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In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of ...HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.
Reproducible and efficient high-throughput phenotyping approaches, combined with advances in genome sequencing, are facilitating the discovery of genes affecting plant performance. Salinity tolerance ...is a desirable trait that can be achieved through breeding, where most have aimed at selecting for plants that perform effective ion exclusion from the shoots. To determine overall plant performance under salt stress, it is helpful to investigate several plant traits collectively in one experimental setup. Hence, we developed a quantitative phenotyping protocol using a high-throughput phenotyping system, with RGB and chlorophyll fluorescence (ChlF) imaging, which captures the growth, morphology, color and photosynthetic performance of
plants in response to salt stress. We optimized our salt treatment by controlling the soil-water content prior to introducing salt stress. We investigated these traits over time in two accessions in soil at 150, 100, or 50 mM NaCl to find that the plants subjected to 100 mM NaCl showed the most prominent responses in the absence of symptoms of severe stress. In these plants, salt stress induced significant changes in rosette area and morphology, but less prominent changes in rosette coloring and photosystem II efficiency. Clustering of ChlF traits with plant growth of nine accessions maintained at 100 mM NaCl revealed that in the early stage of salt stress, salinity tolerance correlated with non-photochemical quenching processes and during the later stage, plant performance correlated with quantum yield. This integrative approach allows the simultaneous analysis of several phenotypic traits. In combination with various genetic resources, the phenotyping protocol described here is expected to increase our understanding of plant performance and stress responses, ultimately identifying genes that improve plant performance in salt stress conditions.
In this paper, a new method for automatic first arrival picking is introduced based on pseudo-coloring and color image segmentation of refracted seismic shot-gathers. The two-step method uses the ...spiral color transformation (known as the warm color mapping) to pseudo-color the shot-gathers. This is followed by a two-class k -means clustering algorithm to segment the given colored vectors representing the first arrival amplitudes and their timing. The proposed algorithm was tested on two challenging real seismic shot-gathers. With an assessment error of a 20 ms window with manual picking of such data, the proposed method outperforms the automatic method of Coppens'. Similarly, it also outperforms the semi-automatic method based on color segmentation of energy-ratios using Projections Onto Convex Sets (POCS). In both cases, the suggested method outperforms them by an overall average of 27.58%. Therefore, it is believed that the proposed method provides an accurate and a very good alternative to existing automatic first-arrival picking methods.
Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information ...source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.
Abstrak
Masa pendemi mengharuskan setiap warga negara mengikuti protokol kesehatan kapan dan dimana pun. Dianjurkan cuci tangan dengan air mengalir. Tangan termasuk organ penting perantara keluar ...masuk bakteri, jamur, virus dan berbagai kuman berbahaya yang secara langsung maupun tidak langsung. Dalam bidang pengolahan citra dikenal segmentasi warna. Proses ekstraksi ciri warna RGB, HSV dan ruang warna lainnya dapat menghasilkan akurasi yang tinggi dengan jumlah parameter ciri seminimal mungkin sehingga proses komputasi menjadi lebih cepat. Dalam penelitian ini, dilakukan proses segmentasi citra berwarna pada bakteri Bacilus yang menempel pada telapak tangan. Ekstraksi ciri warna dilakukan untuk mengklasifikasikan bakteri. Euclidean Distance untuk klasifikasi warna pada jarak minimum dua titik tetangga yang saling berdekatan (nearest neighbor). Jumlah kelompok terlebih dahulu ditentukan sebelum pengelompokan item berdasarkan analisa data. Ciri warna diekstraks menggunakan segmantasi warna, sedangkan ciri tekstur menggunakan analisis tekstur dengan deteksi BLOB (Binary Large Object). Segementasi berbasis clutering dapat mengidentifikasi tangan yang belum cuci tangan dan kondisi tangan sesudah mencuci tangan menggunakan sabun berdasarkan warna bakteri yang telah diekstrak.
Kata kunci: bakteri, telapak tangan, segmentasi warna, clustering, pendemi
Abstract
HSV Color Segmentation of the Palm for the Detection of Bacteria in the Covid 19 Pandemic. The pandemic period requires every citizen to follow health protocols anytime and anywhere. Hand washing under running water is recommended. The hand is a vital organ directly or indirectly as an intermediary for the entry and exit of bacteria, fungi, viruses, and various harmful germs. In the field of image processing, color segmentation is known. The extraction process for RGB, HSV, and other color space features can produce high accuracy with a minimum number of feature parameters so that the computation process is faster. In this study, a color image segmentation process was carried out on Bacillus bacteria attached to the hands' palms. The extraction of color features was carried out to classify bacteria. To classify colors in a certain color group, Euclidean Distance is used, finding the minimum distance between two points of the nearest neighbor. With K-Mean, the number of groups is determined in advance, and grouping is based on predetermined information. Color features are extracted using color segmentation, while texture features use texture analysis with BLOB (Binary Large Object) detection. Clustering-based segmentation can identify hands that have not been washed and the condition of hands after washing hands using soap based on the color of the extracted bacteria.
