O. Imocha Singh

Bio: O. Imocha Singh is an academic researcher. The author has contributed to research in topics: Thresholding & Pixel. The author has an hindex of 3, co-authored 5 publications receiving 212 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

Local Contrast and Mean based Thresholding Technique in Image Binarization

Abstract: is a process of separation of pixel values of an input image into two pixel values like white as background and black as foreground. It is an important part in image processing and it is the first step in many document analysis and OCR processes. Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned input grayscale image into two classes like background and foreground. Each and every pixel should be compared with the threshold and transformed to its respective class according to the threshold value. Thus threshold takes a major role in binarization. Hence determination of proper threshold value in binarization is a major factor of being a good binarised image and it can be approached in two categories like global thresholding and local thresholding techniques. In uniform contrast distribution of background and foreground documents, global thesholding is more suitable than that of local thresholding one. In degraded documents, where considerable background noise or variation in contrast and illumination exists, local technique is more suitable than that of global one. In this paper a local thresholding technique using local contrast and mean is described. Local adaptation is carried out with the local contrast and mean.

Image Enhancement by Adaptive Power-Law Transformations

Abstract: Normally the quality of an image is improved by enhancing contrast and sharpness. The enhancement of contrast and sharpening of an image with a single function is a complex task. In real-time imaging, many complex scenes require local contrast improvements that should bring details to the best possible visibility of the image. However, local enhancement methods mainly suffer from ringing artefacts and noise over-enhancement. In this paper, we present a new adaptive spatial domain contrast and sharpness enhancement method, in which modified powerlaw transformations function is applied. Our algorithm controls perceived sharpness/smoothness, ringing artefacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artefacts by controlled parameters. Its advantage over the standard power-law transformations is to enhance sharpness /smoothness and contrast with a single function by appropriate choice of control parameters. Sharpness control parameter can be also used to smoothen the image by taking the negative value of sharpness parameter. This method can be applied both to grey scale and colour images like Gamma Correction (GC). In the case of colour images, it is applied to each channel R, G and B separately.

Image Binarization Using Block Boundary Pixels Mean

Abstract: Input image pixels classification into two intensities like black and white as foreground and background respectively refers to image binarization. Binarization with a local threshold value is termed as adaptive image binarization. Locally adaptive thresholding techniques are local pixels dependent process. Local environment dependent process is normally time consuming one and hence its computational time complexity is also local region dependent. Adaptation depends on the local region contrast condition. Low contrast region may not be adapted correctly. This article presents a new locally adaptive fast binarization technique applied on contrast stretched domain of an input image. The local adaptation is based on the mean of the 9 local block boundary pixels. As it associates only 9 pixels for mean calculation, its computational time complexity is free from local reason block size. As it applies on contrast stretched domain image, it can adapt low contrast region for binarization while other techniques fail. Not only its low contrast adapting capability, it has a local block size free computational time complexity. Hence it is more convenient to adapt the low contrast region very fast as compared with other techniques. From the experimental results, it is observed that it yields fast better result than other related local techniques.

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,