No description available.

No description available.

No description available.

Autonomous vehicles are equipped with systems that can detect and track the objects in a vehicle’s vicinity and make appropriate driving decisions accordingly. Infrared (IR) cameras are not typically employed on these systems, but the new information that can be supplied by IR cameras can help improve the probability of detecting all objects in a vehicle’s surroundings. The purpose of this research is to investigate how IR imaging can be leveraged to improve existing autonomous driving detection systems. This research serves as a proof-of-concept demonstration. In order to achieve detection using thermal images, raw data from seven different driving scenarios was captured and labelled using a calibrated camera. Calibrating the camera made it possible to estimate the distance to objects within the image frame. The labelled images (ground truth data) were then used to train several YOLOv2 neural networks to detect similar objects in other image frames. Deeper YOLOv2 networks trained on larger amounts of data were shown to perform better on both precision and recall metrics. A novel method of estimating pixel error in detected object locations has also been proposed which can be applied to any detection algorithm that has corresponding ground truth data. The pixel errors were shown to be normally distributed with unique spreads about different ranges of y-pixels. Low correlations were seen in detection errors in the x-pixel direction. This methodology can be used to create a gate estimation for the detected pixel location of an object. Detection using IR imaging has been shown to have promising results for applications where typical autonomous sensors can have difficulties. The work done in this thesis has shown that the additional information supplied by IR cameras has potential to improve existing autonomous sensory systems. / Thesis / Master of Applied Science (MASc)

Capsule Network (CapsNet) was introduced in 2017 as the new generation of the image classifiers to perform supervised classification of images. It incorporates a new structure of neurons which is called a capsule. A capsule is basically a vector of neurons and serves as the basic computation unit in CapsNet. CapsNet has obtained state-of-the-art testing accuracy on the task of classifying the MNIST digit recognition dataset. Despite its fundamental advantages over CNNs, it has its own shortcomings as well. CapsNet provides a relatively high accuracy in classifying images with affine transforms applied to them and also classifying images containing overlapping categories, compared to CNNs. Unlike CNNs, CapsNet creates the representation based on the part to whole relationship of the features of different levels. As a result, it comes with a more robust representation of the input image. CapsNet could only get reasonable inference accuracy on small-scale datasets. Also, it only supports a limited number of categories in the classification task. Finally, CapsNet is a relatively slow network, which is mostly due to the iterative Dynamic Routing (DR) algorithm used in it. There have been several works trying to address the shortcomings of CapsNet since it was introduced. In this work, we focus on optimizing CapsNet in several aspects: the network speed i.e. training and testing times, the number of parameters in the network, the network accuracy and its generalization ability. We propose several optimizations in order to compensate for the drawbacks of CapsNet. First, we introduce the Quick-CapsNet (QCN) network with our primary focus on the network speed. QCN makes changes to the feature extractor of CapsNet and produces fewer capsules compared to the baseline network (Base-CaspsNet). It performs inference 5x faster on small-scale datasets i.e. MNIST, F-MNIST, SVHN and CIFAR-10. QCN however loses testing accuracy marginally compared to the baseline e.g. 1% for F-MNIST dataset. Our second contribution is designing a capsule-specific layer for the feature extractor of CapsNet referred to as the Convolutional Fully-Connected (CFC) layer. We employ the CFC layer into CapsNet and call this new architecture CFC-CapsNet. CFC layer is added on top of the current feature extractor to translate the feature map into capsules. This layer has two parameters: kernel size and the output dimension. We performed some experiments to explore the effect of these two parameters on the network performance. Using the CFC layer results in reducing the number of parameters, faster training and testing, and higher test accuracy. On the CIFAR-10 dataset, CFC-CapsNet gets 1.46% higher accuracy (with baseline of 71.69%) and 49% fewer number of parameters. CFC-CapsNet is 4x and 4.5x faster than Base-CapsNet on CIFAR-10 for training and testing respectively. Our third contribution includes the introduction of LE-CapsNet as a light, enhanced and resource-aware variant of CapsNet. This network contains a Primary Capsule Generator (PCG) module as well as a robust decoder. Using 3.8M weights, LE-CapsNet obtains 77.21% accuracy for the CIFAR-10 dataset while performing inference 4x faster than CapsNet. In addition, our proposed network is more robust at detecting images with affine transformations compared to CapsNet. We achieve 94.37% accuracy on the AffNIST dataset (compared to CapsNet's 90.52%). Finally, we propose a deep variant of CapsNet consisting of several capsule layers referred to as Deep Light CapsNet (DL-CasNet). In this work, we design the Capsule Summarization (CapsSum) layer to reduce the complexity of the proposed deep network by reducing the number of parameters. DL-CapsNet, while being highly accurate, employs a small number of parameters compared to the state-of-the-art CapsNet based networks. Moreover DL-CapsNet delivers faster training and inference. Using a 7-ensemble model on the CIFAR-10 dataset, we achieve a 91.29% accuracy. DL-CapsNet is among the few networks based on CapsNet that supports the CIFAR-100 dataset (68.36% test accuracy using the 7-ensemble model) and can process complex datasets with a high number of categories. / Graduate

The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.

