Computational Modeling of Human Dorsal Pathway for Motion Processing

Reliable motion estimation in videos is of crucial importance for background iden- tification, object tracking, action recognition, event analysis, self-navigation, etc. Re- constructing the motion field in the 2D image plane is very challenging, due to variations in image quality, scene geometry, lighting condition, and most importantly, camera jit- tering. Traditional optical flow models assume consistent image brightness and smooth motion field, which are violated by unstable illumination and motion discontinuities that are common in real world videos. To recognize observer (or camera) motion robustly in complex, realistic scenarios, we propose a biologically-inspired motion estimation system to overcome issues posed by real world videos. The bottom-up model is inspired from the infrastructure as well as functionalities of human dorsal pathway, and the hierarchical processing stream can be divided into three stages: 1) spatio-temporal processing for local motion, 2) recogni- tion for global motion patterns (camera motion), and 3) preemptive estimation of object motion. To extract effective and meaningful motion features, we apply a series of steer- able, spatio-temporal filters to detect local motion at different speeds and directions, in a way that\u27s selective of motion velocity. The intermediate response maps are cal- ibrated and combined to estimate dense motion fields in local regions, and then, local motions along two orthogonal axes are aggregated for recognizing planar, radial and circular patterns of global motion. We evaluate the model with an extensive, realistic video database that collected by hand with a mobile device (iPad) and the video content varies in scene geometry, lighting condition, view perspective and depth. We achieved high quality result and demonstrated that this bottom-up model is capable of extracting high-level semantic knowledge regarding self motion in realistic scenes. Once the global motion is known, we segment objects from moving backgrounds by compensating for camera motion. For videos captured with non-stationary cam- eras, we consider global motion as a combination of camera motion (background) and object motion (foreground). To estimate foreground motion, we exploit corollary dis- charge mechanism of biological systems and estimate motion preemptively. Since back- ground motions for each pixel are collectively introduced by camera movements, we apply spatial-temporal averaging to estimate the background motion at pixel level, and the initial estimation of foreground motion is derived by comparing global motion and background motion at multiple spatial levels. The real frame signals are compared with those derived by forward predictions, refining estimations for object motion. This mo- tion detection system is applied to detect objects with cluttered, moving backgrounds and is proved to be efficient in locating independently moving, non-rigid regions. The core contribution of this thesis is the invention of a robust motion estimation system for complicated real world videos, with challenges by real sensor noise, complex natural scenes, variations in illumination and depth, and motion discontinuities. The overall system demonstrates biological plausibility and holds great potential for other applications, such as camera motion removal, heading estimation, obstacle avoidance, route planning, and vision-based navigational assistance, etc

Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks

Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, storage size and memory usage. In QNNs, parameters and activations are uniformly quantized, such that the multiplications and additions can be accelerated by bitwise operations. However, distributions of parameters in Neural Networks are often imbalanced, such that the uniform quantization determined from extremal values may under utilize available bitwidth. In this paper, we propose a novel quantization method that can ensure the balance of distributions of quantized values. Our method first recursively partitions the parameters by percentiles into balanced bins, and then applies uniform quantization. We also introduce computationally cheaper approximations of percentiles to reduce the computation overhead introduced. Overall, our method improves the prediction accuracies of QNNs without introducing extra computation during inference, has negligible impact on training speed, and is applicable to both Convolutional Neural Networks and Recurrent Neural Networks. Experiments on standard datasets including ImageNet and Penn Treebank confirm the effectiveness of our method. On ImageNet, the top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is superior to the state-of-the-arts of QNNs

Frost Porosity Measurement Using Capacitive Sensor

Frosting is a dynamic process because of the changes in the frost-air interface temperature as the frost layer grows. The frost properties, such as frost density and frost porosity, are highly dependent on the frosting conditions and vary with time even under a constant environmental and operational conditions. Precise detection of frost properties is important for understanding frosting mechanisms and predicting frost growth, and it is also important for defrost control in many applications. So far there have been very few research reports on dynamic frost porosity measurement, and most work reports an average measurement approach, which is undertaken by measuring the mass and volume within certain frost accumulating period to estimate the averaged frost properties. Those approaches ignore the temporal variation of frost properties with an assumption that the frost buildup at a constant porosity, at least within a certain time period. As the result, there is a distinct deviation between different frost models, because as a very important input of models, most empirical frost porosity correlations were based on different time intervals of measurement. Frost, as a mixture of ice crystal and air, could have its properties estimated based on the percentage of each component. In this work, a capacitive sensor is developed to detect the capacitance variation as frost growing, which together with the dielectric constant of ice and air, could be used to determine the temporal porosity according to the Maxwell-Garnett (MG) theory. An interdigital electrode designed in this work is fabricated using photolithography technique (shown in Figure 1), together with the PCB connector and a commercial digital converter (FDC 2214) can sense the capacitance reading with a 0.0001 pF resolution. 3-D printed Polyvinyl-chloride porous structure with controlled porosity filled with/without gelatin of different concentration (shown in Figure 2) has been used to valid the sensor’s responding function. Frost porosity was measured under different conditions with known sensor function and the empirical correlation of frost porosity is provided in this work and compared with existing work. This work presents a new method to dynamically detect the frost porosity as frost growing, and it is a big contribution to the mass-based defrost strategy development and frost growth modeling

