Miniaturized embedded stereo vision system (MESVS)

Stereo vision is one of the fundamental problems of computer vision. It is also one of the oldest and heavily investigated areas of 3D vision. Recent advances of stereo matching methodologies and availability of high performance and efficient algorithms along with availability of fast and affordable hardware technology, have allowed researchers to develop several stereo vision systems capable of operating at real-time. Although a multitude of such systems exist in the literature, the majority of them concentrates only on raw performance and quality rather than factors such as dimension, and power requirement, which are of significant importance in the embedded settings. In this thesis a new miniaturized embedded stereo vision system (MESVS) is presented, which is miniaturized to fit within a package of 5x5cm, is power efficient, and cost-effective. Furthermore, through application of embedded programming techniques and careful optimization, MESVS achieves the real-time performance of 20 frames per second. This work discusses the various challenges involved regarding design and implementation of this system and the measures taken to tackle them

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Design and implementation of a real-time miniaturized embedded stereo-vision system

The main motivation of the thesis is to develop a fully integrated, modular, small baseline (\u3c=3cm), low cost (\u3c=CAD$600), real-time miniaturized embedded stereo-vision system which fits within 5x5cm and consumes very low power ([email protected]). The system consists of two small profile cameras and a dualcore embedded media processor, running at 600MHz per core. The stereo-matching engine performs sub-sampling, rectification, pre-processing using census transform, correlation-based Sum of Hamming Distance matching using three levels of recursion, LRC check and post-processing. The novel post processing algorithm removes outliers due to low-texture regions and depth-discontinuities. A quantitative performance of the post processing algorithm is presented which shows that for all regions, it has an average percentage improvement of 13.61% (based on 2006 Middlebury dataset). To further enhance the performance of the system, optimization steps are employed to achieve a speed of around 10fps for disparity maps in MESVS-I and 20fps in MESVS-II system

Image processing techniques for mixed reality and biometry

2013 - 2014This thesis work is focused on two applicative fields of image processing research, which, for different reasons, have become particularly active in the last decade: Mixed Reality and Biometry. Though the image processing techniques involved in these two research areas are often different, they share the key objective of recognizing salient features typically captured through imaging devices. Enabling technologies for augmented/mixed reality have been improved and refined throughout the last years and more recently they seems to have finally passed the demo stage to becoming ready for practical industrial and commercial applications. To this regard, a crucial role will likely be played by the new generation of smartphones and tablets, equipped with an arsenal of sensors connections and enough processing power for becoming the most portable and affordable AR platform ever. Within this context, techniques like gesture recognition by means of simple, light and robust capturing hardware and advanced computer vision techniques may play an important role in providing a natural and robust way to control software applications and to enhance onthe- field operational capabilities. The research described in this thesis is targeted toward advanced visualization and interaction strategies aimed to improve the operative range and robustness of mixed reality applications, particularly for demanding industrial environments... [edited by Author]XIII n.s

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

GPUを用いた高解像度画像の実時間ステレオマッチングシステムの研究

筑波大学 (University of Tsukuba)201