Image Processing Projects with Source Code-converted

1. Image Processing Projects with Source Code URL: https://takeoffprojects.com/image-processing-projects-with-source-code Description: Image Processing Projects with Source Code With the vast expectations the domain bears on its shoulders, getting started with Image Processing can unsurprisingly be a touch intimidating. As if to form matters worse for a beginner, the myriad of high-level functions implemented can make it extremely hard to navigate. Since one among the simplest ways to urge an intuitive understanding of the sector is often to deconstruct and implement these commonly used functions yourself, the list of image processing projects ideas presented during this section seeks to try to do just that! Image Processing Projects for Beginners 1. Gray scaling Gray scaling is among the foremost commonly used preprocessing techniques because it allows for dimensionality reduction and reduces computational complexity. This process is an almost indispensable step even for more complex algorithms like Optical Character Recognition around which companies like Microsoft have built and deployed entire products (i.e. Microsoft OCR).

2. 2. Image Smoothing Image smoothing ameliorates the effect of high-frequency spatial noise from a picture. It is also a crucial step used even in advanced critical applications like medical image processing because it makes operations like derivative computation numerically stable. For this beginner- level image processing project, you'll implement Gaussian smoothing. To do so you'll get to create a 2-dimensional Gaussian kernel (possibly from one-dimensional kernels using the outer product) by employing the NumPy library then convolute it over the padded image of your choice. 3. Edge Detection Edge detection is useful for segmenting images to permit data extraction. An edge in a picture is actually a discontinuity (or a pointy change) within the pixel intensity values of a picture. You must have witnessed edge detection at play in software like King soft WPS or your very own smartphone scanners, and thus should be conversant in the importance it holds. For this project, you'll implement the sober operator for edge detection. For this, you'll use Open CV to read the image, NumPy to make the masks and perform the convolution operations, and eventually combine the horizontal and vertical mask outputs to extract all the edges. 4. Skew Correction Skew correction is beneficial in applications like OCR. The pain of skew correction is entirely avoided by having artificial intelligence-enabled features built into applications like King soft WPS. To implement your skew correction program, you'll try using Open CV to read and grayscale the image. To eliminate the skew, you'll subsequently get to compute the bounding box containing the text and adjust its angle. 5. Image Compression using MATLAB Quoting Hawking, “A picture is worth a thousand words...and uses up thousand times the memory.” Despite the benefits images have over text data, there's no denying the complexities that the additional bytes they eat up can bring. Optimization, therefore, becomes the only way out. If words alone haven’t made the case at hand convincing enough, perhaps the mention of the startup, Deep Render, which is predicated on applying machine learning to compression, raising £1.6 million in seed funding, should serve to stress the importance of this domain succinctly. For this MATLAB Image Processing project, you'll implement the discrete cosine transform approach to realize compression. It is supported the property that the majority of the important information of a picture is often described by just a couple of coefficients of the DCT. The Image Processing Toolbox software is often used for DCT computation. The input image is split into 8- by-8 or 16-by-16 blocks, and therefore the DCT coefficients computed, which have values on the brink of zero, are often discarded without seriously degrading image quality. You can use the quality ‘cameraman. If’ image as input for this purpose.