Invisibility Cloak using Color Extraction and Image Segmentation with OpenCV

Cast shadow affects computer vision tasks such as image segmentation, object detection and tracking since objects and shadows share the same visual motion characteristics. This unavoidable problem decreases video surveillance system performance. The basic idea of this paper is to exploit the evidence that shadows darken the surface which they are cast upon. For this reason, we propose a simple and accurate method for detection of moving cast shadows based on chromatic properties in RGB, HSV and YUV color spaces. The method requires no a priori assumptions regarding the scene or lighting source. Starting from a normalization step, we apply canny filter to detect the boundary between self-shadow and cast shadow. This treatment is devoted only for the first sequence. Then, we separate between background and moving objects using an improved version of Gaussian mixture model. In order to remove these unwanted shadows completely, we use three change estimators calculated according to the intensity ratio in HSV color space, chromaticity properties in RGB color space, and brightness ratio in YUV color space. Only pixels that satisfy threshold of the three estimators are labeled as shadow and will be removed. Experiments carried out on various video databases prove that the proposed system is robust and efficient and can precisely remove shadows for a wide class of environment and without any assumptions. Experimental results also show that our approach outperforms existing methods and can run in real-time systems.

Hybrid leader corona virus herd optimizer with multilevel thresholding techniques for foreground and background image segmentation

Skin cancer is the most common type of cancer, a life-threatening condition that leads to serious health problems if not detected early, and its incidence is increasing worldwide. In recent years, computer vision and decision support systems have been used for disease detection in dermoscopic images. Furthermore, it has been observed that data representation methods affect the detection performance of these models, and the effect of color information on transformer-based models has not been sufficiently investigated. This study used the International Skin Imaging Collaboration (ISIC) 2017 dataset consisting of RGB images, and these images were converted into the HSV, LAB, and YCbCr color spaces. Transformer-based models, including visual transformer (ViT), swin transformer, data efficient image transformer (DeiT), and label-free self-distillation (DINO), were used for benign and malignant classification. According to the classification performance results, RGB and HSV color spaces particularly in the DeiT and Swin models, stable and high accuracy values ​​were obtained. It was observed that the ViT and DINO models were more sensitive to color space transformations and achieved lower classification performance compared to other models. The highest performance in skin cancer classification was achieved with the DeiT model trained in the RGB color space, with the highest accuracy (0.7668). Furthermore, the explainability-based gradient-weighted class activation mapping (Grad-CAM) method was used to analyze where the models focused in image regions when making classification decisions. This study shows the effect and usability of color space transformations in transformer-based models for skin cancer classification and offers a comparative contribution to the literature.

Precision agriculture is finding its roots in India. PA always deals with the accuracy and timely information about agriculture products. With the rapid development of computer hardware and software technology, the application of image processing technology in the agricultural research are playing key role [1]. Also, with the advantages of superior speed and accuracy, computer vision has attracted it as an alternative to human inspection [2]. In this paper, we have described a novel application of computer vision and color image segmentation for automating the precise yield prediction process of gerbera flower yield from the polyhouse images. The purpose of the present study is to design a decision support system that could generate flower yield information and serve as base for management & planning of flower marketing. Current study has applied the color image segmentation technique using threshold, to extract the flowers from the scene. Color is considered a fundamental physical property of agriculture products and foods in information analysis [3]. Using HSV color space and histogram analysis, flower color definition is done. Then by the image segmentation process, flowers were separated from the background & detected in the images. Image set with 75 images were tested with this technique.

This paper presents a real time vision-based finger counting method combining convex-hull detection and PCA techniques. The method starts by segmenting an input image to detect the area corresponding to the observed hand. For that purpose, a skin color detection method is used to differentiate the foreground containing the hand from the image background. Lighting variation can affect the accuracy of the segmentation which has an impact on the functioning of the proposed method. In order to deal with the lighting variation problem, HLS color space is used to represent the colors. Hand contour is then calculated and fingertips are detected through the detection of the convex-hull and convexity defects of the hand shape. The use of convex-hull algorithm is simple and gives accurate results when more than one finger is observed in the input image but the accuracy decreases when it comes to deal with the case where only one finger is observed. To overcome this problem, principal component analysis technique (PCA) is used to analyze the hand shape and detect the case where only one finger is observed with better accuracy. The proposed method could be utilized in Human Computer Interaction System (HCI) where the machine reacts to each detected number. Both real and synthetic images are used to test and demonstrate the potential of our method.