Thermal processing in lithography: Equipment design, control and metrology

Ph.DDOCTOR OF PHILOSOPH

Reinventing Chineseness: Corporate social responsibility reporting in Chinese Confucian companies

Prior accounting studies have examined how CSR discourses are intertwined with institutional pressures and market demands in non-Western contexts, but only a few studies have focused attention on the interaction between global CSR discourses and non-Western local cultural systems. Taking a set of Chinese Confucian companies as the empirical case, therefore, this study explores the ways the dialectics between the global and the local produce and reproduce a particular corporate social responsibility reporting regime (CSRR). This study draws on Robertson`s (1995) glocalisation theory. Accounting scholars have used various theoretical frameworks to explore how global-local duality implicates accounting and accountability, including CSRR. Examples include the use of Gidden’s globalisation theory, Edward Said’s Orientalism and Homi Bhabha’s concept of hybridity. While such social theories tend to view the global and globalisation as competing counterparts of the local and localisation, Robertson’s (1995) theory of glocalisation provides an alternative view in that the two seemingly opposite tendencies of globalisation and localisation are inter-reinforcing and interpenetrating in the construction of the “glocal”. The “glocal” then provides a political-cultural condition in which the supportive or reinforcing elements of the global and the local are highlighted and materialised while contradictions between the global and local are mitigated. Accordingly, this study explores how Chinese companies mobilise a concept of Confucianism to translate and adapt global discourses of CSR to Chinese cultural conditions so that a “glocalised” version of CSR is produced amid the political demands of the Chinese political state. In methodological terms, this study contains two mutually inclusive data collection and methods of analyses: a qualitative content analysis and a couple of interview-based case studies. A qualitative content analysis of 11 Confucian companies’ CSR reports was conducted to give a basic understanding of the contents and forms of the CSR reports. The qualitative content analysis also helped me to select 2 Confucian companies where I conducted 60 interviews. A document analysis, including of external and internal documents, was conducted to generate understanding about how Chinese political reforms shape and reshape Confucian companies` behaviours. Empirically, the glocal is understood as a political-cultural space covering both monophonic sameness and polyphonic variances, reflecting the co-presence of the global homogeneous and the local heterogeneous, universalising and particularising tendencies. Monophonic sameness means that there are some principles which CSR and Confucianism have in common. The polyphonic variances signify the different but not opposite ideas between CSR and Confucianism. Both CSR and Confucianism enjoy and celebrate the existences of differences because they treat their differences as a source of learning new knowledge. Thus, glocalised accounting regimes reflect the co-presence of the global and the local, homogenisation and heterogenisation, and particularisation and universalisation. To be more specific, Company A encountered the CSR concepts before becoming a Confucian company. Company A started its business by mimicking Western CSR practices and aligning with global CSR guidance such as the Global Reporting Initiative on Sustainability Reporting. After China’s policy of Cultural Confidence was issued in 2012, this company started adapting global CSR practices to Confucian principles. In this process, I found that CSR knowledge facilitates the adoption and adaptation of Confucian ideology as CSR and Confucianism have many ideas in common. For example, providing a comfortable workplace for employees is a requirement of global CSR guidance, and treating employees as family members also reflect the Confucian principle of benevolence. Thus, when Company A acquired knowledge of CSR, understanding the Confucian ideology become smooth. Moreover, the universal (i.e., CSR discourses) not only facilitates the adoption of the particular (i.e., Confucianism) but also gets reinforced by Confucian wisdom. For example, Confucian companies treat people as part of nature and they respect other non-human objects of nature. This is a Confucian idea being added to the CSR discourses; hence, the latter is being enriched by the former. This phenomenon demonstrates a key difference between localisation and glocalisation. While localisation emphases the role of local actors in reshaping global accounting discourses, glocalisation focuses attention on how the global and the local inter-penetrate and inter-reinforce each other. Company B, however, became a Confucian company before it encountered the CSR concepts. Even though CSR accounting was a Western concept, it was smoothly adopted and adapted into this non-Western context. In Company B, its acquired Confucian knowledge helped employees understand the CSR principles, so the local culture of Confucianism expedited the adoption and adaptation of global CSR concepts. Also, the universal (i.e., global CSR discourses) penetrate and reinforce the particular (i.e., Confucianism). In practice, Confucianism cannot provide specific behavioural guidance for Company B because Confucianism is an abstract ethical framework. The advent of global CSR knowledge thus gave Company B concrete guidance. An example is that Company B learnt from the CSR activities of global leading companies and set up a baby care room on the second floor of its headquarters. Thus, the CSR concepts are not excluded, rejected or resisted but welcomed and accepted by this Confucian company. Being glocal is a strategic act and a disposition in a contemporary political context, where a company faces the challenges of simultaneously satisfying certain political-economic and market demands and impositions by global forces and certain local political-cultural imperatives created by local forces. In this work’s cases, the Chinese government played an important role in shaping the CSR practices in Confucian companies. I found that Xi aimed to make China great again and build China as a cultural superpower. He promoted the Cultural Confidence policy to revitalise Chinese cultural foundations within their political and economic systems. Following this policy, traditional Confucianism and Confucian business became reawakened. The central government then asked local governments and institutions to set up Confucian-related conferences and forums to find a way of linking Confucianism with Western technologies and ideologies, or the local with the global. To seek political-cultural legitimacy, some Chinese companies tended to attend the conferences, become Confucian companies and engage their accounting practices with Confucianism. I saw this as a process of reinventing Chineseness: incorporating Confucian principles into business activities while satisfying global forces such as global stakeholders and customers

