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Algorithms based on computer vision are commonly used in pre-operative procedures to help health professionals detecting blood vessels, which is also the case with the Deep Inferior Epigastric Perforators (DIEPs). These blood vessels are essential to produce a viable autologous DIEP flap, and the analysis of characteristics such as their location, diameter and course is essential to ensure the success of surgeries. This analysis is made by a team of radiology technicians and then validated by a surgeon, making it a complex process that can take up to 2 hours. The proposed algorithm called Assisted Vascular Analysis (AVA) was developed to ensure a faster alternative to the conventional methods, using automation to identify structures of interest such as the skin, umbilicus and fascia, while also requiring minimum input from the user to segment each DIEP (2 points for the subcutaneous portion and 2 for the intramuscular portion). The AVA feasibility tests where conducted using 6 Computed Tomography Angiographies (CTAs), with a total of 28 DIEP calibers obtained during surgery (ground truths) from patients that underwent breast reconstruction with a DIEP flap. The algorithm was evaluated for its capability to segment the DIEPs and measure their caliber, comparing the results with the ground truth calibers and the manual mapping done by the radiology technicians. The Root Mean Square Error (RMSE) metric shows that the calibers obtained by the AVA algorithm (0.57 millimeters) and the radiology technicians (0.46 millimeters) are very similar, with the radiology technicians gaining a smaller edge of 0.11 millimeters. These results are very promising, since the errors are inferior to the average image resolution (0.88 millimeters). It was also demonstrated that the AVA algorithm is a faster alternative to manual segmentation, taking around 10 minutes to fully analyze each CTA, while the radiology technicians takes around 1 hour to do the DIEP mapping and caliber measurements. In conclusion, AVA is a validated algorithm to segment DIEP vessels and a faster alternative compared with conventional methods.

Breast cancer remains one of the most prevalent and deadly cancers worldwide, making accurate evaluation of molecular markers important for effective disease management. Biomarkers such as ER, PR, and HER2 are typically assessed because they help inform prognosis and guide treatment decisions. Predicting these characteristics from imaging can support earlier clinical intervention, reduce reliance on invasive procedures, and contribute to more personalized care. While radiomics and deep learning approaches have demonstrated potential, comprehensive comparisons across these methods are still limited. This study evaluated handcrafted features, deep features, and end-to-end deep learning models for predicting ER, PR, and HER2 status from DCE-MRI. Each feature type was first assessed individually and then combined using early and late fusion. Handcrafted and deep features were processed through a pipeline that included resampling, dimensionality reduction, and model selection, while end-to-end models were trained using different initialization strategies and loss functions. The best models achieved AUCs of 0.659 for ER, 0.679 for PR, and 0.686 for HER2. Although late fusion generally improved performance, bias toward the majority classes persisted. Overall, the results suggest that combining different modeling strategies may enhance robustness in breast cancer characterization.

Neuroblastoma (NB) is the most common extracranial tumor in pediatric cases. The MYCN oncogene amplification (MNA) is knowingly correlated with a poor prognosis, making detecting this biomarker crucial for treatment selection and survival prediction. The current clinical protocol for MNA detection includes invasive procedures, such as biopsy. The proposed work aims to develop non-invasive techniques for predicting MNA in patients with diagnosed NB, using AI-based models and Computerized Tomography (CT) scans. Machine learning methods that use the imaging features extracted from the tumor on the CT slices were developed and compared with deep learning (DL) models. Additionally, agnostic explainable methods for imaging were applied to create explanations about the relevant information used by the DL models in the prediction. The results show a better performance for the DL approach, which achieved an AUC of$0.94 \pm 0.04$. The similarity in the explanations produced by the models trained with different data splits showed that feature extraction remains somewhat invariant to shifts in training data, which is relevant given the small amount of data available. Learning models were shown to have predictive potential that, with further improvements, can be integrated into predictive, explainable, and, thus, trustworthy systems to aid clinicians in the decision-making process.

