the proposed approach lies in the extensive use of spatiotemporal features considering the sparsity of urban objects. The results indicate improvement in classification through relaxation labeling compared with existing methods.

Hierarchical method for building extraction in urban area’s images using unsharp masking [USM] and Bayesian classifier

Recently, due to the availability of high resolution IKONOS image, classification of remote sensing images from urban area become one of the most attractive topics for scientific researches and papers. In this paper, we address a method for classification of remote sensing (IKONOS) image and especially for the extraction of buildings. First step is applying the unsharp masking [USM] which intensify high frequency components of the original image. Then imagepsilas Laplacian, Bayesian classifier and size filter is used for building discrimination. The accuracy of small and large building classification using unsharp mask filter and Bayesian discrimination function is increased compared with the original qualitative model for Bayesian classification. Experiments indicate promising results about the efficiency of the proposed approach.

Slow feature action prototypes effect assessment in mechanism for recognition of biological movement ventral stream

In analysis of the brain and visual system functionality, scientific evidence points to two independent processing pathways in recognising biological movement, i.e. dorsal and ventral streams. Motion information generated in the dorsal processing stream is presented as fuzzy optical flow division while ventral processing stream with information of the object form is implemented as an active basis model. The recognition task however still requires decision-making and mutual interaction between these pathways. This process is done using slow features as action prototypes dictionary of biological movements. For motion information interaction, dorsal pathway guides the shared sketch algorithm that leads to decision-making for a more accurate outcome. Extreme learning machine classifier is used for decision-making unit kernel. The proposed approach is tested on the KTH human action database videos. Good performances are indicated compared to existing methods, with good interaction between dorsal and ventral processing streams.

Fuzzy based foreground background discrimination for probabilistic color based object tracking

In the most of object tracking tasks dealing with partial occlusion is a challenging issue. Recently the use of color cue based on Monte Carlo tracking method and particle filtering is mostly considered to overcome the problem of partial occlusion and nonrigid motion. The proposed approach in this paper is based on using of sequential Monte Carlo and particle filtering for tracking. But in this method a special fuzzy based color model for object is employed. Then comparison of mean value of reference and candidate window in the proposed color space is utilized for tracking of objects. Some of the morphological operation is also used to provide a unit region for object location in the fuzzy based color space. Experimental results indicate that the algorithm is efficient in dealing with partial occlusion.

Thermal NDT applying Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT)

Thermal and infrared imagery creates considerable developments in Non-destructive Testing (NDT) area. An analysis for thermal NDT inspection is addressed applying a new technique for computation of eigen-decomposition (factor analysis) similar to Principal Component Thermography(PCT). It is referred as Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT). The proposed approach uses a computational short-cut to estimate covariance matrix and Singular Value Decomposition(SVD) to obtain faster PCT results, but while the dimension of the data increases. The problem of computational cost for high-dimensional thermal image acquisition is also investigated. Three types of specimens (CFRP, plexiglass and aluminum) have been used for comparative benchmarking. Then, a clustering algorithm segments the defect at the surface of the specimens. The results conclusively indicate the promising performance and demonstrated a confirmation for the outlined properties.

Emissivity retrieval from indoor hyperspectral imaging of mineral grains

The proposed approach addresses the problem of retrieving the emissivity of hyperspectral data in the spectroscopic imageries from indoor experiments. This methodology was tested on experimental data that have been recorded with hyperspectral images working in visible/near infrared and long-wave infrared bands. The proposed technique provides a framework for computing down-welling spectral radiance applying non-negative matrix factorization (NMF) analysis. It provides the necessary means for the non-uniform correction of active thermographical experiments. The obtained results indicate promising accuracy. In addition, the application of the proposed technique is not limited to non-uniform heating spectroscopy but to uniform spectroscopy as well.