Manually generating annotated bounding boxes for object detection is time consuming. Although human-annotation is the most accurate approach, machine learning models can provide additional assistance. In this paper, we propose a human in a loop automatic image labeling framework focusing on aerial images with less features for detection. The proposed model consists of two main parts, prediction model and adjustment model. The user first provides click location to prediction model to generate a bounding box of a specific object. The bounding box is then fine-tuned by the adjustment model for more accurate size and location. A feedback and retrain mechanism is implemented that allows the users to manually adjust the generated bounding box and provide feedback to incrementally train the adjustment network during runtime. This unique online learning feature enables user to generalize existing model to target classes not initially presented in the training set, and gradually improves the specificity of the model to those new targets online. We demonstrate promising results on Neovision 2 Heli dataset. Compared to the state-of-the-art method, our prediction model achieves a higher detection rate, and our adjustment model improves the IOU by up to 45%.

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

Inference models such as the confabulation network are particularly useful in anomaly detection applications because they allow introspection to the decision process. However, building such network model always requires expert knowledge. In this paper, we present a self-structuring technique that learns the structure of a confabulation network from unlabeled data. Without any assumption of the distribution of data, we leverage the mutual information between features to learn a succinct network configuration, and enable fast incremental learning to refine the knowledge bases from continuous data streams. Compared to several existing anomaly detection methods, the proposed approach provides higher detection performance and excellent reasoning capability. We also exploit the massive parallelism that is inherent to the inference model and accelerate the detection process using GPUs. Experimental results show significant speedups and the potential to be applied to real-time applications with high-volume data streams.

Given the recent progress in the evolution of high-performance computing (HPC) technologies, the research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition system (ITRS) that serves as the physical layer of machine reading. A parallel computing architecture is adopted that incorporates the HPC technologies with advances in neuromorphic computing models. The algorithm learns from what has been read and, based on the obtained knowledge, it forms anticipations of the word and sentence level context. The information processing flow of the ITRS imitates the function of the neocortex system. It incorporates large number of simple pattern detection modules with advanced information association layer to achieve perception and recognition. Such architecture provides robust performance to images with large noise. The implemented ITRS software is able to process about 16 to 20 scanned pages per second on the 500 trillion floating point operations per second (TFLOPS) Air Force Research Laboratory (AFRL)/Information Directorate (RI) Condor HPC after performance optimization.

Autonomous and intelligent recognition of printed or handwritten text image is one of the key features to achieve situational awareness. A neuromorphic model based intelligent text recognition (ITR) system has been developed in our previous work, which recognizes texts based on word level and sentence level context represented by statistical information of characters and words. While quite effective, sometimes the existing ITR system still generates results that are grammatically incorrect because it ignores semantic and syntactic properties of sentences. In this work, we improve the accuracy of the existing ITR system by incorporating parts-of-speech tagging into the text recognition procedure. Our experimental results show that the tag-assisted text recognition improves sentence level success rate by 33% in average.

We are pursuing an investigation of neuromorphic computational models and architectures in order to leverage present understanding of how the estimated 1011neurons and 1015neuron connections in the mammalian brain are able to do some of the things a human does, and as quickly as it does it, using slow base components, while consuming very little power on affordable synthetic non-biological computing hardware. Understanding and harvesting neurologically based methods is a promising approach with great potential that may help us achieve massively parallel computation far beyond the scope of traditional computing.

A model of portions of the cerebral cortex is being developed to explore neuromorphic computing strategies in the context of highly parallel platforms. The interest is driven by the value of applications which can make use of highly parallel architectures we expect to see surpassing one thousand cores per die in the next few years. A central question we seek to answer is what the architecture of hyper-parallel machines should be. We also seek to understand computational methods akin to how a brain deals with sensing, perception, memory, and cognition. The model is being developed incrementally, starting with the primary visual cortex (V1) field. It is based upon structures roughly corresponding to neocortical minicolumn and functional column structures. Gaps in neuroscience, such as inter-cell connectivity, are filled using estimates of functionality that are plausible given current understanding of the micro-anatomy. The success we encountered with achieving real-time performance is evidence validating the use of Cell-Be architecture in some classes of neuromorphic emulation. In this study we identified a particular gap-fill algorithm for lateral connections within V1 that is suggestive of a learning strategy whereby the lateral network subsumes expectation affect, reducing perception time and improving perception affect.