Object Tracking Using HSV Values and OpenCV

This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

Aerial tracer particle distribution system for surface image velocimetry

A novel aerial tracer particle distribution system has been developed. This system is mounted on an Unmanned Aerial Vehicle (UAV) and flown upstream from where surface velocimetry measurements are conducted. This enables surface velocimetry techniques to be applied in rivers and channels lacking sufficient natural tracer particles or surface features. Lack of tracers is a common problem during low flows, in lowland rivers, or in artificial channels. This is particularly problematic for analysis conducted using Particle Image Velocimetry (PIV) techniques where dense tracer particles are required. Techniques for colouring tracer particles with biodegradable dye have also been developed, along with methods for extracting them from Red Green Blue (RGB) imagery in the Hue Saturation Value (HSV) colour space. The use of coloured tracer particles enables flow measurements in situations where sunglint, surface waves, moving shadows, or dappled lighting on riverbeds can interfere with and corrupt results using surface velocimetry techniques. These developments further expand the situations where surface velocimetry can be applied, as well as improving the accuracy of the results.

Research on the Application of Artificial Intelligence Technology in Computer Graphics Processing

In structured light geometric reconstruction, due to the complexity of shooting methods and scene lighting conditions, the resulting images may be lack of image details due to uneven light. For this reason, the article proposes a Retinex algorithm with colour restoration and colour saturation correction strategy based on HSV colour space transformation based on artificial intelligence technology. Then distinguish whether it is a bright area according to the threshold value, and modify the insufficient transmittance estimation of the bright area. Finally, the intensity component and saturation value are restored in the HIS colour space, and the histogram is used to stretch the intensity component.

Harumanis Mango Leaves Image Segmentation on RGB and HSV Colour Spaces using Fast k-Means Clustering

Harumanis mango is the signature fruit in Perlis due to its delicious taste and its sweet-smelling. A good quality Harumanis tree requires rich in nutrition (healthy), and the tree will grow lots of fruits compared to the trees which are poor in nutrition (unhealthy). The health condition of a tree can be observed through the leaves in term of shape of leaves. For a healthy Harumanis tree, the leaves grow in scattering shapes. Meanwhile, an unhealthy Harumanis tree grows in gathered shapes. Therefore, this research is focusing on Harumanis mango leaves image segmentation by comparing between RGB and HSV colour spaces in order to obtain the best segmentation performance. 100 of Harumanis mango tree leaves images are used in this research. These images have undergo through image pre-processing such as modified linear contrast stretching and colour components extraction based on RGB and HSV colour spaces. Then, the colour component images have been segmented by using fast k-means clustering in order to obtain the leaves segmented images. Finally, quantitative analyses have been performed to measure the segmentation performance based on sensitivity, specificity and accuracy. Overall, the results show that S component of HSV colour space archives the highest accuracy with 85.81%.

AUTOMATIC POINT CLOUD SEGMENTATION FOR THE DETECTION OF ALTERATIONS ON HISTORICAL BUILDINGS THROUGH AN UNSUPERVISED AND CLUSTERING-BASED MACHINE LEARNING APPROACH

The article describes an innovative procedure for the three-dimensional analysis of decay morphologies of ancient buildings, through the application of machine learning methods for the automatic segmentation of point clouds. In the field of Cultural Heritage conservation, photogrammetric data can be exploited, for diagnostic and monitoring support, to recognize different typologies of alterations visible on the masonry surface, starting from colour information. Actually, certain stone and plaster surface pathologies (biological patina, biological colonization, chromatic alterations, spots,…) are typically characterized by chromatic variations. To this purpose, colour-based segmentation with hierarchical clustering has been implemented on colour data of point clouds, considered in the HSV colour-space. In addition, geometry-based segmentation of 3D reconstructions has been performed, in order to identify the main architectural elements (walls, vaults), and to associate them to the detected defects. The proposed workflow has been applied to some ancient buildings’ environments, chosen because of their irregularity both in geometrical and colorimetric characteristics.

Multiple Object Recognition Using OpenCV

For automatic vision systems used in agriculture, the project presents object characteristics analysis using image processing techniques. In agriculture science, automatic object characteristics identification is important for monitoring vast areas of crops, and it detects signs of object characteristics as soon as it occurs on plant leaves. Image content characterization and supervised classifier type neural network are used in the proposed deciding method. Pre-processing, image segmentation, and detection are some of the image processing methods used in this form of decision making. An image data will be rearranged and, if necessary, a region of interest will be selected during preparation. For network training and classification, colour and texture features are extracted from an input. Colour characteristics such as mean and variance in the HSV colour space, as well as texture characteristics such as energy, contrast, homogeneity, and correlation. The device will be trained to automatically identify test images in order to assess object characteristics. With some training samples of that type, an automated classifier NN could be used for classification supported learning in this method. The tangent sigmoid function is used as the kernel function in this network. Finally, the simulated results show that the used network classifier has a low error rate during training and higher classification accuracy. In the previous researches Object detection has been made possible, but in our current research we have attempted to do live Object Detection using OpenCV and also the techniques involved in it.

Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features

Peanuts are a food crop commodity that Indonesians widely consume as a vegetable fat and protein source. However, the quality and quantity of peanut productivity may decline, one of which is due to plant diseases. Efforts that can be made to maintain peanut productivity are the application of technology to detect peanut plant diseases early; thus, disease control can be carried out earlier. This study presents a technology development application, particularly digital image processing, to identify disease features of infected peanut leaves based on GLCM texture features and colour features in the HSV colour space and classified using the SVM method. The development of the SVM method that is applied is the Multiclass SVM with the DAGSVM strategy, which can classify more than two classes. Based on the experimental results, it confirms that the combination of HSV colour features and GLCM texture features with an angular orientation of 0 degrees and classified by the Multiclass SVM method with polynomial kernels produces the highest accuracy, i.e. 99.1667% for leaf spot class, 97.5% for leaf rust class, 98.8333% for eyespot class, 100% for normal leaf class and 100% for other leaf class.

Artificial Intelligence-Assisted Fresco Restoration with Multiscale Line Drawing Generation

In this article, we study the mural restoration work based on artificial intelligence-assisted multiscale trace generation. Firstly, we convert the fresco images to colour space to obtain the luminance and chromaticity component images; then we process each component image to enhance the edges of the exfoliated region using high and low hat operations; then we construct a multistructure morphological filter to smooth the noise of the image. Finally, the fused mask image is fused with the original mural to obtain the final calibration result. The fresco is converted to HSV colour space, and chromaticity, saturation, and luminance features are introduced; then the confidence term and data term are used to determine the priority of shedding boundary points; then a new block matching criterion is defined, and the best matching block is obtained to replace the block to be repaired based on the structural similarity between the block to be repaired and the matching block by global search; finally, the restoration result is converted to RGB colour space to obtain the final restoration result. An improved generative adversarial network structure is proposed to address the shortcomings of the existing network structure in mural defect restoration, and the effectiveness of the improved modules of the network is verified. Compared with the existing mural restoration algorithms on the test data experimentally verified, the peak signal-to-noise ratio (PSNR) score is improved by 4% and the structural similarity (SSIM) score is improved by 2%.