Segmentation of the lungs in Computed Tomography (CT) is very challenging due to changes in lung shape, size, and parenchyma pattern, as well as differences in imaging acquisition protocols. As a consequence, these models may not be robust and may decrease their performance when deployed in a clinical setting. The Continual Learning paradigm holds great promise since learning models continually acquire incoming knowledge, having the ability to adapt to changing environments. In this work, experience replay with random sampling of past data was implemented, using the original CT images and the corresponding ground-truths. Data from four different institutions were used to develop the experiments, and the models were evaluated on a cross-cohort dataset. Using raw data, the goal was to study how the datasets and their imaging patterns were related and what impact the training datasets have on one another. The catastrophic forgetting effect diminished for almost all datasets. For two of the in-domain test datasets there was forward and backward transfer, results that could be linked to a possible similarity between them. A mean DSC of 0.94 was obtained across all datasets. The results showed how the similarity or disparity between data from different institutions can influence the performance of learning models.

Lung cancer remains the leading cause of cancer related deaths globally, responsible for approximately 1.8 million deaths each year. A key contributor to this high mortality rate is the late-stage diagnosis of the disease, underscoring the urgent need for effective early detection strategies. Low-dose computed tomography (CT) has shown great value in early screening, particularly when paired with clinical information. Clinical data, while valuable, lacks spatial and morphological insights essential for comprehensive evaluation. Combining both modalities offers a more holistic approach for lung cancer classification. This study presents AI-based methods for lung cancer classification using unimodal approaches - structured clinical data and chest CT imaging - alongside a novel multimodal deep learning framework that integrates both data types to classify lung nodules as malignant or benign. For the clinical modality, machine learning models including logistic regression, random forests, LightGBM, XGBoost, and multilayer perceptrons were evaluated with extensive hyperparameter tuning. In the imaging modality, ResNet18 and ResNet34 convolutional neural networks were used, with and without data augmentation. The study explored both intermediate and late fusion strategies to combine modality-specific representations. Results show that multimodal models consistently outperformed their unimodal counterparts, achieving a best-case area under the ROC curve (AUC) of 0.9138, with an accuracy of 0.8424 and an F1-score of 0.8422. These findings highlight the complementary strengths of imaging and clinical data and support the growing potential of multimodal deep learning in improving diagnostic accuracy in lung cancer classification.

Epidermal Growth Factor Receptor (EGFR) is one of the most frequently mutated genes in lung cancer. Its mutation status characterization is crucial for personalized treatment in Non-Small-Cell Lung Cancer (NSCLC). Biopsy is the gold standard for characterizing the EGFR mutation status. However, it is an invasive time-consuming method and is often burdensome or even impractical for some patients. Therefore, it is of utmost importance to identify alternative non-invasive methods for classifying this mutation. Computed Tomography (CT) images represent a non-invasive, safer and faster method to directly characterize lung cancer. This study developed a comprehensive radiomic approach for EGFR mutation classification using CT images, in which two preprocessing strategies were compared and five machine learning algorithms were evaluated across different datasets. We analyzed two independent datasets individually and combined, implementing lung containing nodule versus bounding box around nodule preprocessing approaches. Radiomic features were extracted using PyRadiomics and selected through Principal Component Analysis (PCA)$(65-95 \%$variance thresholds) and pairwise correlation filtering. The results demonstrated that the lung with nodule strategy achieved better and more consistent performance compared to the bounding box around the nodule method. The best performance$(\text{AUC}=0.780)$was achieved using Random Forest with correlation filtering. The results suggest that radiomics may be a potential support tool for EGFR classification when biopsy is not feasible or recommended. This would enable safer and more efficient personalized treatment. Nevertheless, the results underscore the need for larger, diverse datasets to improve model robustness for characterizing such complex and variable information before clinical integration.

