The processing demands of current and emerging applications, such as image/video processing, are increasing due to the deluge of data, generated by mobile and edge devices. This raises challenges for a vast range of c...
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ISBN:
(纸本)9781728119588
The processing demands of current and emerging applications, such as image/video processing, are increasing due to the deluge of data, generated by mobile and edge devices. This raises challenges for a vast range of computing systems, starting from smart-phones and reaching cloud and data centers. Heterogeneous computing demonstrates its ability as an efficient computing model due to its capability to adapt to various workload requirements. Field programmable gate arrays (FPGAs) provide power and performance benefits and have been used in many application domains from embedded systems to the cloud. In this paper, we used a closely coupled CPU-FPGA heterogeneous system to accelerate a sliding window based imageprocessing algorithm, Canny edge detector. We accelerated Canny using two different implementations: Code partitioned and data partitioned. In the data partitioned implementation, we proposed a weighted round robin based algorithm that partitions input images and distributes the load between the CPU and the FPGA based on latency. The paper also compares the performance of the proposed accelerators with separate CPU and FPGA implementations. Using our hybrid CPU-FPGA based algorithm, we achieved a speedup up to 4.8× over a CPU-only and up to 2.1× over a FPGA-only implementations. Moreover, the estimated total energy consumption of our algorithm is more efficient than a CPU-only implementation. Our results show a significant reduction in energy delay product (EDP) compared to the CPU-only implementation, and comparable EDP results to the FPGA-only implementation.
Synergy of optimum illumination and imageprocessing techniques is a very important aspect which needs to be incorporated in a machine vision environment to improve the durability of the lighting unit and also to cons...
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ISBN:
(纸本)9789811047626;9789811047619
Synergy of optimum illumination and imageprocessing techniques is a very important aspect which needs to be incorporated in a machine vision environment to improve the durability of the lighting unit and also to conserve power requirements. This research work presents a novel way to optimize lighting requirements in a machine vision system using image feature analysis and imageprocessingalgorithms for texture identification. The practical implementation could be considered for automated machine vision environment for object surface inspection and quality monitoring.
Lane detection algorithms have been the key enablers for a fully-assistive and autonomous navigation systems. In this paper, a novel and pragmatic approach for lane detection is proposed using a convolutional neural n...
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Lane detection algorithms have been the key enablers for a fully-assistive and autonomous navigation systems. In this paper, a novel and pragmatic approach for lane detection is proposed using a convolutional neural network (CNN) model based on SegNet encoder-decoder architecture. The encoder block renders low-resolution feature maps of the input and the decoder block provides pixel-wise classification from the feature maps. The proposed model has been trained over 2000 image data-set and tested against their corresponding ground-truth provided in the data-set for evaluation. To enable real-time navigation, we extend our model's predictions interfacing it with the existing Google APIs evaluating the metrics of the model tuning the hyper-parameters. The novelty of this approach lies in the integration of existing segnet architecture with google APIs. This interface makes it handy for assistive robotic systems. The observed results show that the proposed method is robust under challenging occlusion conditions due to pre-processing involved and gives superior performance when compared to the existing methods.
With the emergence of industry 4.0, autonomous driving vehicles have become an exciting research topic in the science technology community. The driving system requires many complex algorithms that provide both accurat...
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The current research work involves the design and development of imaging approach along with the desktop application to enhance the subcutaneous vein patterns in difficult to access subjects. The proposed approach inv...
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The current research work involves the design and development of imaging approach along with the desktop application to enhance the subcutaneous vein patterns in difficult to access subjects. The proposed approach involves a simple camera sensitive to Near Infrared Reflectance(NIR) spectra. NIR image is very high wavelength illuminated image and it is based upon the principle of recording the high contrast image in low light vision applications. The methodology involves enhancement techniques with the Frangi filter so it gives better result than the earlier techniques. The Frangi filter is specially used for vesselness detection in the human body for the analysis and observation of veinous patterns. The pre-processing and some feature extraction techniques with the Frangi filter gives the real-time image analysis so it can reduce the time required for vein detection. The main objective of this research paper is to give a more accurate result of real-time imaging and reduce the noise problem occurring in the different image enhancement algorithms in python.
