DESIGN AND IMPLEMENTATION OF AN EFFICIENT IMAGE COMPRESSOR FOR WIRELESS CAPSULE ENDOSCOPY

Capsule endoscope (CE) is a diagnosis tool for gastrointestinal (GI) diseases. Area and power are the two important parameters for the components used in CE. To optimize these two parameters, an efficient image compressor is desired. The mage compressor should be able to sufficiently compress the captured images to save transmission power, retain reconstruction quality for accurate diagnosis and consumes small physical area. To meet all of the above mentioned conditions, we have studied several transform coding based lossy compression algorithms in this thesis. The core computation tool of these compressors is the Discrete Cosine Transform (DCT) kernel. The DCT accumulates the distributed energy of an image in a small centralized area and supports more compression with non-significant quality degradation. The conventional DCT requires complex floating point multiplication, which is not feasible for wireless capsule endoscopy (WCE) application because of its high implementation cost. So, an integer version of the DCT, known as iDCT, is used in this work. Several low complexity iDCTs along with different color space converters (such as, YUV, YEF, YCgCo) were combined to obtain the desired compression level. At the end a quantization stage is used in the proposed algorithm to achieve further compression. We have analyzed the endoscopic images and based on their properties, three quantization matrix sets have been proposed for three color planes. The algorithms are verified at both software (using MATLAB) and hardware (using HDL Verilog coding) levels. In the end, the performance of all the proposed schemes has been evaluated for optimal operation in WCE application

PhotoCloud: navigazione 3D di ambienti ricostruiti a partire da foto

La tesi propone un sistema interattivo per la navigazione virtuale di ambienti tridimensionali ricostruiti a partire da sequenze di immagini. Il sistema integra in una interfaccia innovativa dati tridimensionali e fotografici e per mezzo di tecniche di streaming permette la navigazione in remoto dei dataset ricostruiti

Semi-supervised image classification based on a multi-feature image query language

The area of Content-Based Image Retrieval (CBIR) deals with a wide range of research disciplines. Being closely related to text retrieval and pattern recognition, the probably most serious issue to be solved is the so-called \semantic gap". Except for very restricted use-cases, machines are not able to recognize the semantic content of digital images as well as humans. This thesis identifies the requirements for a crucial part of CBIR user interfaces, a multimedia-enabled query language. Such a language must be able to capture the user's intentions and translate them into a machine-understandable format. An approach to tackle this translation problem is to express high-level semantics by merging low-level image features. Two related methods are improved for either fast (retrieval) or accurate(categorization) merging. A query language has previously been developed by the author of this thesis. It allows the formation of nested Boolean queries. Each query term may be text- or content-based and the system merges them into a single result set. The language is extensible by arbitrary new feature vector plug-ins and thus use-case independent. This query language should be capable of mapping semantics to features by applying machine learning techniques; this capability is explored. A supervised learning algorithm based on decision trees is used to build category descriptors from a training set. Each resulting \query descriptor" is a feature-based description of a concept which is comprehensible and modifiable. These descriptors could be used as a normal query and return a result set with a high CBIR based precision/recall of the desired category. Additionally, a method for normalizing the similarity profiles of feature vectors has been developed which is essential to perform categorization tasks. To prove the capabilities of such queries, the outcome of a semi-supervised training session with \leave-one-object-out" cross validation is compared to a reference system. Recent work indicates that the discriminative power of the query-based descriptors is similar and is likely to be improved further by implementing more recent feature vectors.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

PGF: a new progressive file format for lossy and lossless image compression

We present a new image file format, called Progressive Graphics File (PGF), which is based on a discrete wavelet transform with progressive coding features. We show all steps of a transform based coder in detail and discuss some important aspects of our careful implementation. PGF can be used for lossless and lossy compression. It performs best for natural images and aerial ortho-photos. For these types of images it shows in its lossy compression mode a better compression efficiency than JPEG. This efficiency gain is almost for free, because the encoding and decoding times are only marginally longer. We also compare PGF with JPEG 2000 and show that JPEG 2000 is about ten times slower than PGF. In its lossless compression mode PGF has a slightly worse compression efficiency than JPEG 2000, but a clearly better compression efficiency than JPEG-LS and PNG. If both, compression efficiency and runtime, is important, then PGF is the best of the tested algorithms for compression of natural images and aerial photos

PGF: a new progressive file format for lossy and lossless image compression

We present a new image file format, called Progressive Graphics File (PGF), which is based on a discrete wavelet transform with progressive coding features. We show all steps of a transform based coder in detail and discuss some important aspects of our careful implementation. PGF can be used for lossless and lossy compression. It performs best for natural images and aerial ortho-photos. For these types of images it shows in its lossy compression mode a better compression efficiency than JPEG. This efficiency gain is almost for free, because the encoding and decoding times are only marginally longer. We also compare PGF with JPEG 2000 and show that JPEG 2000 is about ten times slower than PGF. In its lossless compression mode PGF has a slightly worse compression efficiency than JPEG 2000, but a clearly better compression efficiency than JPEG-LS and PNG. If both, compression efficiency and runtime, is important, then PGF is the best of the tested algorithms for compression of natural images and aerial photos

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page