Abstract—We present a neuromorphic cortical-layer processing microchip for address event representation (AER) spike-based processing systems. The microchip computes 2-D convolutions of video information represented in AER format in real time. AER, as opposed to conventional frame-based video representation, describes visual information as a sequence of events or spikes in a way similar to biological brains. This format allows for fast information identification and processing, without waiting to process complete image frames. The neuromorphic cortical-layer processing microchip presented in this paper computes convolutions of programmable kernels over the AER visual input information flow. It not only computes convolutions but also allows for a programmable forgetting rate, which in turn allows for a bio-inspired coincidence detection processing. Kernels are programmable and can be of arbitrary shape and arbitrary size of up to 32 32 pixels. The convolution processor operates on a pixel array of size 32 32, but can process an input space of up to 128 128 pixels. Larger pixel arrays can be directly processed by tiling arrays of chips. The chip receives and generates data in AER format, which is asynchronous and digital. However, its internal operation is based on analog low-current circuit techniques. The paper describes the architecture of the chip and circuits used for the pixels, including calibration techniques to overcome mismatch. Extensive experimental results are provided, describing pixel operation and calibration, convolution processing with and without forgetting, and high-speed recognition experiments like discriminating rotating propellers of different shape rotating at speeds of up to 5000 revolutions per second. Index Terms—2-D convolutions, address-event representation (AER), bio-inspired systems, digitally calibrated analog circuits, high-speed signal processing, MOS transistor mismatch, spike-based processing, subthreshold circuits, vision, VLSI mixed-circuit design. I.

Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes local smoothness constraints among data into account during ensemble learning. In this paper, we introduce a local smoothness regularizer to semi-supervised boosting algorithms based on the universal optimization framework of margin cost functionals. Our regularizer is applicable to existing semi-supervised boosting algorithms to improve their generalization and speed up their training. Comparative results on synthetic, benchmark and real world tasks demonstrate the effectiveness of our local smoothness regularizer. We discuss relevant issues and relate our regularizer to previous work. 1

adaptive image processing, and form a link between general feedforward neural networks and adaptive filters. Two dimensional CNNs are formed by one or more layers of two dimensional filters, with possible non-linear activation functions and/or down-sampling. Conventional neural network error minimization methods may be used to optimize convolutional networks in order to implement quite powerful image transformations. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). CNNs are an interesting alternative when the the input is spatially or temporally distributed, and the desired output of a system may be specified. The present paper presents a description of the convolutional network architecture, and an application to a practical image processing application on a mobile robot. As a formal CNN framework has not yet been specified in the literature, we describe CNNs in some detail, conceptually and formally. A CNN is used to detect and characterize cracks on an autonomous sewer inspection robot. Although cracks are relatively easy to detect by a human operator, autonomous sewer inspection necessitates

Abstract—In this paper, a chip that performs real-time image convolutions with programmable kernels of arbitrary shape is presented. The chip is a first experimental prototype of reduced size to validate the implemented circuits and system level techniques. The convolution processing is based on the address–event-representation (AER) technique, which is a spike-based biologically inspired image and video representation technique that favors communication bandwidth for pixels with more information. As a first test prototype, a pixel array of 16 16 has been implemented with programmable kernel size of up to 16 16. The chip has been fabricated in a standard 0.35- m complimentary metal–oxide–semiconductor (CMOS) process. The technique also allows to process larger size images by assembling 2-D arrays of such chips. Pixel operation exploits low-power mixed analog–digital circuit techniques. Because of the low currents involved (down

Abstract—An eye detection algorithm based on Convolutional Neural Networks (CNN) architecture was developed. The algorithm was designed to detect eyes in video images from a remote gaze estimation system that is part of a gaze-controlled human-computer interface. The CNN for eye detection has two stages of convolutional and sub-sampling layers followed by a fully connected feed forward neural network with a total of 1227 trainable parameters. Experiments with 3 subjects showed that for the full range of expected head movements, the CNN achieved a detection rate of 100%, for images with fully opened eyes, and a false alarm rate of 2.65 X 10-4 %. The CNN failed to detect eyes that were either partially or completely covered by the eyelids. The CNN for eye detection did not require pre-processing or normalization and was shown to be robust to changes in scale, rotation and illumination of the eyes.

Abstract—This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing–processing–learning–actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address–event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

Abstract-In this paper, we had studied that different types of classical raga music evokes different feelings and emotions. Certain raags produces peace happiness,cheerfullness, sad and Depression mood. According to Indian aesthetics, each poem or musical composition produces a certain rasa (emotion). Local Binary Patterns (LBP) have been well exploited for facial image analysis in the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. In this empirical paper we studied LBP Histogram (LBP) bins for the task of facial expression recognition while listening to Indian classical ragas. Our experiments illustrate that the selected LBP bins provide a compact and discriminative facial expression representation. The selected LBP bins will be used to obtain the best recognition performance rate on collected database. The local binary pattern (LBP) operator is defined as a grayscale invariant texture measure, derived from a general definition of texture in a local neighborhood. Due to its discriminative power and computational simplicity, the LBP texture operator has become a popular approach in various applications, including visual inspection, image retrieval, remote sensing, biomedical image analysis, motion analysis, environment modeling, and outdoor scene analysis.Subjective evaluation shows that Indian classical raags evokes certain emotions & feelings which can be reflect on the human face and was evaluated using LBP approach.

Abstract: Human Computer Interaction (HCI) is the biggest goal of computer vision researchers. Features form the different facial images are able to provide a very deep knowledge about the activities performed by the different facial movements. In this paper we presented a technique for feature extraction from various regions of interest with the help of Skin color segmentation technique, Thresholding, knowledge based technique for face recognition. Keywords: Skin color segmentation, Thresholding, HCI. considered as skin pixels in the respective regions. After the detection of non-skin pixels we apply the flood fill algorithm to find the curves from the nonskin pixels. The flood fill algorithm takes three parameters: a start node, a target color, and a replacement color [7]. The algorithm looks for all nodes in the array which are connected to the start node by a path of the target color, and changes them to the replacement color. Figure 1 shows the implementation of Flood-fill algorithm: I.

In this empirical paper, we had observed that Indian classical raaga music such as raag Khamaj and raag Darbari evokes different emotions. Raag Khamaj produces peace, happiness, cheerfulness and Raag Darbari produces sad and Depression mood. According to Indian aesthetics, each poem or musical composition produces a certain rasa (emotion). Local Binary Patterns (LBP) have been well exploited for facial image analysis in the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. In this empirical paper we studied LBP Histogram (LBP) bins for the task of facial expression recognition while listening to Indian classical ragas. Our experiments illustrate that the selected LBP bins provide a compact and discriminative facial expression representation. The selected LBP bins will be used to obtain the best recognition performance rate on collected database. The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Due to its discriminative power and computational simplicity, the LBP texture operator has become a popular approach in various applications, including visual inspection, image retrieval, remote sensing, biomedical image analysis, motion analysis, environment modeling, and outdoor scene analysis.Subjective evaluation shows that Indian classical raags evokes certain emotions & feelings which can be reflect on the human face and was evaluated using LBP approach.