Tejmani Sinam

Bio: Tejmani Sinam is an academic researcher. The author has contributed to research in topics: Thresholding & Balanced histogram thresholding. The author has an hindex of 1, co-authored 1 publications receiving 176 citations.

A New Local Adaptive Thresholding Technique in Binarization

Abstract: Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground Thresholding plays a major in binarization of images Thresholding can be categorized into global thresholding and local thresholding In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background In such cases, binarization with local thresholding is more appropriate This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation Normally the local mean computational time depends on the window size Our technique uses integral sum image as a prior processing to calculate local mean It does not involve calculations of standard deviations as in other local adaptive techniques This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques

Server Load Balancing with Round Robin Technique in SDN

Abstract: Round Robin Algorithm plays a crucial role in load balancing of server farms. This paper’s primary focus is to benchmark the Round Robin algorithm in Server Load Balancing (SLB) using Software Defined Networking (SDN). Experimental results show that the Round Robin algorithm gives 100% availability of the servers. Load balancing is done among the live servers from the server pool. In our experiment, the Round Robin load-balancing algorithm is implemented using a POX controller and an OpenFlow switch.

A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery

Abstract: Emergency response to floods requires timely information on water extents, which can be produced by satellite-based remote sensing. As the synthetic aperture radar (SAR) can emit and receive signal in nighttime or cloudy conditions, it is particularly suitable to delineate water extent during flood events. Thresholding SAR imagery is one of the most widely used approaches to delineate water extent for its effectiveness and efficiency. However, most thresholding methods rely on a single threshold to separate water and land without considering the complexity and variability of different land surface types in an image. To account for the heterogeneous surface characteristics, this paper proposes a new local thresholding method to water delineation with SAR images. Specifically, our method follows four major steps. First, a global threshold is applied to the SAR imagery to delineate initial water pixels, from which non-water pixels are further clustered into several land surface types. This divides the SAR imagery into one water cluster and several land clusters. Second, local thresholds are estimated at each subset of land cluster paired with water cluster by fitting Gamma distributions to the backscatter intensities of the combined water/land pixels in each subset. Third, local water extents are delineated from each subset and then merged as the union of all subsets. The results are combined across multiple polarizations by taking an intersection operation to generate the global inundation extent. Finally, the flood water extent is further improved by imposing basic hydrologic constraints. This approach is fast and fully automated for flood detection. Our experiments using Sentinel-1 SAR imagery show that the proposed local thresholding approach could distinguish water from non-water with significantly higher accuracy (4–13% improvement in the harmonic mean of user’s and producer’s accuracy of water) than conventional global-thresholding methods.

Adaptive Thresholding: A comparative study

Abstract: — With the growth of image processing applications, image segmentation has become an important part of image processing. The simplest method to segment an image is thresholding. Using the thresholding method, segmentation of an image is done by fixing all pixels whose intensity values are more than the threshold to a foreground value. The remaining pixels are set to a background value. Such technique can be used to obtain binary images from grayscale images. The conventional thresholding techniques use As previously noted, recently a number of worksa global threshold for all pixels, whereas done adaptive thresholding changes the threshold value dynamically over the image. This paper offers a comparative study on adaptive thresholding techniques to choose the accurate method for binarizing an image based on the contrast, texture, resolution etc. of an image. Keywords —Threshold, Otsu’s Method, Kapur’s threshold, Rosin’s threshold, Entropy based thresholding,

Degraded Historical Document Binarization: A Review on Issues, Challenges, Techniques, and Future Directions.

Abstract: In this era of digitization, most hardcopy documents are being transformed into digital formats. In the process of transformation, large quantities of documents are stored and preserved through electronic scanning. These documents are available from various sources such as ancient documentation, old legal records, medical reports, music scores, palm leaf, and reports on security-related issues. In particular, ancient and historical documents are hard to read due to their degradation in terms of low contrast and existence of corrupted artefacts. In recent times, degraded document binarization has been studied widely and several approaches were developed to deal with issues and challenges in document binarization. In this paper, a comprehensive review is conducted on the issues and challenges faced during the image binarization process, followed by insights on various methods used for image binarization. This paper also discusses the advanced methods used for the enhancement of degraded documents that improves the quality of documents during the binarization process. Further discussions are made on the effectiveness and robustness of existing methods, and there is still a scope to develop a hybrid approach that can deal with degraded document binarization more effectively.