Skin Lesion Extraction And Its Application

In this thesis, I study skin lesion detection and its applications to skin cancer diagnosis. A skin lesion detection algorithm is proposed. The proposed algorithm is based color information and threshold. For the proposed algorithm, several color spaces are studied and the detection results are compared. Experimental results show that YUV color space can achieve the best performance. Besides, I develop a distance histogram based threshold selection method and the method is proven to be better than other adaptive threshold selection methods for color detection. Besides the detection algorithms, I also investigate GPU speed-up techniques for skin lesion extraction and the results show that GPU has potential applications in speeding-up skin lesion extraction. Based on the skin lesion detection algorithms proposed, I developed a mobile-based skin cancer diagnosis application. In this application, the user with an iPhone installed with the proposed application can use the iPhone as a diagnosis tool to find the potential skin lesions in a persons\u27 skin and compare the skin lesions detected by the iPhone with the skin lesions stored in a database in a remote server

Fast GPU Accelerated Stereo Correspondence for Embedded Surveillance Camera Systems

Many surveillance applications could benefit from the use of stereo cam- eras for depth perception. While state-of-the-art methods provide high quality scene depth information, many of the methods are very time consuming and not suitable for real-time usage in limited embedded systems. This study was conducted to examine stereo correlation methods to find a suitable algorithm for real-time or near real-time depth perception through disparity maps in a stereo video surveillance camera with an embedded GPU. Moreover, novel refinements and alternations was investigated to further improve performance and quality. Quality tests were conducted in Octave while GPU suitability and performance tests were done in C++ with the OpenGL ES 2.0 library. The result is a local stereo correlation method using Normalized Cross Correlation together with sparse support windows and a suggested improvement for pixel-wise matching confidence. Applying sparse support windows increased frame rate by 35% with minimal quality penalty as compared to using full support windows

On the Hardware/Software Design and Implementation of a High Definition Multiview Video Surveillance System

