Over the past 20+ years, I have been working as a software engineer, architect, and programmer, creating, designing, and programming various applications. My main focus has always been to achieve business goals and transform business ideas into digital reality. I have successfully solved numerous business problems and increased productivity for small businesses as well as enterprise corporations through the solutions that I created.
My strong technical background and ability to work effectively in team environments make me a valuable asset to any organization.
The article begins by highlighting the dynamic nature of image processing in the context of advancing information technology. It predicts a future shift from image to video processing due to the increasing prevalence of videos over text-based media. The Video Stream Matcher (VSM) is introduced as a tool for analyzing video data using statistical models like KS Test, Variation, Mean, and Norm.
Key frames extraction is identified as a technique to minimize the vast collection of frames in a video. Four features are extracted from each frame: histogram, edge, slope, and wavelets. The literature review section references research papers and books that inform the implementation of VSM, including studies on similarity analysis of video sequences and key frame extraction.
The key elements of VSM are outlined, including the input of source and test data (videos or images), key frames extraction, feature extraction, and application of statistical models for decision making. The problem statement highlights the need for automated video evaluation systems, particularly in scenarios like security checks on public transportation and video database management in TV stations.
The proposed solution revolves around VSM’s ability to process videos, focusing on key frames extraction, feature extraction, and statistical model application for decision making. The scope of the project encompasses mean frame extraction, feature extraction algorithms, and dual-phase statistical decision making. The organization of the report is structured to cover research findings, implementation details, software functioning, and conclusions with future work discussions.
Image processing encompasses image restoration, enhancement, and compression. It involves manipulating images already captured or generated, distinct from computer graphics, which generates synthetic images. Pixels in digital images have important relationships, including neighbors, adjacency, connectivity, regions, and boundaries. Pixels are considered connected if they share a similar criterion, such as grayscale value. Different types of adjacency include 4-adjacency, 8-adjacency, and mixed adjacency, the latter resolving ambiguities in the former. Paths between pixels can be defined based on adjacency type, such as 4-, 8-, or m-paths. Regions in images are connected sets of pixels, with boundaries defined as pixels with neighboring pixels outside the region. Edges, based on intensity discontinuities, are local concepts, while boundaries are global, forming closed paths.
The BMP (Bitmap) file format, standard for Windows, stores device-independent bitmap images. It can include compression but typically lacks animation support. Comprising structures like BITMAPFILEHEADER and BITMAPINFOHEADER, it specifies image dimensions, colors, and compression. The RGBQUAD array defines color components, while pixel data interpretation varies based on the BITMAPINFOHEADER. Notably, DIB rows are stored upside-down, with row byte counts adjusted to multiples of four. Key frames in videos are essential for defining movement sequences, serving as anchor points for animation. Feature extraction simplifies complex data analysis by constructing combinations of variables, reducing resource requirements while maintaining accuracy.
