Manvinder Sharma

Bio: Manvinder Sharma is an academic researcher from Chandigarh Engineering College. The author has contributed to research in topics: Antenna (radio) & Computer science. The author has an hindex of 6, co-authored 25 publications receiving 182 citations. Previous affiliations of Manvinder Sharma include University of Exeter & Indian Institute of Technology Kharagpur.

Evaluation of texture methods for image analysis

Abstract: The evaluation of texture features is important for several image processing applications. Texture analysis forms the basis of object recognition and classification in several domains. There is a range of texture extraction methods and their performance evaluation is an important part of understanding the utility of feature extraction tools in image analysis. In this paper we evaluate five different feature extraction methods. These are autocorrelation, edge frequency, primitive-length., Law's method, and co-occurrence matrices. All these methods are used for texture analysis of Meastex database. This is a publicly available database and therefore a meaningful comparison between the various methods is useful to our understanding of texture algorithms. Our results show that the Law's method and co-occurrence matrix method yield the best results. The overall best results;are obtained when we use features from all five methods. Results are produced using leave-one-out method.

Radially Optimized Zone-Divided Energy-Aware Wireless Sensor Networks (WSN) Protocol Using BA (Bat Algorithm)

Abstract: The problems of hot spot and energy consumption of nodes in wireless sensor networks (WSNs) have been tackled by the adoption of hybrid energy efficient distributed (HEED) and unequal clustering algorithm to prolong the network lifetime (UCAPN) protocols. These have involved the implementation of unequally sized clusters that are based on the distance of cluster head (CH) from the base station (BS). The BS partitions the network area into several radially divided zones depending on the distance from centre of the field and CHs are independently selected in each zone. Clustering, on the other hand, controls how data are transmitted and allows for aggregation in the clusters. However, whereas these topologies have been able to improve network life time, the problem of network void still persists. To address this, a radially optimized zone-divided energy-aware WSN protocol using bat algorithm is proposed. This protocol considers not only the distance from the BS, but also the angle at which the WSN d...

Brain tumor detection using self-adaptive K-means clustering

Abstract: Brain tumor detection is an important diagnostic process in medical field. Magnetic resonance imaging (MRI) is the prime imaging technique while analysing the brain/skull with respect to brain tumor localization and detection. The brain MRI images show a complex network of brain cells along with bony structures and suspected solid growth if present. Therefore, in order to extract the growth, a segmentation process is required. In original K-means algorithm, the no. of clusters are define by the user i.e. user input is required. However, this limitation is overcome by using the self-adaptive K-means clustering algorithm to detect brain tumor accurately and in minimal execution time. A sobel edge detection technique is followed to extract the edges of the segmented brain tumor from its surroundings. In self-adaptive k-means clustering, the number of clusters are computed by computing the peaks in histogram. The segmented part is then processed to binary image format for its size and location estimation. The gray version is used to extract textural and color based features for nature of growth analysis. The final segmented part is applied the size estimation algorithm for tumour area and perimeter estimation.

Waveguide Diplexer: Design and Analysis for 5G Communication

Abstract: Diplexers circuits are recurrently used for mobile communications for 5G and satellite communication and used to separate the high power wide-band of transmission band from the error prone reception frequency range. Various telecommunication services like navigation system or television systems are based on satellite communication. for speed considerations, and in order to manage IoT devices diplexer devices are used. Diplexer is improvement over the microwave filters. This paper presents the analysis of simulated WR-28 waveguide that is used for Ka-Band. The cut off frequency in dominant mode is 30.4 GHz. The design is simulated over a frequency ranging from 20 to 50 GHz.

Minerva scene analysis benchmark

Abstract: The analysis of natural scenes is an important research area. Scene analysis research provides the foundation for the development of autonomous systems whose vision sensors provide important information about the surrounding environment. In this paper we introduce the Minerva scene analysis benchmark to the vision community and provide preliminary results on this data. The scene analysis benchmark contains 448 natural images in both colour and greyscale format. The images contain 8 natural objects including sky, brick, clouds, pebbles, road, trees, grass and leaves. The benchmark is intended to facilitate further research into scene analysis and to encourage the development of tools and techniques that work on natural object recognition. The results reported here have used four image segmentation techniques including fuzzy c-means clustering, histogram based thresholding, region growing, and split and merge. Following segmentation, texture features for object classification have been generated using five different texture analysis methods including autocorrelation, co-occurrence matrices, edge frequency, Law's, and run length. These results can be taken as a preliminary baseline on this benchmark.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Image Noise Level Estimation by Principal Component Analysis

Abstract: The problem of blind noise level estimation arises in many image processing applications, such as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation method on the basis of principal component analysis of image blocks. We show that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13 existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than other methods. Our method does not assume the existence of homogeneous areas in the input image and, hence, can successfully process images containing only textures.

Review of vision-based steel surface inspection systems

Abstract: Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

A texture approach to leukocyte recognition

Abstract: Millions of white blood cells are manually classified in laboratories using microscopes, a painstaking and subjective task. A trained medical technician takes about 15min to evaluate and count 100 cells for each blood slide, a time consuming and susceptible to error procedure. Leukocyte shape is usually insufficient to differentiate even among normal types since it varies widely. The current paper addresses the pattern recognition problem of blood image analysis and how textural information can improve differentiation among leukocytes. Cooccurrence probabilities can be used as a measure of gray scale image texture, a statistical method for characterizing the spatial organization of the gray-tones. We calculate five textural attributes based on gray level cooccurrence matrices (GLCM) as energy, entropy, inertia and local homogeneity, testing these features in leukocyte recognition. Several parameters must be estimated for obtaining GLCM, therefore we implement datamining algorithms for estimating suitable scales. Feature selection methods are also applied to define the most discriminative attributes for describing the cellular patterns. Experimental results show that texture parameters are essential to differentiate among the five types of normal leukocytes and chronic lymphocytic leukemia, evidencing the importance of biological aspects regarded by hematologists as nuclear chromatin and cytoplasmical granularity.