Advancements in the care for patients with breast cancer have demanded the development of biomechanical breast models for the planning and risk mitigation of such invasive surgical procedures. However, these approaches require large amounts of high-quality magnetic resonance imaging (MRI) training data that is of difficult acquisition and availability. Although this can be solved using synthetic data, generating high resolution images comes at the price of very high computational constraints and tipically low performances. On the other hand, producing lower resolution samples yields better results and efficiency but falls short of meeting health professional standards. Therefore, this work aims to validate a joint approach between lower resolution generative models and the proposed superresolution architecture, titled Shifted Window Image Restoration (SWinIR), which was used to achieve a$4 x$increase in image size of breast cancer patient MRI samples. Results prove to be promising and to further expand upon the super-resolution state-of-the-art, achieving good maximum peak signal-to-noise ratio of 41.36 and structural similarity index values of 0.962 and thus beating traditional methods and other machine learning architectures.

3D human pose estimation aims to reconstruct the human skeleton of all the individuals in a scene by detecting several body joints. The creation of accurate and efficient methods is required for several real-world applications including animation, human–robot interaction, surveillance systems or sports, among many others. However, several obstacles such as occlusions, random camera perspectives, or the scarcity of 3D labelled data, have been hampering the models’ performance and limiting their deployment in real-world scenarios. The higher availability of cameras has led researchers to explore multi-view solutions due to the advantage of being able to exploit different perspectives to reconstruct the pose. Most existing reviews focus mainly on monocular 3D human pose estimation and a comprehensive survey only on multi-view approaches to determine the 3D pose has been missing since 2012. Thus, the goal of this survey is to fill that gap and present an overview of the methodologies related to 3D pose estimation in multi-view settings, understand what were the strategies found to address the various challenges and also, identify their limitations. According to the reviewed articles, it was possible to find that most methods are fully-supervised approaches based on geometric constraints . Nonetheless, most of the methods suffer from 2D pose mismatches, to which the incorporation of temporal consistency and depth information have been suggested to reduce the impact of this limitation, besides working directly with 3D features can completely surpass this problem but at the expense of higher computational complexity . Models with lower supervision levels were identified to overcome some of the issues related to 3D pose, particularly the scarcity of labelled datasets. Therefore, no method is yet capable of solving all the challenges associated with the reconstruction of the 3D pose. Due to the existing trade-off between complexity and performance, the best method depends on the application scenario. Therefore, further research is still required to develop an approach capable of quickly inferring a highly accurate 3D pose with bearable computation cost. To this goal, techniques such as active learning, methods that learn with a low level of supervision, the incorporation of temporal consistency, view selection, estimation of depth information and multi-modal approaches might be interesting strategies to keep in mind when developing a new methodology to solve this task.

Lung Cancer (LC) is still among the top main causes of death worldwide, and it is the leading death number among other cancers. Several AI-based methods have been developed for the early detection of LC, trying to use Computed Tomography (CT) images to identify the initial signs of the disease. The survival prediction could help the clinicians to adequate the treatment plan and all the proceedings, by the identification of the most severe cases that need more attention. In this study, several deep learning models were compared to predict the survival of LC patients using CT images. The best performing model, a CNN with 3 layers, achieved an AUC value of 0.80, a Precision value of 0.56 and a Recall of 0.64. The obtained results showed that CT images carry information that can be used to assess the survival of LC.

Currently, lung cancer is one of the deadliest diseases that affects millions of people globally. However, Artificial Intelligence is being increasingly integrated with healthcare practices, with the goal to aid in the early diagnosis of lung cancer. Although such methods have shown very promising results, they still lack transparency to the user, which consequently could make their generalised adoption a challenging task. Therefore, in this work we explore the use of post-hoc explainable methods, to better understand the inner-workings of an already established multitasking framework that executes the segmentation and the classification task of lung nodules simultaneously. The idea behind such study is to understand how a multitasking approach impacts the model’s performance in the lung nodule classification task when compared to single-task models. Our results show that the multitasking approach works as an attention mechanism by aiding the model to learn more meaningful features. Furthermore, the multitasking framework was able to achieve a better performance in regard to the explainability metric, with an increase of 7% when compared to our baseline, and also during the classification and segmentation task, with an increase of 4.84% and 15.03% for each task respectively, when also compared to the studied baselines.

