CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

Image Feature Extraction Acceleration

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of parallelism and distributed memory from the very beginning of the processing chain. Raw pixel values provided by the front-end image sensor are squeezed into a high-speed interface with the rest of system components. Only then, after deserializing this massive dataflow, parallelism, if any, is exploited. This chapter introduces a rather different approach from an architectural point of view. We present two Application-Specific Integrated Circuits (ASICs) where the 2-D array of photo-sensitive devices featured by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algorithms within the computer vision community: the Viola-Jones face detection algorithm and the Scale Invariant Feature Transform (SIFT). Experimental results prove the feasibility and benefits of this architectural solution.Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011- 1625-430000, IPC-20111009Junta de Andalucía TIC 2338-2013Xunta de Galicia EM2013/038Office of NavalResearch (USA) N00014141035

ENERGY-EFFICIENT FEATURE EXTRACTION ENGINE AND SECURE CHIP IDENTIFICATION FOR UBIQUITOUS SURVEILLANCE

Ph.DDOCTOR OF PHILOSOPH

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

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 prediction-based approach for features aggregation in Visual Sensor Networks

Visual Sensor Networks (VSNs) constitute a key technology for the implementation of several visual analysis tasks. Recent studies have demonstrated that such tasks can be efficiently performed following an operative paradigm where cameras transmit to a central controller local image features, rather than pixel-domain images. Furthermore, features from multiple camera views may be efficiently aggregated exploiting the spatial redundancy between overlapping views. In this paper we propose a routing protocol designed for supporting aggregation of image features in a VSN. First, we identify a predictor able to estimate the efficiency of local features aggregation between different cameras in a VSN. The proposed predictor is chosen so as to minimize the prediction error while keeping the network overhead cost low. Then, we harmonically integrate the proposed predictor in the Routing Protocol for Low-Power and Lossy Networks (RPL) in order to support the task of in-network feature aggregation. We propose a RPL objective function that takes into account the predicted aggregation efficiency and build the routes from the camera nodes to a central controller so that either energy consumption or used network bandwidth is minimized. Extensive experimental results confirm that the proposed approach can be used to increase the efficiency of VSNs

Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines

PhD thesisIn this thesis, I describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of (Hubel & Wiesel, 1959) from primary to extrastriate visual areas. It attempts to explain the first few hundred milliseconds of visual processing and Âimmediate recognitionÂ. One of the key elements in the approach is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by (Riesenhuber & Poggio, 1999) and builds upon several existing neurobiological models and conceptual proposals.First, I present evidence to show that the model can duplicate the tuning properties of neurons in various brain areas (e.g., V1, V4 and IT). In particular, the model agrees with data from V4 about the response of neurons to combinations of simple two-bar stimuli (Reynolds et al, 1999) (within the receptive field of the S2 units) and some of the C2 units in the model show a tuning for boundary conformations which is consistent with recordings from V4 (Pasupathy & Connor, 2001). Second, I show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli, but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Third, I describe a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. This suggests that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path.Taken together, the evidences suggest that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images

Merging chrominance and luminance in early, medium, and late fusion using Convolutional Neural Networks

The field of Machine Learning has received extensive attention in recent years. More particularly, computer vision problems have got abundant consideration as the use of images and pictures in our daily routines is growing. The classification of images is one of the most important tasks that can be used to organize, store, retrieve, and explain pictures. In order to do that, researchers have been designing algorithms that automatically detect objects in images. During last decades, the common approach has been to create sets of features -- manually designed -- that could be exploited by image classification algorithms. More recently, researchers designed algorithms that automatically learn these sets of features, surpassing state-of-the-art performances. However, learning optimal sets of features is computationally expensive and it can be relaxed by adding prior knowledge about the task, improving and accelerating the learning phase. Furthermore, with problems with a large feature space the complexity of the models need to be reduced to make it computationally tractable (e.g. the recognition of human actions in videos). Consequently, we propose to use multimodal learning techniques to reduce the complexity of the learning phase in Artificial Neural Networks by incorporating prior knowledge about the connectivity of the network. Furthermore, we analyze state-of-the-art models for image classification and propose new architectures that can learn a locally optimal set of features in an easier and faster manner. In this thesis, we demonstrate that merging the luminance and the chrominance part of the images using multimodal learning techniques can improve the acquisition of good visual set of features. We compare the validation accuracy of several models and we demonstrate that our approach outperforms the basic model with statistically significant results