published_or_final_versio

Large-Scale Image Processing Using MapReduce

Jälgides tänapäeva tehnoloogia arengut ning odavate fotokaamerate üha laialdasemat levikut, on üha selgem, et ühe osa üha kasvavast inimeste tekitatud andmete hulgast moodustavad pildid. Teades, et tõenäoliselt tuleb neid andmeid ka töödelda, ning et üksikute arvutite võimsus ei luba kohati juba praegu neid mahukamate ülesannete jaoks kasutada, on inimesed hakanud uurima mitmete hajusarvutuse mudelite pakutavaid võimalusi. Üks selline on MapReduce, mille põhiliseks aluseks on arvutuste üldisele kujule viimine, seades programmeerija ülesandeks defineerida vaid selle, mis toimub andmetega nelja arvutuse faasi - Input, Map, Reduce, Output - jooksul. Kuna sellest mudelist on olemas kvaliteetseid vabavara realisatsioone, ning mahukamateks arvutusteks on kerge vaeva ja vähese kuluga võimalik rentida vajalik infrastruktuur, siis on selline lähenemine pilditöötlusele muutunud peaaegu igaühele kättesaadavaks. Antud magistritöö eesmärgiks on uurida MapReduce mudeli kasutatavust suuremahulise pilditöötluse vallas. Selleks vaatlen eraldi juhte, kus tegemist on tavalistest piltidest koosneva suure andmestikuga, ning kus tuleb töödelda ühte suuremahulist pilti. Samuti jagan nelja klassi vahel kõik pilditöötlusalgoritmid, nimetades need vastavalt lokaalseteks, iteratiivseteks lokaalseteks, mittelokaalseteks ja iteratiivseteks mittelokaalseteks algoritmideks. Kasutades neid jaotusi, kirjeldan üldiselt põhilisi probleeme ja takistusi, mis võivad segada mingit tüüpi algoritmide hajusat rakendamist mingit tüüpi piltandmetel, ning pakun välja võimalikke lahendusi. Töö praktilises osas kirjeldan MapReduce mudeli kasutamist Apache Hadoop raamistikuga kahel erineval andmestikul, millest esimene on 265GiB-suurune pildikogu, ning teine 6.99 gigapiksli suurune mikroskoobifoto. Esimese näite puhul on ülesandeks pildikogust meta-andmete eraldamine, kasutades selleks objekti- ning tekstituvastust. Teise andmestiku puhul on ülesandeks töödelda pilti ühe kindla mitteiteratiivse lokaalse algoritmiga. Kuigi mõlemal juhul on tegemist vaid katsetamise eesmärgil loodud rakendustega, on mõlemal puhul näha, et olemasolevate pilditöötluse algoritmide MapReduce programmideks teisendamine on küllaltki lihtne, ning ei too endaga kaasa suuri kadusid jõudluses. Kokkuvõtteks väidan, et tavapärases mõõdus piltidest koosnevate andmestike puhul on MapReduce mudel lihtne viis arvutusi hajusale kujule viies kiirendada, kuid suuremahuliste piltide puhul kehtib see enamasti ainult mitteiteratiivsete lokaalsete algoritmidega.Due to the increasing popularity of cheap digital photography equipment, personal computing devices with easy to use cameras, and an overall im- provement of image capture technology with regard to quality, the amount of data generated by people each day shows trends of growing faster than the processing capabilities of single devices. For other tasks related to large-scale data, humans have already turned towards distributed computing as a way to side-step impending physical limitations to processing hardware by com- bining the resources of many computers and providing programmers various different interfaces to the resulting construct, relieving them from having to account for the intricacies stemming from it’s physical structure. An example of this is the MapReduce model, which - by way of placing all calculations to a string of Input-Map-Reduce-Output operations capable of working in- dependently - allows for easy application of distributed computing for many trivially parallelised processes. With the aid of freely available implemen- tations of this model and cheap computing infrastructure offered by cloud providers, having access to expensive purpose-built hardware or in-depth un- derstanding of parallel programming are no longer required of anyone who wishes to work with large-scale image data. In this thesis, I look at the issues of processing two kinds of such data - large data-sets of regular images and single large images - using MapReduce. By further classifying image pro- cessing algorithms to iterative/non-iterative and local/non-local, I present a general analysis on why different combinations of algorithms and data might be easier or harder to adapt for distributed processing with MapReduce. Finally, I describe the application of distributed image processing on two ex- ample cases: a 265GiB data-set of photographs and a 6.99 gigapixel image. Both preliminary analysis and practical results indicate that the MapReduce model is well suited for distributed image processing in the first case, whereas in the second case, this is true for only local non-iterative algorithms, and further work is necessary in order to provide a conclusive decision

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

About the development of visual search algorithms and their hardware implementations