We report the successful integration of deep learning approach for autonomous robot KUKA YouBot navigation. The incorporation of deep learning approach was carried out through the combination among Robot Operating System, Python software and Open Source Computer Vision Library working environments. The Robot Operating System with Hydro medusa distribution provides the managing of odometry, kinematics and path planning nodes. The combination of all nodes allows the simulation of the autonomous robot navigation by using Gazebo and provides the implementation of the algorithms in simulated and real platforms. Python software improves the communication tasks taking advantage of data processing tools in the deep learning process. Then, Open Source Computer Vision Library allows the integration of the deep learning approach by using the Single Shot Detector algorithm which provides robustness in velocity and precision which mainly allows the human detection by using a trained neuronal network. The integration of Robot Operating System, Python software and Open Source Computer Vision Library is a promising architecture for providing smart autonomous navigation capability for different applications like: social robots, human rescue, precision farming, industry and so on.

We present the successful demonstration of autonomous robot KUKA YouBot navigation based on path planning and traffic signals recognition. The integration of both capabilities path planning and traffic signals recognition was carried out, thanks to the integration among Robot Operating System, MATrix LABoratory software and Open Source Computer Vision Library working environments. The Robot Operating System allows the simulation of the autonomous robot navigation by using Gazebo and provides the implementation of the algorithms in simulated and real platforms. MATrix LABoratory software improves the communication tasks taking advantage of data processing tools in the path planning process. Finally, Open Source Computer Vision Library allows the traffic signals recognition by using the Scale-Invariant Feature Transform and Speeded-Up Robust Features algorithm. The integration of Robot Operating System, MATrix LABoratory software and Open Source Computer Vision Library is a promising approach to provide autonomous navigation capability in any mobile robot and in uncontrolled environments.

This paper presents a color recognition and segmentation of color image through a combination of a multi-scale fuzzy system, an image edge based on Prewitt operator and morphological operators. In the proposed algorithm, after converting image from RGB color space to hue-saturation-value (HSV) color space, different scales of fuzzy sets in hue channel is constructed according to the people's psychological cognition to color. The image color would be quickly recognized fuzzily as less than ten kinds on scale one. To improve the performance of the color recognition rate, recognized image need to be identified on scale two to distinguish different colors observed by human eyes with the same color scheme. Then, adopting Prewitt operator to extract image edge information and applying a sequence of some morphological operators to the edge image, completing the multi-scale segmentation of image with the integration of color and edge information. Finally, indicating its area and species of specific color. The proposed algorithm can automatically adjusted in the process of color recognition and segmentation of image, it achieves recognition and segmentation without supervision. The obtained result has a good consistency with man's subjective visual perception.

For industrial flotation foam image processing, accurate bubble size measurement and feature extraction are very important to optimize the flotation process and to improve the recovery of mineral resources. This paper presents an improved algorithm to investigate mineral flotation foam image segmentation for mineral processing. Several libraries implemented for the Python programming language are used for image enhancement and compensation, quantitative analysis of factors influencing the image segmentation accuracy, and suggestions for improvement of the flotation foam image processing. The bubble characteristics-size and morphology-and the influence of the flotation conditions on the flotation foam image are analyzed. A Python implementation of the Retinex image compensation method-region-adaptive and multiscale-is proposed to address known issues of uneven illumination and shadows affecting flotation foam images, thereby improving brightness uniformity. Finally, an improved version of the watershed segmentation algorithm included in the Python Open Source Computer Vision library is used for segmentation analysis. The accuracy of the flotation foam image segmentation is 3.3% higher than for the standard watershed algorithm and the segmentation time is 9.9% shorter.

