Computationally Efficient Data and Application Driven Color Transforms for the Compression and Enhancement of Images and Video

Minervini, Massimo; Rusu, Cristian; Tsaftaris, Sotirios A.

doi:10.1007/978-3-319-09363-5_13

Cited by 4 publications

(3 citation statements)

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“…Choice of parameters: We use only the green channel of the original RGB images for computational simplicity. Alternatively we could opt for an illumination invariant transform such as the HSV (Hue, Saturation, Value), or color transforms with class separation properties to obtain one or more channels [26]. For each dataset we repeat the learning process separately.…”

Section: Resultsmentioning

confidence: 99%

Learning to Count Leaves in Rosette Plants

Giuffrida¹,

Minervini²,

Tsaftaris³

2015

Procedings of the Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015

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121

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Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the Leaf Counting Challenge (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ∼75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image.

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Section: Resultsmentioning

confidence: 99%

Learning to Count Leaves in Rosette Plants

Giuffrida¹,

Minervini²,

Tsaftaris³

2015

Procedings of the Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015

Self Cite

121

View full text Add to dashboard Cite

show abstract

“…Image capture can be triggered manually through a push button or by the processor itself. Once the camera captures the image, it sends a Bayer format image to the FPGA, which stamps the image with a time tag and converts it from Bayer [16] to RGB and then to YCbCr to obtain the intensity image Y. Meanwhile, a histogram and statistics, such as µ, σ, Min/Max, Common1, Common2, Range & Saturation State, are calculated in parallel while the intensity image is transferred to the DDR memory.…”

Section: Optical Sensor and Image Capturementioning

confidence: 99%

AI-Based Real-Time Star Tracker

Carmeli

Ben‐Moshe

2023

Electronics

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Many systems on Earth and in space require precise orientation when observing the sky, particularly for objects that move at high speeds in space, such as satellites, spaceships, and missiles. These systems often rely on star trackers, which are devices that use star patterns to determine the orientation of the spacecraft. However, traditional star trackers are often expensive and have limitations in their accuracy and robustness. To address these challenges, this research aims to develop a high-performance and cost-effective AI-based Real-Time Star Tracker system as a basic platform for micro/nanosatellites. The system uses existing hardware, such as FPGAs and cameras, which are already part of many avionics systems, to extract line-of-sight (LOS) vectors from sky images. The algorithm implemented in this research is a “lost-in-space” algorithm that uses a self-organizing neural network map (SOM) for star pattern recognition. SOM is an unsupervised machine learning algorithm that is usually used for data visualization, clustering, and dimensionality reduction. Today’s technologies enable star-based navigation, making matching a sky image to the star map an important aspect of navigation. This research addresses the need for reliable, low-cost, and high-performance star trackers, which can accurately recognize star patterns from sky images with a success rate of about 98% in approximately 870 microseconds.

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“…Also, data acquisition and processing have a significant impact on accuracy and time limits. In order to achieve a high quality tomographic image [5], complex reconstruction algorithms performing many matrix calculations have to be applied [6][7][8][9]. However, choosing an optimal reconstruction algorithm and its implementation depends on an application.…”

Section: Introductionmentioning

confidence: 99%

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

Majchrowicz

Kapusta

Jackowska-Strumiłło

et al. 2020

Sensors

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Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on large size matrices. Therefore, a new parallel approach for 3D ECT image reconstruction is proposed, which is based on application of multi-GPU, multi-node algorithms in heterogeneous distributed system. This solution allows to speed up the required data processing. Distributed measurement system with a new framework for parallel computing and a special plugin dedicated to ECT are presented in the paper. Computing system architecture and its main features are described. Both data distribution as well as transmission between the computing nodes are discussed. System performance was measured using LBP and the Landweber’s reconstruction algorithms which were implemented as a part of the ECT plugin. Application of the framework with a new network communication layer reduced data transfer times significantly and improved the overall system efficiency.

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Computationally Efficient Data and Application Driven Color Transforms for the Compression and Enhancement of Images and Video

Cited by 4 publications

References 50 publications

Learning to Count Leaves in Rosette Plants

Learning to Count Leaves in Rosette Plants

AI-Based Real-Time Star Tracker

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

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