FPGA-based module for SURF extraction

We present a complete hardware and software solution of an FPGA-based computer vision embedded module capable of carrying out SURF image features extraction algorithm. Aside from image analysis, the module embeds a Linux distribution that allows to run programs specifically tailored for particular applications. The module is based on a Virtex-5 FXT FPGA which features powerful configurable logic and an embedded PowerPC processor. We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. The module's overall performance is evaluated and compared to CPU and GPU based solutions. Results show that the embedded module achieves comparable disctinctiveness to the SURF software implementation running in a standard CPU while being faster and consuming significantly less power and space. Thus, it allows to use the SURF algorithm in applications with power and spatial constraints, such as autonomous navigation of small mobile robots

Bio-Inspired Multi-Spectral Imaging Sensors and Algorithms for Image Guided Surgery

Image guided surgery (IGS) utilizes emerging imaging technologies to provide additional structural and functional information to the physician in clinical settings. This additional visual information can help physicians delineate cancerous tissue during resection as well as avoid damage to near-by healthy tissue. Near-infrared (NIR) fluorescence imaging (700 nm to 900 nm wavelengths) is a promising imaging modality for IGS, namely for the following reasons: First, tissue absorption and scattering in the NIR window is very low, which allows for deeper imaging and localization of tumor tissue in the range of several millimeters to a centimeter depending on the tissue surrounding the tumor. Second, spontaneous tissue fluorescence emission is minimal in the NIR region, allowing for high signal-to-background ratio imaging compared to visible spectrum fluorescence imaging. Third, decoupling the fluorescence signal from the visible spectrum allows for optimization of NIR fluorescence while attaining high quality color images. Fourth, there are two FDA approved fluorescent dyes in the NIR region—namely methylene blue (MB) and indocyanine green—which can help to identify tumor tissue due to passive accumulation in human subjects. The aforementioned advantages have led to the development of NIR fluorescence imaging systems for a variety of clinical applications, such as sentinel lymph node imaging, angiography, and tumor margin assessment. With these technological advances, secondary surgeries due to positive tumor margins or damage to healthy organs can be largely mitigated, reducing the emotional and financial toll on the patient. Currently, several NIR fluorescence imaging systems (NFIS) are available commercially or are undergoing clinical trials, such as FLARE, SPY, PDE, Fluobeam, and others. These systems capture multi-spectral images using complex optical equipment and are combined with real-time image processing to present an augmented view to the surgeon. The information is presented on a standard monitor above the operating bed, which requires the physician to stop the surgical procedure and look up at the monitor. The break in the surgical flow sometimes outweighs the benefits of fluorescence based IGS, especially in time-critical surgical situations. Furthermore, these instruments tend to be very bulky and have a large foot print, which significantly complicates their adoption in an already crowded operating room. In this document, I present the development of a compact and wearable goggle system capable of real-time sensing of both NIR fluorescence and color information. The imaging system is inspired by the ommatidia of the monarch butterfly, in which pixelated spectral filters are integrated with light sensitive elements. The pixelated spectral filters are fabricated via a carefully optimized nanofabrication procedure and integrated with a CMOS imaging array. The entire imaging system has been optimized for high signal-to-background fluorescence imaging using an analytical approach, and the efficacy of the system has been experimentally verified. The bio-inspired spectral imaging sensor is integrated with an FPGA for compact and real-time signal processing and a wearable goggle for easy integration in the operating room. The complete imaging system is undergoing clinical trials at Washington University in the St. Louis Medical School for imaging sentinel lymph nodes in both breast cancer patients and melanoma patients

High-performance hardware accelerators for image processing in space applications