One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with region merging processes has drawbacks, one of which is suboptimal region splitting due to soft color shades, blurred contours, and uneven lighting, referred to in this study as ambiguous regions. However, in the fuzzy region initialization stage after obtaining values from the marker process, there is a possibility of missing or suboptimal determination of fuzzy regions. This is because it only takes the highest gray level value for the background marker and the lowest gray level value for the object marker. In this study, fuzzy region merging using hierarchical clustering is proposed to find optimal initialization for fuzzy regions in image segmentation. Based on the experimental results, the proposed method can achieve optimal segmentation with an average misclassification error value of 2.62% for Natural Images and 9.33% for Dental Images.

Multivariate mathematical morphology is increasing becoming a powerful utility for color image processing and analysis. In the paper, a new vector ordering scheme based on distances in the HSI color space is proposed. The methodology first uses nonlinear weighted combination with color distances, where vectors are reduced to scalars with the help of weighted coefficients. These coefficients are entirely determined by the saturation-intensity plane, which fit in with human perception. Then a lexicographical cascade is employed to complete total ordering. On the basis of the ordering approach, new erosion and dilation operations are defined. The performance of proposed is illustrated by means of color edge detection. Experimental results demonstrate its advantageous effectiveness. Additionally, the proposed framework can be also extended for other perceptual color representations based on polar coordinate.

Image segmentation and registration of carp brain tissue slices oriented to brain atlas construction.

Gold immunochromatographic strip (GICS) quantitative detective can provide more information than the qualitative or semiquantitative testing. In this paper, a fast color image segmentation method is presented to develop the quantitative detective of GICS. The image of GICS was acquired by Charge-coupled Device (CCD) image sensor, and segmented by fuzzy c-means (FCM) clustering algorithm based on color histogram in HSV color space. Maximin-distance algorithm was adopted to get the initial positions of centroids and cluster number to overcome the shortcoming that the FCM algorithm may produce local optimal results. For the segmented target image, a special characteristic parameter was constructed and calculated in HSV color space to achieve the quantitative interpretation of the GICS.

Machine vision has been widely applied in facility agriculture, and played an important role in obtaining environment information. In this paper, it is studied that application of image processing to recognize and locate corn tassel for corn detasseling machine. The corn tassel identification and location method was studied based on image processing and automated technology guidance information was provided for the actual production of corn emasculation operation. The system is the application of image processing to recognize and locate corn tassel for corn detasseling machine. According to the color characteristic of corn tassel, image processing techniques was applied to identify corn tassel of the images under HSI color space and Image segmentation was applied to extract the part of corn tassel, the feature of corn tassel was analyzed and extracted. Firstly, a series of preprocessing procedures were done. Then, an image segmentation algorithm based on HSI color space was develop to extract corn tassel from background and region growing method was proposed to recognize the corn tassel. The results show that this method could be effective for extracting corn tassel parts from the collected picture and can be used for corn tassel location information; this result could provide theoretical basis guidance for corn intelligent detasseling machine.

Object detection and tracking could be an immense, vivacious however inconclusive and trending area of computer vision. Due to its immense use in official surveillances, tracking modules applied in security and lots of other’s applications have made researchers to devise a lot of optimized and specialized methods. For validation purpose live input video will be taken for the same where objects will be getting detected and it can be simulated same for real-time through external hardware added. In the end we see the proper optimized and efficient algorithm for object detection and alert for security. Object Detection is computer vision technique used to detect object and identify its localization. This technique is not only used to identify the location but also to identify which type of object it is. This CV technique is used to detect objects in real time while maintaining the level of accuracy. By bringing some advancement in it, this system can be very helpful for people to keep track of their precious things or devices which are very expensive and need to be protected. Open CV (Open-Source Computer Vision Library) is a library of programming functions mainly aimed at real- time computer vision. Open CV features GPU acceleration for real-time operations. This feature helps us to write computationally intensive codes in C/C++ and create a Python wrapper for it so that we can use these wrappers as Python modules.