Yes / In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy. / Research presented in this paper is funded by the European Commission Interreg project Smart Cities and Open Data REuse (SCORE).

The world is increasingly controlled by machine learning and deep learning. Deep neural networks are becoming powerful, encroaching on many tasks in computer vision system areas previously seen as the unique domain of humans, such as image classification, object detection, semantic segmentation, and instance segmentation. The success of a deep learning model at a specific application is determined by a sequence of choices, like what kind of deep neural network will be used, what data to be fed into the deep model, and what manners will be adopted to train a deep model. The goal of this work is to design a practical, lightweight image classification model built and trained from scratch which serves as an assistant to researchers and users to recognize if a small bug is a tick. Some of the images used in this work were collected by specialists using a microscope in the Laboratory of Medical Zoology (LMZ) at the University of Massachusetts Amherst. The following techniques are used in this work. We generated four datasets by collecting 53,150 images of small bugs and cleaning the data by deleting images with low quality. Both preprocessed images and augmented images were used in the training and validation processes. Initially, we proposed the use of five lightweight CNNs. We trained each network on the same training dataset and evaluated them using the same validation dataset. After comparing these five architectures, we chose the one with the best performance, named TickNet. We compared TickNet and five other classical image classification architectures used for large-scale image recognition tasks. We determined TickNet outperforms the five classical networks in model size, number of parameters, testing time on both a CPU and GPU with a tradeoff in testing accuracy. We deployed applications on an Android mobile phone to do binary classifications and four-class image classifications to conclude the research. Disclaimer: This work or any part of it should not be used as guidance or instruction regarding the diagnosis, care, or treatment of tick-borne diseases or supersede existing guidance.

Understanding the temporal evolution of cells poses a significant challenge in developmental biology. This study embarks on a comparative analysis of various machine-learning techniques to classify cell colony images across different timestamps, thereby aiming to capture dynamic transitions of cellular states. By performing Transfer Learning with state-of-the-art classification networks, we achieve high accuracy in categorizing single-timestamp images. Furthermore, this research introduces the integration of temporal models, notably LSTM (Long Short Term Memory Network), R-Transformer (Recurrent Neural Network enhanced Transformer) and ViViT (Video Vision Transformer), to undertake this classification task to verify the effectiveness of incorporating temporal features into the classification through a comprehensive comparative analysis of these models compare to non-temporal models. This investigation not only benchmarks the efficacy of different machine-learning approaches in understanding cellular forms but also sets a precedent for future research aimed at enriching our comprehension of cellular developments through enhanced computational methodologies. The insights and methodologies derived from this study promise to contribute significantly to the advancement of computational techniques in the realm of biological research, paving the way for deeper insights into the intricacies of cellular behavior and evolution.

The thesis explores the application of Convolutional Neural Networks (CNNs) in the fashion industry, focusing on fashion style classification and image retrieval. Employing transfer learning, the study investigates the effectiveness of fine-tuning pre-trained CNN models to adapt them for a specific fashion recognition task by initially performing an extensive hyperparameter optimisation, utilising the Optuna framework.  The impact of dataset size on model performance was examined by comparing the accuracy of models trained on datasets containing 2000 and 8000 images. Results indicate that larger datasets significantly improve model performance, particularly for more complex models like EfficientNetV2S, which showed the best overall performance with an accuracy of 85.38% on the larger dataset after fine-tuning. The best-performing and fine-tuned model was subsequently used for image retrieval as features were extracted from the last convolutional layer. These features were used in a cosine similarity measure to rank images by their similarity to a query image. This technique achieved a mean average precision (mAP) of 0.4525, indicating that CNNs hold promise for enhancing fashion retrieval systems, although further improvements and validations are necessary. Overall, this research highlights the versatility of CNNs in interpreting and categorizing complex visual data. The importance of well-prepared, targeted data and refined model training strategies is highlighted to enhance the accuracy and applicability of AI in diverse fields.