Tracking deformable objects with WISARD networks

In this paper, we investigate a new approach based on WISARD Neural Network for the tracking of non-rigid deformable object. The proposed approach allows deploying an on–line training on the texture and shape features of the object, to adapt in real–time to changes, and to partially cope with occlusions. Moreover, the use of parallel classificatory trained on the same set of images allows tracking the movements of the objects. We evaluate our tracking abilities in the scenario of pizza making that represents a very challenging benchmark to test the approach since in this context the shape of the object to track completely changes during the manipulation

WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue

Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal cameras, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications

Distributive thermometer: A new unary encoding for weightless neural networks

The binary encoding of real valued inputs is a crucial part of Weightless Neural Networks. The Linear Thermometer and its variations are the most prominent methods to determine binary encoding for input data but, as they make assumptions about the input distribution, the resulting encoding is sub-optimal and possibly wasteful when the assumption is incorrect. We propose a new thermometer approach that doesn’t require such assumptions. Our results show that it achieves similar or better accuracy when compared to a thermometer that correctly assumes the distribution, and accuracy gains up to 26.3% when other thermometer representations assume an unsound distribution.info:eu-repo/semantics/publishedVersio

Rejection-oriented learning without complete class information

Machine Learning is commonly used to support decision-making in numerous, diverse contexts. Its usefulness in this regard is unquestionable: there are complex systems built on the top of machine learning techniques whose descriptive and predictive capabilities go far beyond those of human beings. However, these systems still have limitations, whose analysis enable to estimate their applicability and confidence in various cases. This is interesting considering that abstention from the provision of a response is preferable to make a mistake in doing so. In the context of classification-like tasks, the indication of such inconclusive output is called rejection. The research which culminated in this thesis led to the conception, implementation and evaluation of rejection-oriented learning systems for two distinct tasks: open set recognition and data stream clustering. These system were derived from WiSARD artificial neural network, which had rejection modelling incorporated into its functioning. This text details and discuss such realizations. It also presents experimental results which allow assess the scientific and practical importance of the proposed state-of-the-art methodology.Aprendizado de Máquina é comumente usado para apoiar a tomada de decisão em numerosos e diversos contextos. Sua utilidade neste sentido é inquestionável: existem sistemas complexos baseados em técnicas de aprendizado de máquina cujas capacidades descritivas e preditivas vão muito além das dos seres humanos. Contudo, esses sistemas ainda possuem limitações, cuja análise permite estimar sua aplicabilidade e confiança em vários casos. Isto é interessante considerando que a abstenção da provisão de uma resposta é preferível a cometer um equívoco ao realizar tal ação. No contexto de classificação e tarefas similares, a indicação desse resultado inconclusivo é chamada de rejeição. A pesquisa que culminou nesta tese proporcionou a concepção, implementação e avaliação de sistemas de aprendizado orientados `a rejeição para duas tarefas distintas: reconhecimento em cenário abertos e agrupamento de dados em fluxo contínuo. Estes sistemas foram derivados da rede neural artificial WiSARD, que teve a modelagem de rejeição incorporada a seu funcionamento. Este texto detalha e discute tais realizações. Ele também apresenta resultados experimentais que permitem avaliar a importância científica e prática da metodologia de ponta proposta

Comparação de desempenho entre os modelos neurais ágeis ELM e WiSARD

Neural models are popular in machine learning. Agile neural models are a subset of this kind of models and are characterized by presenting a significantly faster training time, being applied mainly in online learning domains. Two examples of agile neural models are the Extreme Learning Machine (ELM), a single hidden layer feedforward neural network which synaptic weights do not need to be iteractively adjusted, and the Wilkes, Stonham and Aleksander Recognition Device (WiSARD), a weightless neural network model with multiple discriminators that use neurons based on RAM memory structures. In this work, a comparative study between ELM and WiSARD models is made, aiming to evaluate both models performance when applied to different datasets having different characteristics. The evaluation is made by comparing test accuracy, training and testing times metrics, as well as the amount of RAM memory consumed by the models.Modelos neurais são populares na área de aprendizado de máquina. Dentre os vários tipos de modelos desta classe, os modelos neurais ágeis se destacam por apresentarem tempo de treinamento consideravelmente inferior, sendo utilizados principalmente em domínios de aprendizado online. Dois exemplos deste tipo de modelo são a Extreme Learning Machine (ELM), que é uma rede neural com uma única camada oculta cujos pesos sinápticos não precisam ser ajustados, e a Wilkes, Stonham and Aleksander Recognition Device (WiSARD), um modelo de rede neural sem pesos com múltiplos discriminadores que utilizam neurônios implementados como estruturas de memória RAM. Neste trabalho, ´e realizado um estudo comparativo entre os modelos neurais ágeis ELM e WiSARD, visando avaliar o desempenho de ambos quando aplicados a diferentes conjuntos de dados com diferentes características. A avaliação é feita a partir da comparação das métricas de acurácia de teste, tempos de treinamento e de teste, além do uso de memória RAM dos dois modelos

LambdaNet: A Novel Architecture for Unstructured Change Detection

The goal of this thesis is the development of LambdaNet, a new type of network architecture for the performance of unstructured change detection. LambdaNet combines concepts from Siamese and semantic segmentation architectures, and is capable of identifying and localizing the significant differences between image pairs while simultaneously disregarding background noise. Changes are marked at the pixel level, by interpreting change detection as a binary (change/no change) classification problem. Development of this architecture began with an evaluation of several candidate models, inspired by other successful network architectures and layers, including VGG, ResNet, and the Res2Net layer. Once the best performing LambdaNet architecture was determined, it was extended to incorporate a multi-class version of change detection. Referred to as directional change, this technique allows segmentation-based output of change information in four different classes: No change, additive change, subtractive change, and exchange. Lastly, change detection is not the only unstructured operation of interest. One of the most successful unstructured techniques is that of artistic style transfer. This method allows information from a style image to be merged into a supplied content image. In order to implement this technique, a new variant of LambdaNet was developed, called LambdaStyler. This network is capable of learning multiple artistic styles, which can then be selected for application to the desired content image

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems