Badri Narayan Subudhi

Integration of Gibbs Markov Random Field and Hopfield-Type Neural Networks for Unsupervised Change Detection in Remotely Sensed Multitemporal Images

In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.

A Change Information Based Fast Algorithm for Video Object Detection and Tracking

In this paper, we present a novel algorithm for moving object detection and tracking. The proposed algorithm includes two schemes: one for spatio-temporal spatial segmentation and the other for temporal segmentation. A combination of these schemes is used to identify moving objects and to track them. A compound Markov random field (MRF) model is used as the prior image attribute model, which takes care of the spatial distribution of color, temporal color coherence and edge map in the temporal frames to obtain a spatio-temporal spatial segmentation. In this scheme, segmentation is considered as a pixel labeling problem and is solved using the maximum a posteriori probability (MAP) estimation technique. The MRF-MAP framework is computation intensive due to random initialization. To reduce this burden, we propose a change information based heuristic initialization technique. The scheme requires an initially segmented frame. For initial frame segmentation, compound MRF model is used to model attributes and MAP estimate is obtained by a hybrid algorithm [combination of both simulated annealing (SA) and iterative conditional mode (ICM)] that converges fast. For temporal segmentation, instead of using a gray level difference based change detection mask (CDM), we propose a CDM based on label difference of two frames. The proposed scheme resulted in less effect of silhouette. Further, a combination of both spatial and temporal segmentation process is used to detect the moving objects. Results of the proposed spatial segmentation approach are compared with those of JSEG method, and edgeless and edgebased approaches of segmentation. It is noticed that the proposed approach provides a better spatial segmentation compared to the other three methods.

Object Detection From Videos Captured by Moving Camera by Fuzzy Edge Incorporated Markov Random Field and Local Histogram Matching

In this paper, we put forward a novel region matching-based motion estimation scheme to detect objects with accurate boundaries from videos captured by moving camera. Here, a fuzzy edge incorporated Markov random field (MRF) model is considered for spatial segmentation. The algorithm is able to identify even the blurred boundaries of objects in a scene. Expectation Maximization algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough idea of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. We propose a χ2-test-based local histogram matching scheme for detecting moving objects from complex scenes from low illumination environment and objects that change size from one frame to another. The proposed scheme is successfully applied for detecting moving objects from video sequences captured in both real-life and controlled environments. It is also noticed that the proposed scheme provides better results with less object background misclassification as compared to existing techniques.

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.

dSPACE implementation of fuzzy logic based vector control of induction motor

Vector control of induction motor is an efficient approach to control the speed of induction motor used for industrial drives. Although the vector control is very popular method but there lies difficulty in obtaining an accurate model of induction motor (IM) owing to the variation of induction motor parameters, such as resistance, inductance and time constant. Therefore, to cope up with this uncertainty in the model we used a fuzzy logic based vector control approach to speed control of an induction motor. The focus of the paper is to analyze the real-time implementation issued for realizing a vector control in a laboratory set up of induction motor using dSPACE 1104. This fuzzy logic based vector control scheme has been successfully implemented on a 3 hp induction motor drive in the laboratory and the performances of this controller has been checked with a PID control strategy.

Entropy based region selection for moving object detection

This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences.

Parameter estimation techniques applied to power networks

This paper represents the estimation of frequency which is an important power system parameter by several variant of recursive techniques. Estimation of frequency is not new as found in the literature such as Discrete Fourier Transformation, Least Mean Square Technique etc. In this paper we have implemented Recursive least square (RLS) and Extended Least square (ELS) for estimation of frequency. These are well known algorithms for their simplicity in computations and good convergence properties. Such algorithms require three step calculations with estimation of amplitude and phase followed by frequency. They are simple and attractive with the implementation of covariance matrix. Again the choice of covariance matrix is very crucial without which there might be delay in convergence for estimation of power system parameters. At the same time, feasibility of the above algorithms is tested with signal buried with noise. The above work can be extended for real time implementation, which will be immensely helpful for Modern Power System engineers.

Edge Preserving Region Growing for Aerial Color Image Segmentation

Many image segmentation techniques are available in the literature. One of the most popular techniques is region growing. Research on region growing, however, has focused primarily on the design of feature extraction and on growing and merging criterion. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points and prone to over-segmentation. This paper presents a novel framework for avoiding anomalies like over-segmentation. In this article, we have proposed an edge preserving segmentation technique for segmenting aerial images. The approach implicates the preservation of edges prior to segmentation of images, thereby detecting even the feeble discontinuities. The proposed scheme is tested on two challenging aerial images. Its effectiveness is provided by comparing its results with those of the state-of-the-art techniques and the results are found to be better.

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures.

Moving object detection using spatio-temporal multilayer compound Markov Random Field and histogram thresholding based change detection

In this article, we propose a Multi Layer Compound Markov Random Field (MLCMRF) Model to spatially segment different image frames of a given video sequence. The segmented image frames are combined with the change between the frames to detect the moving objects from a video. The proposed MLCMRF uses five Markov models in a single framework, one in spatial direction using color feature, four in temporal direction (using two color features and two edges/line fields). Hence, the proposed MLCMRF is a combination of spatial distribution of color, temporal color coherence and edge maps in the temporal frames. The use of such an edge preserving model helps in enhancing the object boundary in spatial segmentation and hence can detect moving objects with less effect of silhouette. A difference between the frames is used to generate the CDM and is subsequently updated with the previous frame video object plane (VOP) and the spatial segmentation of the consecutive frames, to detect the moving objects from the target image frames. Results of the proposed spatial segmentation approach are compared with those of the existing state-of-the-art techniques and are found to be better.