The early and accurate detection and the grading characterization of brain cancer will generate a positive impact on the treatment plan of those patients. AI-based models can help analyze the Magnetic Resonance Imaging (MRI) to make an initial assessment of the tumor grading. The objective of this work was to develop an AI-based model to classify the grading of the tumor using the MRI. Two regions of interest were explored, with several levels of complexity for the neural network architecture, and two strategies to deal with imbalanced data. The best results were obtained for the most complex architecture (Resnet50) with a combination of weighted random sampler and data augmentation achieving a balanced accuracy of 62.26%. This work confirmed that complex problems required a more dense neural network and strategies to deal with the imbalanced data.

Amongst the different types of cancer, lung cancer is the one with the highest mortality rate and consequently, there is an urgent need to develop early detection methods to improve the survival probabilities of the patients. Due to the millions of deaths that are caused annually by cancer, there is large interest int the scientific community to developed deep learning models that can be employed in computer aided diagnostic tools.Currently, in the literature, there are several works in the Radiomics field that try to develop new solutions by employing learning models for lung nodule classification. However, in these types of application, it is usually required to extract the lung nodule from the input images, while using a segmentation mask made by a radiologist. This means that in a clinical scenario, to be able to employ the developed learning models, it is required first to manually segment the lung nodule. Considering the fact that several patients are attended daily in the hospital with suspicion of lung cancer, the segmentation of each lung nodule would become a tiresome task. Furthermore, the available algorithms for automatic lung nodule segmentation are not efficient enough to be used in a real application.In response to the current limitations of the state of the art, the proposed work attempts to evaluate a multitasking approach where both the segmentation and the classification task are executed in parallel. As a baseline, we also study a sequential approach where first we employ DL models to segment the lung nodule, corp the lung nodule from the input image and then finally, we classify the cropped nodule. Our results show that the multitasking approach is better than to sequentially execute the segmentation and classification task for lung nodule classification. For instances, while the multitasking approach was able to achieve an AUC of 84.49% in the classification task, the sequential approach was only able to achieve an AUC of 72.43%. These results show that the proposed multitasking approach can become a viable alternative to the classification and segmentation of lung nodules.

Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME.

PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

Microsatellite Instability (MSI) is an important biomarker in cancer patients, showing a defective DNA mismatch repair system. Its detection allows the use of immunotherapy to treat cancer, an approach that is revolutionizing cancer treatment. MSI is especially relevant for three types of cancer: Colon Adenocarcinoma (COAD), Stomach Adenocarcinoma (STAD), and Uterus corpus endometrial cancer (UCEC). In this work, learning algorithms were employed to predict MSI using RNA-seq data from The Cancer Genome Atlas (TCGA) database, with a focus on the selection of the most informative genomic features. The Multi-Layer Perceptron (MLP) obtained the best score (AUC = 98.44%), showing that it is possible to exploit information from RNA-seq data to find relevant relationships with the instability levels of microsatellites (MS). The accurate prediction of MSI with transcription data from cancer patients will help with the correct determination of MSI status and adequate prescription of immunotherapy, creating more precise and personalized patient care. At the genetic level, the study revealed a high expression of genes related to cell regulation functions, and a low expression of genes responsible for Mismatch Repair functions, in patients with high instability.

Due to the huge mortality rate of lung cancer, there is a strong need for developing solutions that help with the early diagnosis and the definition of the most appropriate treatment. In the particular case of target therapy, effective genotyping of the tumor is fundamental since this treatment uses targeted drugs that can induce death in cancer cells. The biopsy is the traditional method to assess the genotype information but it is extremely invasive and painful. Medical imaging is a valuable alternative to biopsies, considering the potential to extract imaging features correlated with specific genomic alterations. Regarding the limitations of single model approaches for gene mutation status predictions, ensemble strategies might bring valuable benefits by combining the strengths and weaknesses of the aggregated methods. This preliminary work aims to provide further advances in the radiogenomics field by studying the use of ensemble methods to predict the Epidermal Growth Factor Receptor (EGFR) mutation status in lung cancer. The best result obtained for the proposed ensemble approach was an AUC of 0.706 (± 0.122). However, the ensemble did not outperform the single models with AUC values of 0.712 (± 0.119) for Logistic Regression, 0.711 (± 0.119) for Support Vector Machine and 0.712 (± 0.120) for Elastic Net. The high correlation found on the decisions of each single model might be a plausible explanation for this behavior, which caused the ensemble to misclassify the same examples as the single models.

Conventionally, vital sign monitoring for neonatal infants inside the Neonatal Intensive Care Unit is performed via probes affixed to their skin. However, such instruments may cause damage to the epidermis and increase the risk of infection as well as promote discomfort to the infant. As an alternative to traditional means of monitoring heart rate, remote Photoplethysmography techniques have been surging among the scientific community. These techniques have been vastly explored for adult subjects but not for neonatal infants, who would greatly benefit from such applications. This study aims at developing a regular consumer camera-based framework for continuous and contactless extraction of the heart rate in adult subjects in challenging conditions and investigating the tool's ability to adapt to a new domain which consists of newborn subjects and the real-world conditions of a Neonatal Intensive Care Unit.

Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. The study of this disease using computer science tools resorts often to the image segmentation operation. Image segmentation, although having been extensively studied, is still an open problem. Shortest path algorithms are extensively used to tackle this problem. There are, however, applications where the starting and ending positions of the shortest path need to be constrained, defining a closed contour enclosing a previously detected seed. Mass and calcification segmentation in mammograms and areola segmentation in digital images are two particular examples of interest within the field of breast cancer research. Usually the closed contour computation is addressed by transforming the image into polar coordinates, where the closed contour is transformed into an open contour between two opposite margins. In this work, after illustrating some of the limitations of this approach, we show how to compute the closed contour in the original coordinate space. After defining a directed acyclic graph appropriate for this task, we address the main difficulty in operating in the original coordinate space. Since small paths collapsing in the seed point are naturally favored, we modulate the cost of the edges to counterbalance this bias. A thorough evaluation is conducted with datasets from the breast cancer field. The algorithm is shown to be fast and reliable and suffers no loss in resolution.

The medical procedures related with the Breast Cancer Conservative Treatment (BCCT) have evolved towards the usage of affordable and practical tools, along with the recent inclusion of volumetric information of the breast. A richer three-dimensional (3D) model of the female torso allows, for instance, improvement of the evaluation the aesthetic outcome of BCCT and the surgery planning. The standard 3D reconstruction methods often fail to model objects of interest using highly misaligned views. In this work, a Tessellation-based coarse registration method is proposed, based on robust keypoints extraction from RGB-D data using the Delaunay Triangulation (DT) principle. With this method, it is possible to reconstruct female torso data with detail using only 3 views, in feasible time. Structures such as the nipples and the breast contour were correctly reconstructed and a highly correlated with reference models.

Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. Surgeons and patients have often many options to consider for undergoing the procedure. The ability to visualise the potential outcomes of the surgery and make decisions on their surgical options is, therefore, very important for patients and surgeons. In this paper we investigate the fitting of a 3d point cloud of the breast to a parametric model usable in surgery planning, obtaining very promising results with real data.