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specilically, we consider a multi-scale heat diffusion on t...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
We propose a novel graph matching algorithm that uses ideas from graph signal processing to match vertices of graphs using alternative graph representations. Specilically, we consider a multi-scale heat diffusion on the graphs to create multiple weighted graph representations that incorporate both direct adjacencies as well as local structures induced from the heat diffusion. Then a multi-objective optimization method is used to match vertices across all pairs of graph representations simultaneously. We show that our proposed algorithm performs significantly better than the algorithm that only uses the adjacency matrices, especially when the number of known latent alignments between vertices (seeds) is small. We test the algorithm on a set of graphs and show that at the low seed level, the proposed algorithm performs at least 15-35% better than the traditional graph matching algorithm.
Convolutional Neural Networks (CNNs) have demonstrated great results for the single-image super-resolution (SISR) problem. Currently, most CNN algorithms promote deep and computationally expensive models to solve SISR...
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ISBN:
(纸本)9783030042240;9783030042233
Convolutional Neural Networks (CNNs) have demonstrated great results for the single-image super-resolution (SISR) problem. Currently, most CNN algorithms promote deep and computationally expensive models to solve SISR. However, we propose a novel SISR method that uses relatively less number of computations. On training, we get group convolutions that have unused connections removed. We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet. Further, a reconstruction network consisting of deconvolutional layers has been used in order to upscale to high resolution. All these steps significantly reduce the number of computations required at testing time. Along with this, bicubic upsampled input is added to the network output for easier learning. Our model is named SRCondenseNet. We evaluate the method using various benchmark datasets and show that it performs favourably against the state-of-the-art methods in terms of both accuracy and number of computations required.
image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of compute-intensive imageprocessingalgorithms whose response times are dependent on image workload. IBC systems are typ...
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ISBN:
(纸本)9781538673768
image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of compute-intensive imageprocessingalgorithms whose response times are dependent on image workload. IBC systems are typically designed for the worst-case workload that results in a long sample period and hence suboptimal quality-of-control (QoC). This worst-case based design is further considered for mapping of controller tasks and allocating platform resources, resulting in significant resource over-provisioning. Our design philosophy is to sample as fast as possible to optimise QoC for a given platform allocation, and for this, we present a structured design flow. Workload variations determine how fast we can sample and we model this dynamic behaviour using the concept of workload scenarios. Our choice of scenario-aware dataflow as the formal model for our application enables us to: i) model dynamic behaviour, analyse timing, and optimally map application tasks to the platform for maximising the effective utilisation of allocated resources, ii) relate throughput of the dataflow graph to the sample period, and thus combine dataflow analysis and mapping with control design parameters and QoC to identify system scenarios, and iii) to efficiently implement a run-time mechanism that manages necessary dynamic reconfiguration between system scenarios. Our results show that our design approach outperforms the worst-case based design with respect to optimising QoC and maximising effective resource utilisation.
Stacked image sensor systems combine an image sensor, memory, and processors using 3D technology. Stacking camera components that have traditionally been packaged separately provides several benefits: very high bandwi...
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ISBN:
(纸本)9783981926309
Stacked image sensor systems combine an image sensor, memory, and processors using 3D technology. Stacking camera components that have traditionally been packaged separately provides several benefits: very high bandwidth out of the image sensor, allowing for higher frame rates;very low latency, providing opportunities for imageprocessing and computer vision algorithms which can adapt at very high rates;and lower power consumption. This paper will review the characteristics of stacked image sensor systems and discuss novel algorithmic and systems concepts that are made possible by these stacked sensors.
The proceedings contain 216 papers. The topics discussed include: data gathering and processing in cloud based dental management systems;the impact of preprocessing on classification performance in convolutional neura...
ISBN:
(纸本)9781538668788
The proceedings contain 216 papers. The topics discussed include: data gathering and processing in cloud based dental management systems;the impact of preprocessing on classification performance in convolutional neural networks for Turkish text;a deep learning based approach to lung cancer identification;crypto-currency sentiment analyze on social media;SELFSIM: a discrete-event simulator for distributed self-stabilizing algorithms;determination of pollution on photovoltaic panels by imageprocessing;practical method for the underwater image enhancement with adjusted CLAHE;analysis of cyber-attacks on smart grid applications;and a chaotic synchronization of double convection.
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