Automated call detection for acoustic surveys with structured calls of varying length

Funding: Y.W. is partly funded by the China Scholarship Council (CSC) for Ph.D. study at the University of St Andrews, UK.1. When recorders are used to survey acoustically conspicuous species, identification calls of the target species in recordings is essential for estimating density and abundance. We investigate how well deep neural networks identify vocalisations consisting of phrases of varying lengths, each containing a variable number of syllables. We use recordings of Hainan gibbon (Nomascus hainanus) vocalisations to develop and test the methods. 2. We propose two methods for exploiting the two-level structure of such data. The first combines convolutional neural network (CNN) models with a hidden Markov model (HMM) and the second uses a convolutional recurrent neural network (CRNN). Both models learn acoustic features of syllables via a CNN and temporal correlations of syllables into phrases either via an HMM or recurrent network. We compare their performance to commonly used CNNs LeNet and VGGNet, and support vector machine (SVM). We also propose a dynamic programming method to evaluate how well phrases are predicted. This is useful for evaluating performance when vocalisations are labelled by phrases, not syllables. 3. Our methods perform substantially better than the commonly used methods when applied to the gibbon acoustic recordings. The CRNN has an F-score of 90% on phrase prediction, which is 18% higher than the best of the SVM or LeNet and VGGNet methods. HMM post-processing raised the F-score of these last three methods to as much as 87%. The number of phrases is overestimated by CNNs and SVM, leading to error rates between 49% and 54%. With HMM, these error rates can be reduced to 0.4% at the lowest. Similarly, the error rate of CRNN's prediction is no more than 0.5%. 4. CRNNs are better at identifying phrases of varying lengths composed of a varying number of syllables than simpler CNN or SVM models. We find a CRNN model to be best at this task, with a CNN combined with an HMM performing almost as well. We recommend that these kinds of models are used for species whose vocalisations are structured into phrases of varying lengths.Publisher PDFPeer reviewe

Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually. Digital recorders allow surveyors to gather large volumes of data at low cost, but identifying target species vocalisations in these data is non-trivial. Machine learning (ML) methods are often used to do the identification. They can process large volumes of data quickly, but they do not detect all vocalisations and they do generate some false positives (vocalisations that are not from the target species). Existing wildlife abundance survey methods have been designed specifically to deal with the first of these mistakes, but current methods of dealing with false positives are not well-developed. They do not take account of features of individual vocalisations, some of which are more likely to be false positives than others. We propose three methods for acoustic spatial capture-recapture inference that integrate individual-level measures of confidence from ML vocalisation identification into the likelihood and hence integrate ML uncertainty into inference. The methods include a mixture model in which species identity is a latent variable. We test the methods by simulation and find that in a scenario based on acoustic data from Hainan gibbons, in which ignoring false positives results in 17% positive bias, our methods give negligible bias and coverage probabilities that are close to the nominal 95% level.Comment: 35 pages, 5 figure