Search results for "Image"
showing 10 items of 6818 documents
Development of handcrafted and deep based methods for face and facial expression recognition
2021
The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effectiv…
Artificial intelligence for image-guided prostate brachytherapy procedures
2020
Radiotherapy procedures aim at exposing cancer cells to ionizing radiation. Permanently implanting radioactive sources near to the cancer cells is a typical technique to cure early-stage prostate cancer. It involves image acquisition of the patient, delineating the target volumes and organs at risk on different medical images, treatment planning, image-guided radioactive seed delivery, and post-implant evaluation. Artificial intelligence-based medical image analysis can benefit radiotherapy procedures. It can help to facilitate and improve the efficiency of the procedures by automatically segmenting target organs and extrapolating clinically relevant information. However, manual delineation…
Computer-aided-diagnosis for ocular abnormalities from a single color fundus photography with deep learning
2023
Any damage to the retina can lead to severe consequences like blindness. This visual impairment is preventable by early detection of ocular abnormalities. Computer-aided diagnosis (CAD) for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). The main objectives of this thesis are to build two CAD models, one to detect the microaneurysms (MAs), the first visible symptom of diabetic retinopathy, and the other for multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using deep learning-based approaches. Two methods were proposed for MAs detection: ensemble-based and c…
Reliable polygonal approximations of imaged real objects through dominant point detection
1998
Abstract The problem of dominant point detection is posed, taking into account what usually happens in practice. The algorithms found in the literature often prove their performance with laboratory contours, but the shapes in real images present noise, quantization, and high inter and intra-shape variability. These effects are analyzed and solutions to them are proposed. We will also focus on the conditions for an efficient (few points) and precise (low error) dominant point extraction that preserves the original shape. A measurement of the committed error (optimization error, E 0 ) that takes into account both aspects is defined for studying this feature.
Sequential Mining Classification
2017
Sequential pattern mining is a data mining technique that aims to extract and analyze frequent subsequences from sequences of events or items with time constraint. Sequence data mining was introduced in 1995 with the well-known Apriori algorithm. The algorithm studied the transactions through time, in order to extract frequent patterns from the sequences of products related to a customer. Later, this technique became useful in many applications: DNA researches, medical diagnosis and prevention, telecommunications, etc. GSP, SPAM, SPADE, PrefixSPan and other advanced algorithms followed. View the evolution of data mining techniques based on sequential data, this paper discusses the multiple …
Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field
2018
Data Mining in general is the act of extracting interesting patterns and discovering non-trivial knowledge from a large amount of data. Medical data mining can be used to understand the events happened in the past, i.e. studying a patients vital signs to understand his complications and discover why he has died, or to predict the future by analyzing the events that had happened. In this chapter we are presenting an overview on studies that use data mining to predict heart failure and heart diseases classes. We will also focus on one of the trendiest data-mining field, namely the Sequential Mining, which is a very promising paradigm. Due to its important results in many fields, this chapter …
Overview on Sequential Mining Algorithms and Their Extensions
2018
The main purpose of data mining is to extract hidden, important and nontrivial information from a database. Sequential Pattern Mining is a data mining technique that aims to obtain and analyze frequent subsequences from sequences of events or items with or without time constraint. The importance of a sequence can be measured based on different factors such as the frequency of their occurrence, their length and also their profit. The pattern mining or the discovery of important and unexpected patterns and information was first introduced in 1990 with the well-known Apriori algorithm. Then, and after many studies on frequent pattern mining, a new approach appeared: Sequential Pattern Mining. …
Hop: Histogram of patterns for human action representation
2017
This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.
Pseudo-path connected homogeneous continua
2015
Abstract The main result of this paper states that every homogeneous pseudo-path connected continuum is weakly chainable, or equivalently, every homogeneous continuum connected by continuous images of the pseudo-arc is itself a continuous image of the pseudo-arc. We notice that even though there exist homogeneous path connected continua that are not continuous images of an arc (Prajs, 2002), they all are continuous images of the pseudo-arc.
Continuous images of arcs: Extensions of Cornette's Theorem
2015
In [J.L. Cornette “Image of a Hausdorff arc” is cyclically extensible and reducible Trans. Am. Math. Soc., 199 (1974), pp. 253–267], Cornette proved that a locally connected Hausdorff continuum X is the continuous image of an arc if and only if each of its cyclic elements is the continuous image of an arc. Cyclic elements form a closed null cover of X by retracts of X. We generalize Cornette's result to closed null covers of X with a dendritic structure. We give examples to show that some of our conditions are necessary and we pose some open questions.