An automatic cluster-based approach for depth estimation of single 2D images

In this paper, the problem of single 2D image depth estimation is considered. This is a very important problem due to its various applications in the industry. Previous learning-based methods are based on a key assumption that color images having photometric resemblance are likely to present similar depth structure. However, these methods search the whole dataset for finding corresponding images using handcrafted features, which is quite cumbersome and inefficient process. To overcome this, we have proposed a clustering-based algorithm for depth estimation of a single 2D image using transfer learning. To realize this, images are categorized into clusters using K-means clustering algorithm and features are extracted through a pre-trained deep learning model i.e., ResNet-50. After clustering, an efficient step of replacing feature vector is embedded to speedup the process without compromising on accuracy. After then, images with similar structure as an input image, are retrieved from the best matched cluster based on their correlation values. Then, retrieved candidate depth images are employed to initialize prior depth of a query image using weighted-correlation-average (WCA). Finally, the estimated depth is improved by removing variations using cross-bilateral-filter. In order to evaluate the performance of proposed algorithm, experiments are conducted on two benchmark datasets, NYU v2 and Make3D

Recolha, extração e classificação de opiniões sobre aplicações lúdicas para saúde e bem-estar

Nowadays, mobile apps are part of the life of anyone who owns a smartphone. With technological evolution, new apps come with new features, which brings a greater demand from users when using an application. Moreover, at a time when health and well-being are a priority, more and more apps provide a better user experience, not only in terms of health monitoring but also a pleasant experience in terms of entertainment and well-being. However, there are still some limitations regarding user experience and usability. What can best translate user satisfaction and experience are application reviews. Therefore, to have a perception of the most relevant aspects of the current applications, a collection of reviews and respective classifications was performed. This thesis aims to develop a system that allows the presentation of the most relevant aspects of a given health and wellness application after collecting the reviews and later extracting the aspects and classifying them. In the reviews collection task, two Python libraries, one for the Google Play Store and one for the App Store, provide methods for extracting data about an application. For the extraction and classification of aspects, the LCF-ATEPC model was chosen given its performance in aspects-based sentiment analysis studies.Atualmente, as aplicações móveis fazem parte da vida de qualquer pessoa que possua um smartphone. Com a evolução tecnológica, novas aplicações surgem com novas funcionalidades, o que traz uma maior exigência por parte dos utilizadores quando usam uma aplicação. Numa altura em que a saúde e bem-estar são uma prioridade, existem cada vez mais aplicações com o intuito de providenciar uma melhor experiência ao utilizador, não só a nível de monitorização de saúde, mas também de uma experiência agradável em termos de entertenimento e bem estar. Contudo, existem ainda algumas limitações no que toca à experiência e usabilidade do utilizador. O que melhor pode traduzir a satisfação e experiência do utilizador são as reviews das aplicações. Assim sendo, para ter uma perceção dos aspetos mais relevantes das atuais aplicações, foi feita uma recolha das reviews e respetivas classificações. O objetivo desta tese consiste no desenvolvimento de um sistema que permita apresentar os aspetos mais relevantes de uma determinada aplicação de saúde e bem estar, após a recolha das reviews e posterior extração dos aspetos e classificação dos mesmos. No processo de recolha de reviews, foram usadas duas bibliotecas em Python, uma relativa à Google Play Store e outra à App Store, que providenciam métodos para extrair dados relativamente a uma aplicação. Para a extração e classificação dos aspetos, o modelo LCF-ATEPC foi o escolhido dada a sua performance em estudos de análise de sentimento baseada em aspectos.Mestrado em Engenharia de Computadores e Telemátic

Optimum Feature Selection for Recognizing Objects from Satellite Imagery Using Genetic Algorithm

Object recognition is a research area that aims to associate objects to categories or classes. Usually recognition of object specific geospatial features, as building, tree, mountains, roads, and rivers from high-resolution satellite imagery is a time consuming and expensive problem in the maintenance cycle of a Geographic Information System (GIS). Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy and reduce data dimensionality. This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain. This makes feature selection as an indispensable task in classification task. In our work, we propose wrapper approach based on Genetic Algorithm (GA) as an optimization algorithm to search the space of all possible subsets related to object geospatial features set for the purpose of recognition. GA is wrapped with three different classifier algorithms namely neural network, k-nearest neighbor and decision tree J48 as subset evaluating mechanism. The GA-ANN, GA-KNN and GA-J48 methods are implemented using the WEKA software on dataset that contains 38 extracted features from satellite images using ENVI software. The proposed wrapper approach incorporated the Correlation Ranking Filter (CRF) for spatial features to remove unimportant features. Results suggest that GA based neural classifiers and using CRF for spatial features are robust and effective in finding optimal subsets of features from large data sets

