The pet market is getting growth rapidly in the world, and the ornamental fish occupy the third in the market ranking, behind dogs and cats. According to the statistics of the Ornamental Fish Association, Taiwan has e...
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The pet market is getting growth rapidly in the world, and the ornamental fish occupy the third in the market ranking, behind dogs and cats. According to the statistics of the Ornamental Fish Association, Taiwan has exported 18 million ornamental shrimps annually since 2010. Almost six-tenths of global ornamental shrimps are from Taiwan. OpenCV (Open Source Computer vision) provides plenty of machinevisionapplications and often cooperates with the Raspberry Pi to enhance the use of machine engineering for commercial products. This research is, therefore, mainly designed to apply the machinevision to undertake the counting of shrimps automatically. The steps of imageprocessing for accurately counting shrimps are as follows: (1) read the image graphic, (2) filter and remain the sampling color, (3) threshold the image, (4) contour the shrimps in the image (5) count the number. Concerning the performance and reliability, we process the image using Amazon Web Service (AWS) Lambda function. Experimental results of counting shrimps (Neocaridina heteropoda var. red) show that it takes 0.1 s to count 150 shrimps and the precision rate is about 95%.
Computer vision plays a crucial role in current technological development, understanding a scene from the properties of 2D images. This research line becomes valuable in sports applications, where the scenario can be ...
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Computer vision plays a crucial role in current technological development, understanding a scene from the properties of 2D images. This research line becomes valuable in sports applications, where the scenario can be challenging to take technical decisions only from the observation. This work aims to develop a system based on computer vision for analyzing tennis games. The implemented method captures videos during the game through cameras installed on the court. machine learning methods and morphological operations will be used over the images to locate the ball position, the court lines and the players location. In addition, the algorithm determines the moment the ball bounces during the game and analyzes whether it occurred in or out of the field. These data are available to players and judges through an Android application, allowing all processed data to be accessed from mobile devices, providing the results quickly and accessible to the user. From the results obtained, the system demonstrated robustness and reliability.
Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of...
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Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel rho-vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed rho-vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the rho-vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.
With emergence of Internet of Things (IoT) and subsequent technologies, smart devices are being increasingly used in various domains such as smart homes, smart parking, intelligent transportation etc. Vast amount of i...
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With emergence of Internet of Things (IoT) and subsequent technologies, smart devices are being increasingly used in various domains such as smart homes, smart parking, intelligent transportation etc. Vast amount of image and video data has been produced by IoT based systems in the form of continuous and possibly infinite image and video streams. This demands the development of advanced predictive vision systems which exploits stream mining concepts for intelligent processing of visual data streams. Among other challenges faced by visual data streams, a major challenge is concept drift, which is caused by overtime change in data distribution. In the presence of skewed data, the detection of concept drift becomes more challenging. When analyzing the data generated from smart devices and other performance critical wireless sensors, concept drift affects data integrity and accuracy of prediction results. EWMA for Concept Drift Detection (ECDD) has been proposed in the literature for detecting data streams. However, ECDD has a high prediction error rate which makes it less useful for performance critical data streams generated by imaging and video data streams. In this paper, vision based Drift Detection Method (VisDDM) is proposed, which systematically handles abrupt and gradual concept drift in data streams. Experiments have been performed using synthetic and real world datasets from different application domains. Our proposed VisDDM algorithm is able to handle abrupt and gradual drift types and outperformed the existing drift detection methods in terms of accuracy and mean evaluation time.
Fabric Defect (FD) detection has played an important role in the process of fabric production. FDs lead to a reduction in cloth prices, causing a significant loss of 45% to 65% for the cloth manufacturer. The detectio...
