With the COVID-19 pandemic outbreak, most countries have limited their grain exports, which has resulted in acute food shortages and price escalation in many countries. An increase in agriculture production is importa...
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ISBN:
(纸本)9783031113451
With the COVID-19 pandemic outbreak, most countries have limited their grain exports, which has resulted in acute food shortages and price escalation in many countries. An increase in agriculture production is important to control price escalation and reduce the number of people suffering from acute hunger. But crop loss due to pests and plant diseases has also been rising worldwide, inspite of various smart agriculture solutions to control the damage. Out of several approaches, computervision-based food security systems have shown promising performance, and some pilot projects have also been successfully implemented to issue advisories to farmers based on image-based farm condition monitoring. Several imageprocessing, machine learning, and deep learning techniques have been proposed by researchers for automatic disease detection and identification. Although recent deep learning solutions are quite promising, most of them are either inspired by ILSVRC architectures with high memory and computational requirements, or light convolutional neural network (CNN) based models that have a limited degree of generalization. Thus, building a lightweight and compact CNN based model is a challenging task. In this paper, a transformer-based automatic disease detection model "PlantViT" has been proposed, which is a hybrid model of a CNN and a vision Transformer. The aim is to identify plant diseases from images of leaves by developing a vision Transformer-based deep learning technique. The model takes the capabilities of CNNs and the vision Transformer. The vision Transformer is based on a multi-head attention module. The experiment has been evaluated on two large-scale open-source plant disease detection datasets: PlantVillage and Embrapa. Experimental results show that the proposed model can achieve 98.61% and 87.87% accuracy on the PlantVillage and Embrapa datasets, respectively. The PlantViT can obtain significant improvement over the current state-of-the-art methods in plan
image upsampling from one input image gathers considerable attention in the field of computervision. The problem is ill-posed because the number of known low-resolution (LR) pixels is less than that of unknown high-r...
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ISBN:
(纸本)9781467399616
image upsampling from one input image gathers considerable attention in the field of computervision. The problem is ill-posed because the number of known low-resolution (LR) pixels is less than that of unknown high-resolution (HR) pixels. Therefore, quality of an up sampled image depends on prior assumptions. image interpolation methods are one of the image upsampling technologies and are faster than other image upsampling technologies such as Super-Resolution. However, these methods tend to cause jaggies and blurs in edge and texture regions. We use the idea of Multi-surface Fitting (MF) to solve these problems. MF uses plural local functions to estimate an HR pixel and it reduces blurs. Moreover, we utilize filtering instead of calculation of each local function in order to reduce a computational cost. And we introduce new weights to estimate edge directions. By these ideas, our method has both high quality and a low computational cost.
In recent years, several loss functions have been proposed for the image reconstruction task of convolutional autoencoders (CAEs). In this paper, a performance analysis of a CAE with respect to different loss function...
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Algorithms and approaches used for implementing software for processing an image output signal from microfluidic devices are presented in this paper. Such microdevices (often called a lab-on-a-chip) are utilized in an...
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ISBN:
(纸本)9786176079132
Algorithms and approaches used for implementing software for processing an image output signal from microfluidic devices are presented in this paper. Such microdevices (often called a lab-on-a-chip) are utilized in analysis of biological cells;their behaviors are investigated under different sets of conditions. We present here algorithms for cell detecting, processing and automatic recognition of shape and size of cells in lab-chips for bioanalysis.
In today’s world, image sharing has been a vital area of digital industry. images are transmitted over an insecure transmission channel and are vulnerable to possible attacks. In this paper, we propose a novel techni...
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With the wide adoption of high-performance processors and accelerators, large-scale computervision applications have gained great performance improvement. However, it often requires extensive experiments and expertis...
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ISBN:
(纸本)9781467375894
With the wide adoption of high-performance processors and accelerators, large-scale computervision applications have gained great performance improvement. However, it often requires extensive experiments and expertise to achieve optimal performance from manually-tuned programs, and the programs often need to be re-tuned when transplanted to a different platform, or using a different system configuration. To overcome this problem, in this paper we proposed HetroCV, a programmer-directed auto-tuning framework and runtime for computervision applications on heterogeneous CPU-MIC platform. In HetroCV auto-tuning framework, computation units in the application pipeline are categorized in to one of three patterns: Map, Stencil and MapReduce, and program statistics are extracted from units' meta-information. Machine learning is adopted to train models for each pattern using the tuned parameters and program statistics from trial-run sets, so that when a new unit is presented, HetroCV autotuner can use the corresponding trained model to generate optimized tuning parameters. In HetroCV runtime, performance models for processor and co-processor are built to predict the prospective execution time of each computation unit in the application pipeline. We adopted the maximum-throughput mapping strategy, thus each unit would be mapped dynamically to the processor/co-processor queue, which would generate the minimum overall execution time. Experiments on two medical imageprocessing applications running on heterogeneous platform composed of Intel Xeon CPU and Intel Phi co-processor showed advanced performance over naive OpenMP tuning and Genetic Algorithm (GA) based heuristic tuning.
Catadioptric panoramic image's application to computer visual field gains its popularity in recent years. However, due to its complicated imaging relationship, most existing mismatching elimination algorithms cann...
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ISBN:
(纸本)9781509019151
Catadioptric panoramic image's application to computer visual field gains its popularity in recent years. However, due to its complicated imaging relationship, most existing mismatching elimination algorithms cannot directly operate on the unprocessed panoramic images. Those above algorithms usually need to unwarp the panoramic images before further processing. In order to solve the above problems, based on the distribution characteristics of features in the panoramic image, a novel mismatching elimination algorithm is proposed in this paper. Under different scene conditions, the novel algorithm can eliminate the mismatching features and improve the matching accuracy effectively. Experiments on the image databases confirm its effectiveness.
In technological advancement, there are several techniques have discovered for exact identification of hydrocarbons which is being used by oil industries to detect the oil reservoirs. In this study, we have investigat...
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Kinect camera produces depth images as well as color images. Yet undefined depth regions called holes are often observed in the depth image and are quite harmful in generating a second view image to construct a stereo...
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Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or ...
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ISBN:
(纸本)9783319483085;9783319483078
Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, imageprocessing and computervision tools such as Hadoop imageprocessing (HIPI) and Open computervision (OpenCV) are integration is shown.
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