Decompilation from Selected Object File Formats

Objektové súbory obsahujú strojový kód, ktorý môže byť vykonaný procesorom. Každý objektový súbor má formát, ktorý popisuje jeho štruktúru. Pre vykonanie spätného prekladu je nutné súbor spracovať a previesť dáta do vnútornej reprezentácie spätného prekladača. Táto práca pojednáva o návrhu a implementácii nových modulov pre podporu spracovania formátov, ktoré budú súčasťou Rekonfigurovateľného spätného prekladača. Cieľom práce je pridanie podpory pre formáty Intel HEX a Mach-O a nová implementácia už podporovaného formátu Portable Executable. Implementácia modulov pre Intel HEX a Mach-O bola úspešná a je možné použiť ich pre spätný preklad. Spracovanie formátu PE nedosahuje dostatočnej kvality kvôli chybám knižnice LLVM, na ktorej je implementácia založená.Object files contain machine code that can be executed by processor unit. Structure of an object file is defined by its file format. In order to decompile an object file, it is necessary to process and convert file data to internal representation of decompiler. This thesis discusses design and implementation of new modules for file format processing that will be part of the Retargetable Decompiler project. The goal of this work is to add support for Intel HEX and Mach-O file formats and new implementation of already supported Portable Executable file format. Implementation of modules for file formats Intel HEX and Mach-O was successful and it is possible to use them for reverse compilation. Processing of PE file format is not possible in sufficient quality due to errors in used LLVM library.

New Statistics for Texture Classification Based on Gabor Filters

The paper introduces a new method of texture segmentation efficiency evaluation. One of the well known texture segmentation methods is based on Gabor filters because of their orientation and spatial frequency character. Several statistics are used to extract more information from results obtained by Gabor filtering. Big amount of input parameters causes a wide set of results which need to be evaluated. The evaluation method is based on the normal distributions Gaussian curves intersection assessment and provides a new point of view to the segmentation method selection

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion