Potential Obstacle Detection Using RGB to Depth Image Encoder–Decoder Network: Application to Unmanned Aerial Vehicles

In this work, a new method is proposed that allows the use of a single RGB camera for the real-time detection of objects that could be potential collision sources for Unmanned Aerial Vehicles. For this purpose, a new network with an encoder–decoder architecture has been developed, which allows rapid distance estimation from a single image by performing RGB to depth mapping. Based on a comparison with other existing RGB to depth mapping methods, the proposed network achieved a satisfactory trade-off between complexity and accuracy. With only 6.3 million parameters, it achieved efficiency close to models with more than five times the number of parameters. This allows the proposed network to operate in real time. A special algorithm makes use of the distance predictions made by the network, compensating for measurement inaccuracies. The entire solution has been implemented and tested in practice in an indoor environment using a micro-drone equipped with a front-facing RGB camera. All data and source codes and pretrained network weights are available to download. Thus, one can easily reproduce the results, and the resulting solution can be tested and quickly deployed in practice

Komputerowe generowanie dynamicznych map perfuzji mózgu, ich analiza i znaczenie w neuroradiologii Computer generation of dynamic brain perfusion maps and they application in neuroradiology /

Tyt. z nagłówka.Bibliografia s. 35-[36].Dostępny również w formie drukowanej.STRESZCZENIE: W artykule szczegółowo zaprezentowano sposoby komputerowego generowania dynamicznych map perfuzji mózgu uzyskiwanych w trakcie badań technikami CT (Computer Tomography) oraz MR-DSC (Magnetic Rezonanse Dynamic Susceptibility Contrast Imaging). W szczególności omówione zostało znaczenie poszczególnych parametrów dynamicznej perfuzji struktur mózgowia, sposób konstruowania krzywych wzmocnienia kontrastowego (Time Density Curre), prawo dyfuzji Ficka, pomiar ilości krwi przepływającej przez mózg przy użyciu niedyfundującego wskaźnika, w oparciu o konwolucyjny model Meiera-Zierlera, sposób przeprowadzenia dekonwolucji za pomocą rozkładu na wartości osobliwe (SVD), oraz konstrukcję map CBF, CBV, MTT i TTP (Cerebral Blond Flow, Cerebral Blood Volume, Mean Transit Time, Time to Peak). Praca zawiera również porównanie wyników otrzymanych przy wykorzystaniu różnych pakietów oprogramowania komercyjnego oraz darmowego pozwalającego na akwizycję danych pomiarowych oraz generację map perfuzyjnych. W ostatniej części pracy zaprezentowano obszar zastosowań dynamicznej perfuzji CTw neuroradiologii oraz opis, w jaki sposób podejmuje się diagnozę medyczną za pomocą analizy mapy na przykładzie rzeczywistych przypdków medycznych. SŁOWA KLUCZOWE: dynamiczna perfuzja ct, konwolucyjny model meiera-zierlera, mapy perfuzyjne. ABSTRACT: This paper presents detailed process of generation dynamic perfusion CT and MR-DSC (Magnetic Rezonanse Dynamic Susceptibility Contrast Imaging) maps. It also describes the meaning of all perfusion parameters, the way to construct time density curves (TDC), the Fick diffusion principle, the method for estimating cerebral blood flow with non diffusing contrast agent based on Meier-Zierler convolution model, the deconvolution calculation based on singular value decomposition (SVD), and CBF, CBV, MTT and TTP (Cerebral Blond Flow, Cerebral Blood Volume, Mean Transit Time, Time to Peak), maps construction. The paper consist also comparison of perfusion maps obtained from various commercial and free software. In the last part of this paper the field of usage of dynamic perfusion CT in neuroradiology is presented. There are also some examples in showing the way in which the diagnosis based on perfusion map analysis is statement. KEYWORDS: dynamic ct perfusion, meier-zierler convultion model, perfusion maps

Techniki rozpoznawania obrazów w zadaniach interpretacji znaczeniowej zmian perfuzji krwi tkanki mózgowej rozprawa doktorska /

Tyt. z ekranu tytułowego.Praca doktorska. Akademia Górniczo-Hutnicza im. Stanisława Staszica (Kraków), 2010.Zawiera bibliogr.Dostępna także w wersji drukowanej.Tryb dostępu: Internet.Wybrane wiadomości na temat diagnostyki mózgowia za pomocą zobrazowań perfuzyjnych, choroby naczyniopochodne ośrodkowego układu nerwowego, techniki diagnozowania chorób naczyniopochodnych ośrodkowego układu nerwowego, OUN, techniki dopplerowskie, USG dopplerowskie, tomografia komputerowa, TK, cyfrowa angiografia substrakcyjna, DSA, Digital Subtraction Angiography, angiografia tomografii komputerowej, rezonansu magnetycznego, angio TK, angio RM, tomografia rezonansu magnetycznego, RM, badania radioizotopowe, SPECT, PET, badanie perfuzji mózgowej, z dyfundującym, niedyfundującym wskaźnikiem kontrastowym, dynamiczna perfuzja TK, model Meiera – Zierlaya, diagnozowanie na podstawie map perfuzyjnych, prognostyczne znaczenie map perfuzji mózgowej dynamicznej pTK, obecny stan wiedzy na temat automatycznych metod diagnostyki zobrazowań perfuzyjnych mózgu, charakterystyka zbioru badawczego, algorytm automatycznego wykrywania potencjalnych zmian chorobowych na mapach perfuzji mózgowej, detekcja osi symetrii, asymetrii na mapach CBF, CBV, wyznaczanie wartości parametrów perfuzji, ocena skuteczności działania algorytmu detekcji asymetrii, techniki dopasowania zobrazowań, metody transformacji globalnej, transformacja afiniczna, metody z rzadką, gęstą siatką, szybka aproksymacja filtru gaussowskiego dla algorytmu Thiriona, inne metody, budowa deformowalnego atlasu mózgu, atlas Talairach, tworzenie wzorcowych przekrojów CT, ich opisów, dobór optymalnego algorytmu dopasowania obrazu, algorytmy oceny znaczenia zmian chorobowych, określenie typu zmiany chorobowej, możliwości określenia rokowań dla chorych tkanek, badanie skuteczności algorytmów rozpoznawania, architektura systemu analizującego zobrazowania diagnostyczne pTK, implementacja systemu DM

Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an internal measurement unit (IMU) sensor that has 6-axis accelerometer and gyroscope. We will validate a classifier that uses Principal Components Analysis (PCA)-based features with various numbers of dimensions, a two-stage PCA-based method, a feedforward artificial neural network, and random forest. Moreover, we will also propose a Dynamic Time Warping (DTW) classifier trained with extension of DTW Barycenter Averaging (DBA) algorithm that utilizes quaternion averaging and a bagged variation of previous method (DTWb) that utilizes many DTW classifiers that perform voting. The evaluation has been performed on 975 head gesture recordings in seven classes acquired from 12 persons. The highest value of recognition rate in a leave-one-out test has been obtained for DTWb and it equals 0.975 (0.026 better than the best of state-of-the-art methods to which we have compared our approach). Among the most important applications of the proposed method is improving life quality for people who are disabled below the neck by supporting, for example, an assistive autonomous power chair with a head gesture interface or remote controlled interfaces in robotics

Image Hashtag Recommendations Using a Voting Deep Neural Network and Associative Rules Mining Approach

Hashtag-based image descriptions are a popular approach for labeling images on social media platforms. In practice, images are often described by more than one hashtag. Due the rapid development of deep neural networks specialized in image embedding and classification, it is now possible to generate those descriptions automatically. In this paper we propose a novel Voting Deep Neural Network with Associative Rules Mining (VDNN-ARM) algorithm that can be used to solve multi-label hashtag recommendation problems. VDNN-ARM is a machine learning approach that utilizes an ensemble of deep neural networks to generate image features, which are then classified to potential hashtag sets. Proposed hashtags are then filtered by a voting schema. The remaining hashtags might be included in a final recommended hashtags dataset by application of associative rules mining, which explores dependencies in certain hashtag groups. Our approach is evaluated on a HARRISON benchmark dataset as a multi-label classification problem. The highest values of our evaluation parameters, including precision, recall, and accuracy, have been obtained for VDNN-ARM with a confidence threshold 0.95. VDNN-ARM outperforms state-of-the-art algorithms, including VGG-Object + VGG-Scene precision by 17.91% as well as ensemble–FFNN (intersection) recall by 32.33% and accuracy by 27.00%. Both the dataset and all source codes we implemented for this research are available for download, and our results can be reproduced

Evaluation of Carotid Artery Segmentation with Centerline Detection and Active Contours without Edges Algorithm

Part 2: WorkshopInternational audienceThe main contribution of this article is a new method of segmentation of carotid artery based on original authors inner path finding algorithm and active contours without edges segmentation method for vessels wall detection. Instead of defining new force to being minimized or intensity metric we decide to find optimal weight of image – dependent forces. This allows our method to be easily reproduced and applied in other software solutions. We judge the quality of segmentation by dice coefficient between manual segmentation done by a specialist and automatic segmentation performed by our algorithm. We did not find any other publication in which such approach for carotid artery bifurcation region segmentation has been proposed or investigated. The proposed algorithm has shown to be reliable method for that task. The dice coefficient at the level of 0.949(0.050 situates our algorithm among best state of the art methods for those solutions. That type of segmentation is the main step performed before sophisticated semantic analysis of complex image patterns utilized by cognitive image and scene understanding methods. The complete diagnostic record (Electronic Health Record – EHR) obtained that way consists private biometric data and its safety is essential for personal and homeland security

Augmented Reality Approaches in Intelligent Health Technologies and Brain Lesion Detection

Part 2: WorkshopInternational audienceIn this paper authors present their new proposition of system for cognitive analysis of dynamic computer tomography perfusion maps (dpCT). The novel contribution of this article is introducing an augmented reality visualization module that supports real time volume rendering (VR) of derived data. Authors also presents the results of their researches on optimization of VR algorithm memory usage by dynamic computation of volume gradient instead of pre-generation of gradient Authors compare five different discrete gradient computation schemas taking into account image quality and processing speed on two VR algorithms: volume ray casting and texture based visualization with view aligned slices

An evaluation of machine learning and latent semantic analysis in text sentiment classification

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public