Mars is a hard place to reach. While there have been many notable success stories in getting probes to the Red Planet, the historical record is full of bad news. The success rate for actually landing on the Martian surface is even worse, roughly 30%. This low success rate must be mainly credited to the Mars environment characteristics. In the Mars atmosphere strong winds frequently breath. This phenomena usually modifies the lander descending trajectory diverging it from the target one. Moreover, the Mars surface is not the best place where performing a safe land. It is pitched by many and close craters and huge stones, and characterized by huge mountains and hills (e.g., Olympus Mons is 648 km in diameter and 27 km tall). For these reasons a mission failure due to a landing in huge craters, on big stones or on part of the surface characterized by a high slope is highly probable. In the last years, all space agencies have increased their research efforts in order to enhance the success rate of Mars missions. In particular, the two hottest research topics are: the active debris removal and the guided landing on Mars. The former aims at finding new methods to remove space debris exploiting unmanned spacecrafts. These must be able to autonomously: detect a debris, analyses it, in order to extract its characteristics in terms of weight, speed and dimension, and, eventually, rendezvous with it. In order to perform these tasks, the spacecraft must have high vision capabilities. In other words, it must be able to take pictures and process them with very complex image processing algorithms in order to detect, track and analyse the debris. The latter aims at increasing the landing point precision (i.e., landing ellipse) on Mars. Future space-missions will increasingly adopt Video Based Navigation systems to assist the entry, descent and landing (EDL) phase of space modules (e.g., spacecrafts), enhancing the precision of automatic EDL navigation systems. For instance, recent space exploration missions, e.g., Spirity, Oppurtunity, and Curiosity, made use of an EDL procedure aiming at following a fixed and precomputed descending trajectory to reach a precise landing point. This approach guarantees a maximum landing point precision of 20 km. By comparing this data with the Mars environment characteristics, it is possible to understand how the mission failure probability still remains really high. A very challenging problem is to design an autonomous-guided EDL system able to even more reduce the landing ellipse, guaranteeing to avoid the landing in dangerous area of Mars surface (e.g., huge craters or big stones) that could lead to the mission failure. The autonomous behaviour of the system is mandatory since a manual driven approach is not feasible due to the distance between Earth and Mars. Since this distance varies from 56 to 100 million of km approximately due to the orbit eccentricity, even if a signal transmission at the light speed could be possible, in the best case the transmission time would be around 31 minutes, exceeding so the overall duration of the EDL phase. In both applications, algorithms must guarantee self-adaptability to the environmental conditions. Since the Mars (and in general the space) harsh conditions are difficult to be predicted at design time, these algorithms must be able to automatically tune the internal parameters depending on the current conditions. Moreover, real-time performances are another key factor. Since a software implementation of these computational intensive tasks cannot reach the required performances, these algorithms must be accelerated via hardware. For this reasons, this thesis presents my research work done on advanced image processing algorithms for space applications and the associated hardware accelerators. My research activity has been focused on both the algorithm and their hardware implementations. Concerning the first aspect, I mainly focused my research effort to integrate self-adaptability features in the existing algorithms. While concerning the second, I studied and validated a methodology to efficiently develop, verify and validate hardware components aimed at accelerating video-based applications. This approach allowed me to develop and test high performance hardware accelerators that strongly overcome the performances of the actual state-of-the-art implementations. The thesis is organized in four main chapters. Chapter 2 starts with a brief introduction about the story of digital image processing. The main content of this chapter is the description of space missions in which digital image processing has a key role. A major effort has been spent on the missions in which my research activity has a substantial impact. In particular, for these missions, this chapter deeply analizes and evaluates the state-of-the-art approaches and algorithms. Chapter 3 analyzes and compares the two technologies used to implement high performances hardware accelerators, i.e., Application Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs). Thanks to this information the reader may understand the main reasons behind the decision of space agencies to exploit FPGAs instead of ASICs for high-performance hardware accelerators in space missions, even if FPGAs are more sensible to Single Event Upsets (i.e., transient error induced on hardware component by alpha particles and solar radiation in space). Moreover, this chapter deeply describes the three available space-grade FPGA technologies (i.e., One-time Programmable, Flash-based, and SRAM-based), and the main fault-mitigation techniques against SEUs that are mandatory for employing space-grade FPGAs in actual missions. Chapter 4 describes one of the main contribution of my research work: a library of high-performance hardware accelerators for image processing in space applications. The basic idea behind this library is to offer to designers a set of validated hardware components able to strongly speed up the basic image processing operations commonly used in an image processing chain. In other words, these components can be directly used as elementary building blocks to easily create a complex image processing system, without wasting time in the debug and validation phase. This library groups the proposed hardware accelerators in IP-core families. The components contained in a same family share the same provided functionality and input/output interface. This harmonization in the I/O interface enables to substitute, inside a complex image processing system, components of the same family without requiring modifications to the system communication infrastructure. In addition to the analysis of the internal architecture of the proposed components, another important aspect of this chapter is the methodology used to develop, verify and validate the proposed high performance image processing hardware accelerators. This methodology involves the usage of different programming and hardware description languages in order to support the designer from the algorithm modelling up to the hardware implementation and validation. Chapter 5 presents the proposed complex image processing systems. In particular, it exploits a set of actual case studies, associated with the most recent space agency needs, to show how the hardware accelerator components can be assembled to build a complex image processing system. In addition to the hardware accelerators contained in the library, the described complex system embeds innovative ad-hoc hardware components and software routines able to provide high performance and self-adaptable image processing functionalities. To prove the benefits of the proposed methodology, each case study is concluded with a comparison with the current state-of-the-art implementations, highlighting the benefits in terms of performances and self-adaptability to the environmental conditions

OPTIMIZED ARCHITECTURE DESIGN AND IMPLEMENTATION OF OBJECT TRACKING ALGORITHM ON FPGA

FPGA based Object tracking implementation is one of the most recent video surveillance applications in embedded systems. In general, FPGA implementation is more efficient than general purpose computers in attaining high throughput due to its parallelism and execution speed. The system need to be designed on a standard frame rate in such a way to achieve optimal performance in real time environment. Optimal design of a system is dependent on minimizing the cost, area (device utility) and power while achieving the required speed. Past research work that investigated object tracking systems' implementation on FPGA achieved a significantly high throughput but have shown high device utilization. This research work aims at optimizing the device utilization under real time constraints. The Adaptive Hybrid Difference algorithm (AHD), which is used to detect the moving objects, was chosen to be implemented on FPGA due to its computation ability and efficiency with regard to hardware implementation. AHD can work at various lighting conditions automatically by determining the adaptive threshold in every period of time

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

A Real-time SAR Echo Simulator Based on FPGA and Parallel Computing

This paper designs and implements a SAR (Synthetic Aperture Radar) real-time echo simulator based on multi-FPGA parallel computing. The one-dimensional frequency-domain Fourier transform algorithm is used in the simulator, and the echo signal model and the rapid calculation algorithm of impulse response function are introduced. The pipeline compute structure, multichannel parallel computing and procedure flow design are the key technologies of the simulator, which are also presented in details. And finally, the validity and correctness of the SAR echo simulator are verified through the imaging results of the point-array target and the nature scene target

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