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In digital image processing, the process of improving visual quality and clarity of a digital image by changing the color intensities is referred to as "contrast enhancement". Contrast enhancement is an important image processing task in various fields such as photography, remote imaging, medical imaging, etc. We can use various tools to perform contrast enhancement of an image. In this tutorial, I will explain the process of performing contrast enhancement using histogram equalization in MATLAB. What is Contrast Enhancement? In digital image processing, contrast enhancement is a basic image processing task performed to improve visual quality of an image to ... Read More
As we know, MATLAB is a powerful to perform image processing and signal processing. We can use it to alter different parameters of an image like brightness, contrast, saturation, and more. In this article, I will explain how we can perform contrast enhancement of a color image using MATLAB. What is a Contrast Enhancement? In digital image processing, the process of improving the colors and visual quality of an image is called contrast enhancement. This technique improves the quality of an image by changing the brightness and color concentration among different regions of the image. Contrast enhancement is used ... Read More
A histogram is nothing but a graphical representation that shows the distribution of a set of data points. The normalization of a histogram is a process of distributing its frequencies over a wide range. Before discussing the implementation of histogram normalization in MATLAB, let us first get an overview of histogram normalization. What is Histogram Normalization? A histogram is a graphical way of representing the distribution of frequencies of a dataset. Sometimes, we see a histogram in which the frequencies are distributed in a small range. It results in producing poor contrast in a digital image. There is a ... Read More
MATLAB is a powerful tool to perform various operations on matrices. In the case of matrices, iterating through elements of a matrix is one of the common operations used to perform various other matrix operations such as finding sum of all elements of a matrix, finding maximum and minimum values in a matrix, and more. In this article, I will explain how to iterate through elements in an N-dimensional matrix in MATLAB. Iterating Through Elements of a Matrix In a matrix, iterating through each element means visiting and processing every value present within the matrix. An N-dimensional matrix is one ... Read More
As we know, MATLAB is a powerful tool to perform various operations on matrices and arrays. Vector is a type of matrix having only one row or one column. We can determine the inverted version of a vector using MATLAB. For this, MATLAB provides different methods and functions. So, let’s discuss the process of inverting a vector using different methods in MATLAB. Inverse a Vector Using Indexing Method in MATLAB In MATLAB, we can utilize the indexing method to find inverse of a given vector. In this method, the elements of the vector are arranged in the reverse order using ... Read More
These three types of signals (unit step, sinusoidal, and exponential) are basic signals used in analyzing different types of systems. A unit step signal has a step of magnitude one after a specific time instant. A sinusoidal signal is a type of signal which has either a sine or cosine waveform. A exponential signal is a type of signal that rises or decays exponentially with time. We can use MATLAB to generate all these types of signals. In this tutorial, I am going to explain how you can generate unit step, sinusoidal, and exponential signal in ... Read More
Introduction There is a wide range of potential language-based apps made possible by building a language detector in Android with Firebase ML Kit. Developers can simply add language recognition capabilities to their Android apps with the help of Firebase ML Kit's robust language identification features. This paves the way for automatic language recognition, which in turn allows for more individualized user experiences regardless of a user's native language. Setting up Firebase ML Kit in Android Studio Follow these steps to set up Firebase ML Kit in Android Studio Installing Firebase ML Kit Dependencies Open your Android Studio project. Add ... Read More
Introduction Algorithms are crucial in machine learning for constructing reliable and precise models. This article will compare and contrast LightGBM and XGBoost, discussing the pros and cons of each and highlighting the best applications for each. In the end, we hope to shed light on when one algorithm could be preferable to another. LightGBM Algorithm LightGBM is an effective gradient boosting method for massive datasets. Faster training and improved accuracy are the results of its tree-based learning strategy and the usage of techniques like leaf-wise tree growth and histogram-based computing. Applications include classification, regression, and ranking, all of which see ... Read More
Introduction The Android Firebase ML Kit's text detection tool makes it easier to get text from an image, which helps apps like OCR, paper scanning, and augmented reality. This guide shows how to add a text analysis to Android apps. Setting up The Development Environment Set up an Android text detector to work with Firebase ML Kit by running Android Studio and creating a project. Here are steps shown below to do so. Installing Android Studio Downloading and installing Android Studio Setting up the Android SDK Configuring virtual devices for testing Creating a New Android Project Creating ... Read More
Introduction Extraction of useful information from high-dimensional datasets is made easier by Principal component analysis, (PCA) a popular dimensionality reduction method. It does this by re-projecting data onto a different axis, where the highest variance can be captured. The complexity of the dataset is reduced while its basic structure is preserved by PCA. It helps with things like feature selection, data compression, and noise reduction in data analysis, and it can even reduce the dimensionality of the data being analyzed. Image processing, bioinformatics, economics, and the social sciences are just a few of the places PCA has been put to use. ... Read More