The extraction of morphometric features from images of biological structures is a crucial task for the study of several diseases. Particularly, concerning neuropathies, the state of the myelination process is vital for neuronal integrity and may be an indicator of the disease type and state. Few approaches exist to automatically analyse nerve morphometry and assist researchers in this time consuming task. The aim of this work is to develop an algorithm to detect axons and myelin contours in myelinated fibres of sciatic nerve images, thus allowing the automated assessment and quantification of myelination through the measurement of the g-ratio. The application of a directional gradient together with an active contour algorithm was able to effectively and accurately determine the degree of myelination in an imagiological dataset of sciatic nerves. It was obtained an average error of 1.80%, in comparison with the manual annotation performed by the specialist in all dataset.

Biometric systems based on iris are vulnerable to several attacks, particularly direct attacks consisting on the presentation of a fake iris to the sensor. The development of iris liveness detection techniques is crucial for the deployment of iris biometric applications in daily life specially in the mobile biometric field. The 1stMobile Iris Liveness Detection Competition (MobILive) was organized in the context of IJCB2014 in order to record recent advances in iris liveness detection. The goal for (MobILive) was to contribute to the state of the art of this particular subject. This competition covered the most common and simple spoofing attack in which printed images from an authorized user are presented to the sensor by a non-authorized user in order to obtain access. The benchmark dataset was the MobBIOfake database which is composed by a set of 800 iris images and its corresponding fake copies (obtained from printed images of the original ones captured with the same handheld device and in similar conditions). In this paper we present a brief description of the methods and the results achieved by the six participants in the competition.

Animal behavior assessment plays an important role in basic and clinical neuroscience. Although assessing the higher functional level of the nervous system is already possible, behavioral tests are extremely complex to design and analyze. Animal's responses are often evaluated manually, making it subjective, extremely time consuming, poorly reproducible and potentially fallible. The main goal of the present work is to evaluate the use of consumer depth cameras, such as the Microsoft's Kinect, for detection of behavioral patterns of mice. The hypothesis is that the depth information, should enable a more feasible and robust method for automatic behavior recognition. Thus, we introduce our depth-map based approach comprising mouse segmentation, body-like per-frame feature extraction and per-frame classification given temporal context, to prove the usability of this methodology.

This paper presents two initiatives run by groups of engineering students at the University of Porto: the Microelectronics Students' Group and BioStar. These groups are student-led initiatives that promote different scientific fields through self-guided projects. Both experiences have proven to be very successful in increasing the undergraduate student's interest in science and technology. This work reports the activities, organization and main methodologies employed by these groups, which can be seen as successful approaches to enhance the technical curriculum of students.

Breast Cancer Conservative Treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as Breast Cancer Conservative Treatment.cosmetic results (BCCT.core) software tool. Although the satisfying results, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system as a low-cost tool, the incorporation of the Microsoft Kinect sensor in BCCT evaluations was studied. The aim with this work is to enable the simultaneous detection of breast contour and breast peak points using depth-map data. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. Additionally, comparatively to previous research, the procedure for detecting prominent points was automated.

Automatic biometric identification based on fingerprints is still one of the most reliable identification method in criminal and forensic applications. A critical step in fingerprint analysis without human intervention is to automatically and reliably extract singular points from the input fingerprint images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. Poincare¿ Index-based methods are one of the most common for singular points detection. However, these methods usually result in many spurious detections. Therefore, we propose an enhanced version of the method presented by Zhou et al. [13] that introduced a feature called DORIC to improve the detection. Our principal contribution lies in the adoption of a smoothed orientation field and in the formulation of a new algorithm to analyze the DORIC feature. Experimental results show that the proposed algorithm is accurate and robust, giving better results than the best reported results so far, with improvements in the range of 5% to 7%.

This paper describes the sensor and actuator modeling of a realistic wheeled mobile robot simulator. The motivation of developing such simulator is to produce a personalized versatile tool that allows production and validation of robot software reducing considerably the development time. The mobile robot simulator was developed in object pascal with its dynamics based on the ODE (open dynamics engine), allowing to develop robot software for a three wheel omnidirectional robot equipped with Infra-Red distance sensors and brushless motors.