Novel Techniques for Automated Dental Identification

Automated dental identification is one of the best candidates for postmortem identification. With the large number of victims encountered in mass disasters, automating the process of postmortem identification is receiving an increased attention. This dissertation introduces new approaches for different stages of Automated Dental Identification system: These stages include segmentations, classification, labeling, and matching:;We modified the seam carving technique to adapt the problem of segmenting dental image records into individual teeth. We propose a two-stage teeth segmentation approach for segmenting the dental images. In the first stage, the teeth images are preprocessed by a two-step thresholding technique, which starts with an iterative thresholding followed by an adaptive thresholding to binarize the teeth images. In the second stage, we adapt the seam carving technique on the binary images, using both horizontal and vertical seams, to separate each individual tooth. We have obtained an optimality rate of 54.02% for the bitewing type images, which is superior to all existing fully automated dental segmentation algorithms in the literature, and a failure rate of 1.05%. For the periapical type images, we have obtained a high optimality rate of 58.13% and a low failure rate of 0.74 which also surpasses the performance of existing techniques. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). A dental chart is a key to avoiding illogical comparisons that inefficiently consume the limited computational resources, and may mislead decision-making. We tackle this composite problem using a two-stage approach. The first stage, utilizes low computational-cost, appearance-based features, using Orthogonal Locality Preserving Projections (OLPP) for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and hence to assign each tooth a number corresponding to its location in the dental chart, even in the presence of a missed tooth. The experimental results of teeth classification show that on a large dataset of bitewing and periapical films, the proposed approach achieves overall classification accuracy of 77% and teeth class validation enhances the overall teeth classification accuracy to 87% which is slightly better than the performance obtained from previous methods based on EigenTeeth the performance of which is 75% and 86%, respectively.;We present a new technique that searches the dental database to find a candidate list. We use dental records of the FBI\u27s Criminal Justice Service (CJIC) ADIS database, that contains 104 records (about 500 bitewing and periapical films) involving more than 2000 teeth, 47 Antemortem (AM) records and 57 Postmortem (PM) records with 20 matched records.;The proposed approach consists of two main stages, the first stage is to preprocess the dental records (segmentation and teeth labeling classification) in order to get a reliable, appearance-based, low computational-cost feature. In the second stage, we developed a technique based on LaplacianTeeth using OLPP algorithm to produce a candidate list. The proposed technique can correctly retrieve the dental records 65% in the 5 top ranks while the method based on EigenTeeth remains at 60%. The proposed approach takes about 0.17 seconds to make record to record comparison while the other method based on EigenTeeth takes about 0.09 seconds.;Finally, we address the teeth matching problem by presenting a new technique for dental record retrieval. The technique is based on the matching of the Scale Invariant feature Transform (SIFT) descriptors guided by the teeth contour between the subject and reference dental records. Our fundamental objective is to accomplish a relatively short match list, with a high probability of having the correct match reference. The proposed technique correctly retrieves the dental records with performance rates of 35% and 75% in the 1 and 5 top ranks respectively, and takes only an average time of 4.18 minutes to retrieve a match list. This compares favorably with the existing technique shape-based (edge direction histogram) method which has the performance rates of 29% and 46% in the 1 and 5 top ranks respectively.;In summary, the proposed ADIS system accurately retrieves the dental record with an overall rate of 80% in top 5 ranks when a candidate list of 20 is used (from potential match search) whereas a candidate size of 10 yields an overall rate of 84% in top 5 ranks and takes only a few minutes to search the database, which compares favorably against most of the existing methods in the literature, when both accuracy and computational complexity are considered

Semantic Segmentation in Long-term Visual Localization

Tato práce má pět hlavních cílů. Nejprve mapuje datové sady používané pro dlouhodobou vizuální lokalizaci a vybere vhodné datové sady pro další vyhodnocení. Dále je vybrán a vylepšen jeden ze současných state-of-the-art přístupů. Výsledky s pečlivě vyladěnými parametry vybrané metody dosahují lepších výsledků lokalizace. Dále je ukázáno, že dynamické objekty v obrázku jsou pro dlouhodobou vizuální lokalizaci zbytečné, protože neobsahují žádnou užitečnou informaci a lze je zcela odstranit. Čtvrtým cílem této práce je pokusit se vložit sémantickou informaci do detektoru a deskriptoru klíčových bodů SuperPoint úpravou trénovacích dat. Závěrem je dosaženo nových state-of-the-art výsledků na vybrané datové sadě aplikací nového přístupu filtrování klíčových bodů založeného na sémantické informaci. Význam této práce ukazuje důležitost analýzy obrazové informace v úloze dlouhodobé vizuální lokalizace a detekce klíčových bodů obecně.ObhájenoThis thesis has five main goals. At first, it maps the datasets used for long-term visual localization and selects viable datasets for further evaluation. Next, one of the current state-of-the-art pipelines is selected and enhanced. Results with carefully fine-tuned methods' parameters accomplish better localization results. Furthermore, it shows that dynamic objects in an image are unnecessary for long-term visual localization because they do not contain any helpful information and can be ignored. The fourth goal in this thesis is to embed semantic segmentation information into the SuperPoint keypoint detector and descriptor by editing training data. Finally, the new state-of-the-art results on a selected dataset are achieved by applying a novel keypoint filtering approach based on semantic segmentation information. The significance of this work shows the importance of analyzing underlying image information in long-term visual localization and keypoint detection in general

Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis

Multimodal Sentiment Analysis (MSA) has been a popular topic in natural language processing nowadays, at both sentence and aspect level. However, the existing approaches almost require large-size labeled datasets, which bring about large consumption of time and resources. Therefore, it is practical to explore the method for few-shot sentiment analysis in cross-modalities. Previous works generally execute on textual modality, using the prompt-based methods, mainly two types: hand-crafted prompts and learnable prompts. The existing approach in few-shot multi-modality sentiment analysis task has utilized both methods, separately. We further design a hybrid pattern that can combine one or more fixed hand-crafted prompts and learnable prompts and utilize the attention mechanisms to optimize the prompt encoder. The experiments on both sentence-level and aspect-level datasets prove that we get a significant outperformance