Keywords: bacteria, palms, color segmentation, clustering, pandemic
Real-time traffic sign recognition in three stages Zaklouta, Fatin; Stanciulescu, Bogdan
Robotics and autonomous systems,
January 2014, 2014-1-00, 20140101, 2014-01, Volume:
62, Issue:
1
Journal Article
Peer reviewed
Traffic Sign Recognition (TSR) is an important component of Advanced Driver Assistance Systems (ADAS). The traffic signs enhance traffic safety by informing the driver of speed limits or possible ...dangers such as icy roads, imminent road works or pedestrian crossings. We present a three-stage real-time Traffic Sign Recognition system in this paper, consisting of a segmentation, a detection and a classification phase. We combine the color enhancement with an adaptive threshold to extract red regions in the image. The detection is performed using an efficient linear Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features. The tree classifiers, K-d tree and Random Forest, identify the content of the traffic signs found. A spatial weighting approach is proposed to improve the performance of the K-d tree. The Random Forest and Fisher’s Criterion are used to reduce the feature space and accelerate the classification. We show that only a subset of about one third of the features is sufficient to attain a high classification accuracy on the German Traffic Sign Recognition Benchmark (GTSRB).
► We propose a three stage approach for Traffic Sign Recognition. ► We enhance the color enhancement using an adaptive threshold. ► We classify the signs using K-d tree and Random Forests. ► We improve K-d tree performance using spatial weighting. ► We show that a well-chosen subset of features attains high classification accuracy.
With the advent of the era of artificial intelligence, computer vision and machine learning are developing rapidly, and human-computer interaction technology has become one of the important research ...directions. This paper mainly combines computer vision and other related technologies to analyze and judge the gestures, and predict the corresponding models according to the different matching of gestures, so as to recognize the corresponding gesture information. By converting the image into HSV space, HSV channel separation is realized, and then filtering and skin color segmentation are carried out to carry out morphological operation. Finally, shape matching is carried out according to the contour information of the image to judge the information of gesture. The experimental results show that the gesture recognition algorithm proposed in this paper can effectively judge the gesture information contained in the image, has good operation effect and strong practicability.
The process of identifying substation protection plates faces challenges due to the complex lighting factors that are often inadequately considered. Furthermore， connected domain adhesion during ...image segmentation tends to be overlooked. Consequently， a recognition method for substation protection plate that leverages HIS （hue， saturation， intensity） color characteristics and employs image processing techniques is introduced. Firstly， the images of the captured protection plate are preprocessed to correct the distortions； then， these images are segmented using HIS color characteristics. Secondly， morphological feature analysis is carried out on the connected domains； the boundary information within these domains is used to eliminate the signboards， and the limit corrosion algorithm is employed to separate the adhesive connected domains. Finally， the state of the protection plate is identified through the fusion of two key features： the boundary feature of the simple minimum bounding rectangle and the deflecti
Abstract
Speech is the major way of human communication, but when it is limited, humans move to tactile kinaesthetic communication. People with speech-hearing impairments use sign language as an ...example of such adaptations. The deaf community uses Indian sign language (ISL) throughout India. In India, 250 licensed sign language interpreters are serving a deaf population of 1.8 to 7 million individuals. ISL interpreters are badly needed at institutes and places where persons with hearing impairments communicate. An Indian sign language picture database for English alphabets is established in this project. To prepare it for training, several pre-processing techniques were used. The effectiveness of deep learning neural networks is frequently influenced by the quantity of data available. As a result, data augmentation, a strategy for adding more and diverse samples to train datasets, was used to boost the effectiveness and outcomes of machine learning models. Our model is trained in CNN models utilizing transfer learning methodologies, with an accuracy of 95% for vgg16 and an accuracy of 92% for the inception model. More study on this research, as well as real-time implementation, has the potential to better connect people with hearing loss to society.
Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, ...linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, "ABANICCO" (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC-NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO's accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging.
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