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Fabric Defect (FD) detection has played an important role in the process of fabric production. FDs lead to a reduction in cloth prices, causing a significant loss of 45% to 65% for the cloth manufacturer. The detection of FDs becomes a challenging task in the fabric industry due to its complicated shapes and a considerable amount of FDs. Thus, a new detection technique, which has great detection speed and accuracy, is desired to replace manual work. With the emergence of Convolution Neural Networks (CNN) and the development of machinevision and Deep Learning (DL), various detection techniques, combining the benefits of DL and machinevision, have emerged, which replace manual and imageprocessing approaches. The study presents an Automated Fabric Defect Detection using a Hybrid Particle Cat Swarm Optimizer with a Deep Learning (AFDD-HPCSODL) algorithm. AFDD-HPCSODL method aims to detect and categorize the existence of defects in fabric production. The objective is to automate and improve the performance of fabric defect detection, addressing the complexity of different defect patterns and characteristics in textile images. In the presented AFDD-HPCSODL technique, two stages of data pre-processing take place namely contrast enhancement and bilateral filtering (BF)-based noise elimination. Besides, a fusion of feature extraction processes is carried out using Inception V3 and EfficientNetB3. Moreover, the Attention Convolutional Long Short-Term Memory (ACLSTM) network is exploited for the detection and classification of FDs. Furthermore, the HPCSO technique carries out the hyperparameter selection of the ACLSTM model. Finally, the Root Mean Square Propagation (RMSProp) optimizer with the NestNet model is utilized for the defect segmentation process. The stimulation validation of the AFDD-HPCSODL method is studied on the two FD datasets. Widespread comparative results emphasized the improved performance of the AFDD-HPCSODL technique on FD classification with maximum a
In order to solve the problem of crop disease detection in large-scale planting, a new crop disease detection algorithm based on multi-feature decision fusion is proposed. This paper proposes a multi-feature decision ...
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In order to solve the problem of crop disease detection in large-scale planting, a new crop disease detection algorithm based on multi-feature decision fusion is proposed. This paper proposes a multi-feature decision fusion disease discrimination algorithm (PD R-CNN) based on machinevision on crop surfaces. The algorithm is based on the machinevisionprocessing model of R-CNN and integrates a disease discrimination algorithm on the basis of R-CNN. After training on crop image data sets, PD R-CNN can reach the goal of identifying crop surface lesions. This paper uses machinevisionimage acquisition, imageprocessing and analysis technology to collect and analyze the growth of cucumber seedlings. The research results show that compared with manual judgment, PD R-CNN reduces the workload and can effectively distinguish crop diseases. Through experiments, during the occurrence of pests and diseases, PD R-CNN has a monitoring accuracy of 88.0% for mosaic disease, 92.0% for root rot, 88.0% for powdery mildew, and 86.0% for aphids, indicating that there are errors in actual monitoring, but the accuracy exceeds 85.0% can be put into use.
With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processi...
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Kaziranga National Park, a UNESCO World Heritage Site and a sanctuary for the one-horned rhinoceros represents a critical ecosystem with a rich biodiversity that necessitates comprehensive monitoring and conservation ...
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ISBN:
(纸本)9798400716553
Kaziranga National Park, a UNESCO World Heritage Site and a sanctuary for the one-horned rhinoceros represents a critical ecosystem with a rich biodiversity that necessitates comprehensive monitoring and conservation efforts. This research article presents an in-depth study of the temporal changes within Kaziranga National Park over a decade, employing advanced imageprocessing techniques on satellite imagery data from 2014 to 2022. The primary objective was to quantify and analyze the changes in land cover, including vegetation density, water body dynamics, and wetland alterations within and around the park's premises. Utilizing a combination of multispectral analysis, change detection algorithms, and supervised classification methods, the assessment of the variations in the park's landscape was studied. There was a notable fluctuation in the water bodies, largely attributable to the annual flood cycles of the Brahmaputra River, which both enriches the park's alluvial soil and poses a challenge to wildlife conservation. Vegetation analysis indicated areas of regrowth and decline, highlighting the impacts of natural processes and human intervention on the park's wetlands. This study underlines the importance of leveraging satellite imagery and imageprocessing technologies for continuous environmental monitoring, providing an indispensable tool for conservationists, policymakers, and researchers dedicated to safeguarding natural habitats in the face of global environmental changes.
Potato is a widely consumed food worldwide, and its productivity has increased due to new varieties and the use of technologies related to irrigation, nutrition, and soil preparation, among others. However, diseases s...
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ISBN:
(纸本)9783031530357;9783031530364
Potato is a widely consumed food worldwide, and its productivity has increased due to new varieties and the use of technologies related to irrigation, nutrition, and soil preparation, among others. However, diseases such as late blight disease can often affect the crop, impacting many farmers around the world. As a way to help production, technology in agriculture is increasing. Among the various computational techniques that can be applied, those based on digital imageprocessing associated with machine learning algorithms stand out, producing excellent results. This work aimed to develop a methodology for recognizing late blight disease in potato leaves using digital imageprocessing techniques and machine learning algorithms. It was possible to obtain promising results. The experiments were carried out in a set of images from a public database containing images of healthy and unhealthy leaves (with late blight). We compare the performance of machine learning algorithms using feature vectors obtained with SIFT algorithm and RGB descriptors. The best performance was using the Decision Tree algorithm and SIFT vectors, with 99.24% of accuracy.
object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion me...
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