Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.

Motion detection is an important process in computer vision to analyze activities in videos. This study implements a simple system to detect motion in video files using Python and the OpenCV library. The system works by comparing consecutive frames in a video to detect changes and mark areas that experience motion. The implementation shows satisfactory results on various sample videos. This study provides a solution that is easy to implement and can be used in applications such as video analysis and computer-based monitoring.

OpenCV is a widely used library in the field of image processing and computer vision. Combined with Python's flexibility, OpenCV provides an extensive range of functions for efficient image processing, modification, analysis, and visualization. This paper aims to introduce the fundamental concepts of image processing using Python and OpenCV, including image reading, color conversion, edge detection, and image manipulation such as resizing and cropping. Furthermore, this study discusses basic analysis techniques like color distribution histograms and feature detection. By presenting these concepts, the paper aspires to help readers understand the foundations of digital image processing and explore the potential applications of OpenCV in practical fields such as pattern recognition, object detection, and image segmentation.

implemented and evaluated the edge detection method using the Sobel Operator, which calculates the gradient of image intensity through two convoluted kernels for horizontal (Gx) and vertical directions. (Gy). The magnitudo gradient is obtained from the combination of both such directional gradients to represent the edge force on each pixel. The main steps include image pre-processing, the application of the Sobel kernel, the calculation of magnitudo gradients, and the filtering of results to extract significant edges. The results show that the Sobel Operator is effective in highlighting intensity differences that indicate the boundary of the object, although it is sensitive to noise and less accurate for fine edges. Despite its limitations, this method is simple to implement and useful as an initial step in image processing applications such as segmentation, pattern identification, and object shape analysis.