Author
Kanik Palodhi
Bio: Kanik Palodhi is an academic researcher from University of Calcutta. The author has contributed to research in topics: Convolution & Energy conversion efficiency. The author has an hindex of 3, co-authored 20 publications receiving 26 citations.
Papers
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TL;DR: A novel image processing technique based on convolution is developed for analyzing the etch-pit images in Nuclear Track Detectors and establishes itself to be the first of its kind to have been applied successfully to NTDs.
7 citations
01 Dec 2017
TL;DR: An efficient method of blood cell counting, particularly, for identifying red blood cell and white blood cell is discussed, based on a modified hough transform known as houghtransform filters.
Abstract: In this proceeding, an efficient method of blood cell counting, particularly, for identifying red blood cell and white blood cell is discussed. The method is based on a modified hough transform known as hough transform filters. Using these filters, simultaneous shape recognition of circles of different radii becomes much easier without having to change image parameters using thresholding or contrast enhancement etc. for each step. Initial results show that the method detects RBC and WBC without fail apart from the ones closer to image boundaries.
7 citations
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TL;DR: In this paper, a novel image processing technique based on convolution is developed for analyzing the etchpit images in Nuclear Track Detectors (NTDs), and the outcome of the application of the proposed method on the different types of NTDs (e.g., CR-39, PET) containing etch-pit openings of different sizes and shapes (circular and elliptical) is presented.
Abstract: A novel image processing technique based on convolution is developed for analyzing the etch-pit images in Nuclear Track Detectors (NTDs). The outcomes of the application of the proposed method on the different types of NTDs (e.g., CR-39, PET) containing etch-pit openings of different sizes and shapes (circular and elliptical) is presented. Promising results have been obtained for both identifying and counting the etch-pits in NTDs.
7 citations
TL;DR: In this article, the performance of various active materials categorized as organic, inorganic and organic-inorganic hybrid materials and compared their optical performances to select the efficient material with the help of finite difference time domain analysis (FDTD) and Device solutions provided in Lumerical simulator.
5 citations
TL;DR: This process, involving the sequential application of deconvolution and convolution techniques, followed by the application of Artificial Neural Network (ANN), to identify the etch-pit openings in NTDs, gives a higher degree of success compared to other conventional image analysis techniques.
4 citations
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TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:
1,829 citations
TL;DR: In this paper, nuclear tracks in solids (Principles and Applications) nuclear technology: Vol. 30, No. 1, pp. 91-92, were discussed and discussed in detail.
Abstract: (1976). Nuclear Tracks in Solids (Principles and Applications) Nuclear Technology: Vol. 30, No. 1, pp. 91-92.
973 citations
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01 Apr 1998
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586 citations
01 Aug 2020
TL;DR: In this paper, the VLSI implementation of HAAR wavelet-based image compression is proposed and designed and provides a hardware-free architecture with low cost.
Abstract: The Discrete Wavelet transform is one of the best tools for signal and data analysis, It requires efficient hardware implementation in the real-time applications. The submissions established in the field of imaging necessitates compacted architecture. In DWT discrete sampling is accomplished for the wavelets. In this paper, the VLSI implementation of HAAR wavelet-based image compression is proposed and designed. HAAR wavelet transform is one of the easiest methods for image compression because it has coefficients as either 1 or −1. In this work software alone is used for the compression together with optimizing it with a continuous optimization algorithm and provides a hardware-free architecture with low cost. The VHDL work is carried out in Xilinx Platform and provides a truncated power architecture for a concrete application. The same VHDL architecture can also be instigated in FPGA which will harvest hardware effectual compromising outcomes.
47 citations
05 Mar 2020
TL;DR: Higher compression ratio is obtained after three levels of decompositon and the decomposed can be reconstructed without appreciable loss in the original image.
Abstract: Image Compression aims at minimal the Storage and for the easy transmission Without affecting pictures quality. In this paper HAAR wavelet based Discrete Wavelet Transform (DWT) is done for the effective and efficient image compression..HAAR DWT provides an easy way of compression as the coefficient are either 1 or -1.The wavelet transforms are used for the time and frequency analysis. In this paper higher compression ratio is obtained after three levels of decompositon. The decomposed can be reconstructed without appreciable loss in the original image.
29 citations