Developmental Robots - A New Paradigm

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming

HDR image-based deep learning approach for automatic detection of split defects on sheet metal stamping parts

Sheet metal stamping is widely used for high-volume production. Despite the wide adoption, it can lead to defects in the manufactured components, making their quality unacceptable. Because of the variety of defects that can occur on the final product, human inspectors are frequently employed to detect them. However, they can be unreliable and costly, particularly at speeds that match the stamping rate. In this paper, we propose an automatic inspection framework for the stamping process that is based on computer vision and deep learning techniques. The low cost, remote sensing capability and simple implementation mean that it can be easily deployed in an industrial setting. A particular focus of this research is to account for the harsh lighting conditions and the highly reflective nature of products found in manufacturing environments that affect optical sensing techniques by making it difficult to capture the details of a scene. High dynamic range images can capture details of an environment in harsh lighting conditions, and in the context of this work, can capture highly reflective metals found in sheet metal stamping manufacturing. Building on this imaging technique, we propose a framework including a deep learning model to detect defects in sheet metal stamping parts. To test the framework, sheet metal ‘Nakajima’ samples were pressed with an industrial stamping press. Then optimally exposed, sequence of exposures, tone-mapped and high dynamic range images of the samples were used to train convolutional neural network-based detectors. Analysis of the resulting models showed that high dynamic range image-based models achieved substantially higher accuracy and minimal false-positive predictions

High Dynamic Range Image Watermarking Robust Against Tone-Mapping Operators

High dynamic range (HDR) images represent the future format for digital images since they allow accurate rendering of a wider range of luminance values. However, today special types of preprocessing, collectively known as tone-mapping (TM) operators, are needed to adapt HDR images to currently existing displays. Tone-mapped images, although of reduced dynamic range, have nonetheless high quality and hence retain some commercial value. In this paper, we propose a solution to the problem of HDR image watermarking, e.g., for copyright embedding, that should survive TM. Therefore, the requirements imposed on the watermark encompass imperceptibility, a certain degree of security, and robustness to TM operators. The proposed watermarking system belongs to the blind, detectable category; it is based on the quantization index modulation (QIM) paradigm and employs higher order statistics as a feature. Experimental analysis shows positive results and demonstrates the system effectiveness with current state-of-art TM algorithms

Maia and Mandos: Tools for Integrity Protection on Arbitrary Files

We present the results of our dissertation research, which focuses on practical means of protecting system data integrity. In particular, we present Maia, a language for describing integrity constraints on arbitrary file types, and Mandos, a Linux Security Module which uses verify-on-close to enforce mandatory integrity guarantees. We also provide details of a Maia-based verifier generator, demonstrate that Maia and Mandos introduce minimal delay in performing their tasks, and include a selection of sample Maia specifications

Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression

Imaging systems are essential to a wide range of modern day applications. With the continuous advancement in imaging systems, there is an on-going need to adapt and improve the imaging pipeline running inside the imaging systems. In this thesis, methods are presented to improve the imaging pipeline of digital cameras. Here we present three methods to improve important phases of the imaging process, which are (i) ``Automatic exposure adjustment'' (ii) ``Radiometric calibration'' (iii) ''High dynamic range compression''. These contributions touch the initial, intermediate and final stages of imaging pipeline of digital cameras. For exposure control, we propose two methods. The first makes use of CCD-based equations to formulate the exposure control problem. To estimate the exposure time, an initial image was acquired for each wavelength channel to which contrast adjustment techniques were applied. This helps to recover a reference cumulative distribution function of image brightness at each channel. The second method proposed for automatic exposure control is an iterative method applicable for a broad range of imaging systems. It uses spectral sensitivity functions such as the photopic response functions for the generation of a spectral power image of the captured scene. A target image is then generated using the spectral power image by applying histogram equalization. The exposure time is hence calculated iteratively by minimizing the squared difference between target and the current spectral power image. Here we further analyze the method by performing its stability and controllability analysis using a state space representation used in control theory. The applicability of the proposed method for exposure time calculation was shown on real world scenes using cameras with varying architectures. Radiometric calibration is the estimate of the non-linear mapping of the input radiance map to the output brightness values. The radiometric mapping is represented by the camera response function with which the radiance map of the scene is estimated. Our radiometric calibration method employs an L1 cost function by taking advantage of Weisfeld optimization scheme. The proposed calibration works with multiple input images of the scene with varying exposure. It can also perform calibration using a single input with few constraints. The proposed method outperforms, quantitatively and qualitatively, various alternative methods found in the literature of radiometric calibration. Finally, to realistically represent the estimated radiance maps on low dynamic range display (LDR) devices, we propose a method for dynamic range compression. Radiance maps generally have higher dynamic range (HDR) as compared to the widely used display devices. Thus, for display purposes, dynamic range compression is required on HDR images. Our proposed method generates few LDR images from the HDR radiance map by clipping its values at different exposures. Using contrast information of each LDR image generated, the method uses an energy minimization approach to estimate the probability map of each LDR image. These probability maps are then used as label set to form final compressed dynamic range image for the display device. The results of our method were compared qualitatively and quantitatively with those produced by widely cited and professionally used methods