2015 - 2016The main goal of my work is to exploit the benefits of a hardware implementation of a 3D visual search pipeline. The term visual search refers to the task of searching objects in the environment starting from the real world representation. Object recognition today is mainly based on scene descriptors, an unique description for special spots in the data structure. This task has been implemented traditionally for years using just plain images: an image descriptor is a feature vector used to describe a position in the images. Matching descriptors present in different viewing of the same scene should allows the same spot to be found from different angles, therefore a good descriptor should be robust with respect to changes in: scene luminosity, camera affine transformations (rotation, scale and translation), camera noise and object affine transformations. Clearly, by using 2D images it is not possible to be robust with respect to the change in the projective space, e.g. if the object is rotated with respect to the up camera axes its 2D projection will dramatically change. For this reason, alongside 2D descriptors, many techniques have been proposed to solve the projective transformation problem using 3D descriptors that allow to map the shape of the objects and consequently the surface real appearance. This category of descriptors relies on 3D Point Cloud and Disparity Map to build a reliable feature vector which is invariant to the projective transformation. More sophisticated techniques are needed to obtain the 3D representation of the scene and, if necessary, the texture of the 3D model and obviously these techniques are also more computationally intensive than the simple image capture. The field of 3D model acquisition is very broad, it is possible to distinguish between two main categories: active and passive methods. In the active methods category we can find special devices able to obtain 3D information projecting special light and. Generally an infrared projector is coupled with a camera: while the infrared light projects a well known and fixed pattern, the camera will receive the information of the patterns reflection on a certain surface and the distortion in the pattern will give the precise depth of every point in the scene. These kind of sensors are of i i “output” — 2017/6/22 — 18:23 — page 3 — #3 i i i i i i 3 course expensive and not very efficient from the power consumption point of view, since a lot of power is wasted projecting light and the use of lasers also imposes eye safety rules on frame rate and transmissed power. Another way to obtain 3D models is to use passive stereo vision techniques, where two (or more) cameras are required which only acquire the scene appearance. Using the two (or more) images as input for a stereo matching algorithm it is possible to reconstruct the 3D world. Since more computational resources will be needed for this task, hardware acceleration can give an impressive performance boost over pure software approach. In this work I will explore the principal steps of a visual search pipeline composed by a 3D vision and a 3D description system. Both systems will take advantage of a parallelized architecture prototyped in RTL and implemented on an FPGA platform. This is a huge research field and in this work I will try to explain the reason for all the choices I made for my implementation, e.g. chosen algorithms, applied heuristics to accelerate the performance and selected device. In the first chapter we explain the Visual Search issues, showing the main components required by a Visual Search pipeline. Then I show the implemented architecture for a stereo vision system based on a Bio-informatics inspired approach, where the final system can process up to 30fps at 1024 × 768 pixels. After that a clever method for boosting the performance of 3D descriptor is presented and as last chapter the final architecture for the SHOT descriptor on FPGA will be presented. [edited by author]L’obiettivo principale di questo lavoro e’ quello di esplorare i benefici di una implementazione hardware per una pipeline di visual search 3D. Il termine visual search si riferisce al problema di ricerca di oggetti nell’ambiente. L’object recognition ai giorni nostri e’ principalmente basato sull’uso di descrittori della scena, una descrizione univoca per i punti salienti. Questo compito e’ stato implementato per anni utilizzando immagini: il descrittore di un punto dell’immagine e’ un semplice vettore di caratteristiche. Accoppiando i descrittori presenti in differenti viste della stessa scena permette di trovare punti nello spazio visibili da entrambe le viste. Chiaramente, utilizzando immagini 2D non e’ possibile avere descrittori che sono robusti a cambiamenti della prospettiva, per questo motivo, molte tecniche sono state proposte per risolvere questo problema utilizzando descrittori 3D. Questa categoria di descrittori si avvale di 3D point cloud e mappe di disparita’. Ovviamente tecniche piu’ sofisticate sono necessarie per ottenere la rappresentazione 3D della scena. Il campo dell’acquisizione 3D e’ molto vasto ed e’ possibile distinguere tra due categorie di sensori: sensori attivi e passivi. Tra i sensori attivi possiamo annoverare dispositivi in grado di proiettare un pattern di luce infrarossa sulla scena, questo pattern noto presenta delle variazioni dovute agli oggetti presenti nella scena. Una camera infrarossi riceve l’immagine distorta del pattern e deduce la geometria della scena. Questo tipo di dispositivi non sono molto efficienti dal punto di vista energetico dato che un sacco di corrente viene consumata per proiettare il pattern. Un altro modo per ottenere un modello 3D e’ quello di usare sensori passivi, una coppia di telecamere puo’ essere utilizzata per ottenere informazioni utilizzando metodi di triangolazione. Questi metodi pero’ richiedono un sacco di potenza computazionale nel caso di applicazioni real time, per questo motivo e’ necessario utilizzare dispositivi ad-hoc quali architetture hardware dedicate implementate mediante l’uso di FPGA e ASIC. In questo lavoro ho esplorato gli step principali di una pipeline per la visual search composta da un sistema di visione 3D e uno per la descrizione di punti. Entrambi i sistemi si avvalgono di achitetture hardware dedicate prototipate in RTL e implementate su FPGA. Questo e’ un grosso campo di lavoro e provo ad esplorare i benefici di una implementazione harwadere per l’accelerazione degli algoritmi stessi e il risparmi di energia elettrica. [a cura dell'autore]XV n.s