12 research outputs found
Oil Spill Segmentation using Deep Encoder-Decoder models
Crude oil is an integral component of the modern world economy. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unavoidable. Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills. The work compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data. Multiple combinations of models are used in running the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class when compared with the current benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the "oil spill" class
Oil Spill Segmentation using Deep Encoder-Decoder models
Crude oil is an integral component of the modern world economy. With the
growing demand for crude oil due to its widespread applications, accidental oil
spills are unavoidable. Even though oil spills are in and themselves difficult
to clean up, the first and foremost challenge is to detect spills. In this
research, the authors test the feasibility of deep encoder-decoder models that
can be trained effectively to detect oil spills. The work compares the results
from several segmentation models on high dimensional satellite Synthetic
Aperture Radar (SAR) image data. Multiple combinations of models are used in
running the experiments. The best-performing model is the one with the
ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over
Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class
when compared with the current benchmark model, which achieved a mean IoU of
65.05% and a class IoU of 53.38% for the "oil spill" class.Comment: 10 pages, 8 figures, 4 table
Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. This study takes an innovative approach to palaeography-the study of ancient handwriting-as a new entry point to access this scribal culture. One of the problems of palaeography is to determine writer identity or difference when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we use pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. We report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature-space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent in columns 27-29. We also demonstrated a difference in distance variances such that the variance is higher in the second part of the manuscript. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed using visual inspection of character heatmaps and of the most discriminative Fraglet sets in the script. Demonstrating that two main scribes, each showing different writing patterns, were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes, while carefully mirroring another scribe's writing style, could closely collaborate on one particular manuscript
Writer adaptation for offline text recognition: An exploration of neural network-based methods
Handwriting recognition has seen significant success with the use of deep
learning. However, a persistent shortcoming of neural networks is that they are
not well-equipped to deal with shifting data distributions. In the field of
handwritten text recognition (HTR), this shows itself in poor recognition
accuracy for writers that are not similar to those seen during training. An
ideal HTR model should be adaptive to new writing styles in order to handle the
vast amount of possible writing styles. In this paper, we explore how HTR
models can be made writer adaptive by using only a handful of examples from a
new writer (e.g., 16 examples) for adaptation. Two HTR architectures are used
as base models, using a ResNet backbone along with either an LSTM or
Transformer sequence decoder. Using these base models, two methods are
considered to make them writer adaptive: 1) model-agnostic meta-learning
(MAML), an algorithm commonly used for tasks such as few-shot classification,
and 2) writer codes, an idea originating from automatic speech recognition.
Results show that an HTR-specific version of MAML known as MetaHTR improves
performance compared to the baseline with a 1.4 to 2.0 improvement in word
error rate (WER). The improvement due to writer adaptation is between 0.2 and
0.7 WER, where a deeper model seems to lend itself better to adaptation using
MetaHTR than a shallower model. However, applying MetaHTR to larger HTR models
or sentence-level HTR may become prohibitive due to its high computational and
memory requirements. Lastly, writer codes based on learned features or Hinge
statistical features did not lead to improved recognition performance.Comment: 21 pages including appendices, 6 figures, 10 table
Feature-extraction methods for historical manuscript dating based on writing style development
Paleographers and philologists perform significant research in finding the dates of ancient manuscripts to understand the historical contexts. To estimate these dates, the traditional process of using classical paleography is subjective, tedious, and often time-consuming. An automatic system based on pattern recognition techniques that infers these dates would be a valuable tool for scholars. In this study, the development of handwriting styles over time in the Dead Sea Scrolls, a collection of ancient manuscripts, is used to create a model that predicts the date of a query manuscript. In order to extract the handwriting styles, several dedicated feature-extraction techniques have been explored. Additionally, a self-organizing time map is used as a codebook. Support vector regression is used to estimate a date based on the feature vector of a manuscript. The date estimation from grapheme-based technique outperforms other feature-extraction techniques in identifying the chronological style development of handwriting in this study of the Dead Sea Scrolls
BiNet:Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks
Handwritten document-image binarization is a semantic segmentation process to
differentiate ink pixels from background pixels. It is one of the essential
steps towards character recognition, writer identification, and script-style
evolution analysis. The binarization task itself is challenging due to the vast
diversity of writing styles, inks, and paper materials. It is even more
difficult for historical manuscripts due to the aging and degradation of the
documents over time. One of such manuscripts is the Dead Sea Scrolls (DSS)
image collection, which poses extreme challenges for the existing binarization
techniques. This article proposes a new binarization technique for the DSS
images using the deep encoder-decoder networks. Although the artificial neural
network proposed here is primarily designed to binarize the DSS images, it can
be trained on different manuscript collections as well. Additionally, the use
of transfer learning makes the network already utilizable for a wide range of
handwritten documents, making it a unique multi-purpose tool for binarization.
Qualitative results and several quantitative comparisons using both historical
manuscripts and datasets from handwritten document image binarization
competition (H-DIBCO and DIBCO) exhibit the robustness and the effectiveness of
the system. The best performing network architecture proposed here is a variant
of the U-Net encoder-decoders.Comment: 26 pages, 15 figures, 11 table
LostPaw: Finding Lost Pets using a Contrastive Learning-based Transformer with Visual Input
Losing pets can be highly distressing for pet owners, and finding a lost pet is often challenging and time-consuming. An artificial intelligence-based application can significantly improve the speed and accuracy of finding lost pets. In order to facilitate such an application, this study introduces a contrastive neural network model capable of accurately distinguishing between images of pets. The model was trained on a large dataset of dog images and evaluated through 3-fold cross-validation. Following 350 epochs of training, the model achieved a test accuracy of 90%. Furthermore, overfitting was avoided, as the test accuracy closely matched the training accuracy. Our findings suggest that contrastive neural network models hold promise as a tool for locating lost pets. This paper provides the foundation for a potential web application that allows users to upload images of their missing pets, receiving notifications when matching images are found in the application's image database. This would enable pet owners to quickly and accurately locate lost pets and reunite them with their families