HDR Image Watermarking

In this Chapter we survey available solutions for HDR image watermarking. First, we briefly discuss watermarking in general terms, with particular emphasis on its requirements that primarily include security, robustness, imperceptibility, capacity and the availability of the original image during recovery. However, with respect to traditional image watermarking, HDR images possess a unique set of features such as an extended range of luminance values to work with and tone-mapping operators against whom it is essential to be robust. These clearly affect the HDR watermarking algorithms proposed in the literature, which we extensively review next, including a thorough analysis of the reported experimental results. As a working example, we also describe the HDR watermarking system that we recently proposed and that focuses on combining imperceptibility, security and robustness to TM operators at the expense of capacity. We conclude the chapter with a critical analysis of the current state and future directions of the watermarking applications in the HDR domain

High dynamic range imaging for face matching

Human facial recognition in the context of surveillance, forensics and photo-ID verification is a task for which accuracy is critical. In most cases, this involves unfamiliar face recognition whereby the observer has had very short or no exposure at all to the faces being identified. In such cases, recognition performance is very poor: changes in appearance, limitations in the overall quality of images - illumination in particular - reduces individuals’ ability in taking decisions regarding a person’s identity. High Dynamic Range (HDR) imaging permits handling of real-world lighting with higher accuracy than the traditional low (or standard) dynamic range (LDR) imaging. The intrinsic benefits provided by HDR make it the ideal candidate to verify whether this technology can improve individuals’ performance in face matching, especially in challenging lighting conditions. This thesis compares HDR imaging against LDR imaging in an unfamiliar face matching task. A radiometrically calibrated HDR face dataset with five different lighting conditions is created. Subsequently, this dataset is used in controlled experiments to measure performance (i.e. reaction times and accuracy) of human participants when identifying faces in HDR. Experiment 1: HDRvsLDR (N = 39) compared participants’ performance when using HDR vs LDR stimuli created using the two full pipelines. The findings from this experiment suggest that HDR (µ =90.08%) can significantly (p< 0.01) improve face matching accuracy over LDR (µ =83.38%) and significantly (p<0.05) reduce reaction times (HDR 3.06s and LDR 3.31s). Experiment 2: Backwards-Compatibility HDR (N = 39) compared par ticipants’ performance when the LDR pipeline is upgraded by adding HDR imaging in the capture or in the display stage. The results show that adopt xi ing HDR imaging in the capture stage, even if the stimuli are subsequently tone-mapped and displayed on an LDR screen, allows higher accuracy (capture stage: µ =85.11% and display stage: µ =80.70%), (p<0.01) and faster reaction times (capture stage: µ =3.06s and display stage: µ =3.25s), (p< 0.05) than when native LDR images are retargeted to be displayed on an HDR display. In Experiment 3: the data collected from previous experiments was used to perform further analysis (N = 78) on all stages of the HDR pipeline simultaneously. The results show that the adoption of the full-HDR pipeline as opposed to a backwards-compatible one is advisable if the best values of accuracy are to be achieved (5.84% increase compared to the second best outcome, p<0.01). This work demonstrates scope for improvement in the accuracy of face matching tasks by realistic image reproduction and delivery through the adoption of HDR imaging techniques