High Level Synthesis (HLS) is a new method for developing applications for use on FPGAs. Instead of the classic approach using a Hardware Descriptive Language (HDL), a high level programming language can be used. HLS has many perks, including high level debugging and simulation of the system being developed. This shortens the development time which in turn lowers the development cost. In this thesis an evaluation is made regarding the feasibility of using SDAccel as the HLS tool in the OpenCL environment. Two image processing algorithms are implemented using OpenCL C and then synthesized to run on a Kintex Ultrascale FPGA. The implementation focuses both on low latency and throughput as the target environment is a video distribution network used in vehicles. The network provides the driver with video feeds from cameras mounted on the vehicle. Finally the test result of the algorithm runs are presented, displaying how well the HLS tool has preformed in terms of system performance and FPGA resource utilization.

The attention for low latency computer vision and video processing applications are growing for every year, not least the VR and AR applications. In this thesis the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Radial Dis- tortion algorithms are implemented using both CUDA and OpenCL to determine whether these type of algorithms are suitable for implementations aimed to run at GPUs when low latency is of utmost importance. The result is an implemen- tation of the block versions of the CLAHE algorithm which utilizes the built in interpolation hardware that resides on the GPU to reduce block effects and an im- plementation of the Radial Distortion algorithm that corrects a 1920x1080 frame in 0.3 ms. Further this thesis concludes that the GPU-platform might be a good choice if the data to be processed can be transferred to and possibly from the GPU fast enough and that the choice of compute API mostly is a matter of taste.

AbstractThe mapping of habitats is the first step that is done in policies that target theenvironment, as well as in spatial planning and management. The biodiversityplans are always centered around habitats. Therefore, constant monitoring ofthese delicate species in terms of health, changes, and extinction is a must inbiodiversity plans. Human activities are constantly growing, resulting in theextinction of land and marine habitats. Land habitats are being destroyed using airpollution and the cutting of forests. At the same time, marine habitats are beingdestroyed due to acidification of ocean waters and waste materials from theindustries and pollution. The author has focused on aquatic habitats in thisdissertation, mainly coral reefs. An estimate of 27% of coral reef ecosystems havebeen destroyed, and a further 30% are at risk of being damaged in the comingyears. Coral reefs occupy 1% of the ocean floor, and yet they provide a home to30% of marine organisms. To analyze the health of these aquatic habitats, theyneed to be assessed through habitat mapping. Habitat mapping shows thegeographic distribution of different habitats within a particular area. Marinehabitats are typically mapped using camera imagery. The quality of underwaterimages suffers from the characteristics of the marine environment. This results inblurry images or containing particles that cover many parts of an image. Toovercome this, underwater image enhancement algorithms are used to preprocessimages beforehand. Now, there are many underwater image enhancementalgorithms that target different characteristics of the marine environment, butthere is no consensus among researchers about a single underwater technique thatcan be used for any marine dataset. In this dissertation, multiple experiments onvarious popular image enhancement techniques (seven) were conducted and usedto reach a decision about a single underwater approach for all datasets. Thedatasets include EILAT, EILAT2, RSMAS, and MLC08. Also, two state-of-the-artdeep convolutional neural networks for habitat mapping, i.e., DenseNet andMobileNet tested. Maximum results from the combination of Contrast LimitedAdaptive Histogram Equalization (CLAHE) achieved as underwater imageenhancement technique and DenseNet as deep convolutional network. / Not applicable

In this study, the impact of multiple image preprocessing methods on Convolutional Neural Networks (CNN) was studied. Metrics such as accuracy, precision, recall and F1-score (Hossin et al. 2011) were evaluated. Specifically, this study is geared towards malaria classification using the data set made available by the U.S. National Library of Medicine (Malaria Datasets n.d.). This data set contains images of thin blood smears, where uninfected and parasitized blood cells have been segmented. In the study, 3 CNN models were proposed for the parasitization classification task. Each model was trained on the original data set and 4 preprocessed data sets. The preprocessing methods used to create the 4 data sets were grayscale, normalization, histogram equalization and contrast limited adaptive histogram equalization (CLAHE). The impact of CLAHE preprocessing yielded a 1.46% (model 1) and 0.61% (model 2) improvement over the original data set, in terms of F1-score. One model (model 3) provided inconclusive results. The results show that CNN’s can be used for parasitization classification, but the impact of preprocessing is limited. / I denna studie studerades effekten av flera bildförbehandlingsmetoder på Convolutional Neural Networks (CNN). Mätvärden såsom accuracy, precision, recall och F1-score (Hossin et al. 2011) utvärderades. Specifikt är denna studie inriktad på malariaklassificering med hjälp av ett dataset som tillhandahålls av U.S. National Library of Medicine (Malaria Datasets n.d.). Detta dataset innehåller bilder av tunna blodutstryk, med segmenterade oinfekterade och parasiterade blodceller. I denna studie föreslogs 3 CNN-modeller för parasiteringsklassificeringen. Varje modell tränades på det ursprungliga datasetet och 4 förbehandlade dataset. De förbehandlingsmetoder som användes för att skapa de 4 dataseten var gråskala, normalisering, histogramutjämning och kontrastbegränsad adaptiv histogramutjämning (CLAHE). Effekten av CLAHE-förbehandlingen gav en förbättring av 1.46% (modell 1) och 0.61% (modell 2) jämfört med det ursprungliga datasetet, vad gäller F1-score. En modell (modell 3) gav inget resultat. Resultaten visar att CNN:er kan användas för parasiteringsklassificering, men effekten av förbehandling är begränsad.

From biometrical point of view, main features of retina are fovea, optic nerve and blood vessel tree. Blood vessel tree is unique for each person and this biological feature is used in biometric systems for person-recognition by retinal images. This document describes methods for optic disc and fovea localization, method for vessel tree segmentation, which is based on well-known \emph{Matched filters} method and also describes method for localization of blood vessel bifurcations. Main goal of this thesis is creation of program which can automatically preprocess input image, segment blood vessels and localize vessel bifircations. The program is implemented in Java with OpenCV library.