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Long-term knowledge acquisition in a memory-based epigenetic robot architecture for verbal interaction

Long-term knowledge acquisition in a memory-based epigenetic...

引用

ieee International workshop on Robot and Human Communication (ROMAN)

作者： Ferdian Pratama Fulvio Mastrogiovanni Sungmoon Jeong Nak Young Chong School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Informatics University of Genoa Italy

We present a robot cognitive framework based on (a) a memory-like architecture; and (b) the notion of “context”. We posit that relying solely on machine learning techniques may not be the right approach for a long-term, continuous knowledge acquisition. Since we are interested in long-term human-robot interaction, we focus on a scenario where a robot “remembers” relevant events happening in the environment. By visually sensing its surroundings, the robot is expected to infer and remember snapshots of events, and recall specific past events based on inputs and contextual information from humans. Using a COTS vision frameworks for the experiment, we show that the robot is able to form “memories” and recall related events based on cues and the context given during the human-robot interaction process.

关键词： Context visualization Robot sensing systems Human-robot interaction Memory architecture Knowledge acquisition

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Live demonstration: The “DAVIS” Dynamic and Active-Pixel vision Sensor

Live demonstration: The “DAVIS” Dynamic and Active-Pixel V...

引用

ieee International Symposium on Circuits and Systems (ISCAS)

作者： C. Brandli R. Berner M. Yang S.C. Liu V. Villeneuva T. Delbruck Institute of Neuroinformatics University of Zurich and ETH Zurich Switzerland

ISBN: (纸本)9781479934331

This demonstration will show the features of the Dynamic and Active-Pixel vision Sensor (DAVIS) reported at the VLSI Symposium and the International Imager Sensor workshop in 2013. This sensor concurrently outputs conventional CMOS image sensor frames and sparse, low-latency dynamic vision sensor events from the same pixels, sharing the same photodiodes. The setup will allow visitors to explore the advantages of combining of fast and computationally-efficient neuromorphic event-driven vision with the existing body of methods for frame-based computer and machine vision.

关键词： Robot sensing systems Voltage control machine vision Cameras visualization Software algorithms Very large scale integration

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Selection of universal features for image classification

Selection of universal features for image classification

引用

ieee workshop on Applications of Computer vision (WACV)

作者： Pedro A. Rodriguez Nathan Drenkow Daniel DeMenthon Zachary Koterba Kathleen Kauffman Duane Cornish Bart Paulhamus R. Jacob Vogelstein Johns Hopkins University Applied Physics Laboratory Maryland USA

Neuromimetic algorithms, such as the HMAX algorithm, have been very successful in image classification tasks. However, current implementations of these algorithms do not scale well to large datasets. Often, target-specific features or patches are “learned” ahead of time and then correlated with test images during feature extraction. In this paper, we develop a novel method for selecting a single set of universal features that enables classification across a broad range of image classes. Our method trains multiple Random Forest classifiers using a large dictionary of features and then combines them using a majority voting scheme. This enables the selection of the most discriminative patches based on feature importance measures. Experiments demonstrate the viability of this method using HMAX features as well as the tradeoff between the number of universal features, classification performance, and processing time.

关键词： Feature extraction Training visualization Vegetation TV machine learning algorithms Vectors

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AOA: Ambient obstacle avoidance interface

AOA: Ambient obstacle avoidance interface

引用

ieee International workshop on Robot and Human Communication (ROMAN)

作者： Wei Liang Kenny Chua Matthew Johnson Tom Eskridge Brenan Keller DSO National Laboratories Singapore Florida Institute of Human Machine Cognition (IHMC) Pensacola Florida USA

In this paper, we present a novel interface for teleoperating ground vehicles. Obstacle avoidance with ground vehicles demands a high level of operator attention, typically distracting from the primary mission. The Ambient Obstacle Avoidance (AOA) was designed to allow operators to effectively perform a primary task, such as search, while still effectively avoiding obstacles. The AOA wraps around a standard video interface and provides range information without requiring a separate screen. AOA combines and reduces different data streams into proportionately scaled symbology that directly shows important relationships between vehicle width, vehicle orientation and obstacles in the environment. The use of the AOA interface was tested in both simulation and on physical robots. Results from both tests show an improvement in obstacle avoidance during navigation with the AOA. In addition, results from the simulation test indicate that operators using AOA were able to leverage ambient vision such that the primary visual task was not impeded.

关键词： Vehicles Robots Navigation visualization Collision avoidance Cameras Training

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Autonomous navigation and sign detector learning

Autonomous navigation and sign detector learning

引用

ieee workshop on Robot vision (WORV)

作者： Liam Ellis Nicolas Pugeault Kristoffer Öfjäll Johan Hedborg Richard Bowden Michael Felsberg CVL Linköping University Linkoping Sweden CVSSP University of Surrey Guildford UK

This paper presents an autonomous robotic system that incorporates novel Computer vision, machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time.

关键词： visualization Navigation Feature extraction Robots Training Trajectory Detectors

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Visual hull reconstruction for automated primate behavior observation

Visual hull reconstruction for automated primate behavior ob...

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ieee workshop on machine Learning for Signal Processing

作者： Nastaran Ghadar Xikang Zhang Kang Li Deniz Erdogmus Guillaume Thibault Alireza Bayestehtashk Izhak Shafran Kris Coleman Kathleen A. Grant CSL Northeastern University OHSU Oregon National Primate Research Center

The study of social animal interactions is used as means for understanding animal behavior and biology. In this work, we describe a computerized method that utilizes 3D visual hull reconstruction to identify and localize rhesus macaques in their social groups. There are three major steps in this study. First, we collect experimental data from four synchronized cameras at different locations and angels in a cage containing five rhesus macaques. Second, by using computer vision algorithms, we detect and identify animals using 2D observations that were provided from the previous step. This provides essential quantitative data for animal behavior research. Finally, by applying visual hull reconstruction algorithm, we automatically build a 3D model for each rhesus macaques on every frame. The results of this work can be used for tracking these animals in their cage, and furthermore it can be used for activity recognition of social interactions of rhesus macaques. The method we developed in this paper, shows promising results that are accurate, yet runs in a timely manner; this makes this algorithm suitable for large datasets and we can use it for future high-level recognition tasks.

关键词： Three-dimensional displays visualization Cameras Feature extraction Animals Shape Image reconstruction

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Apparel silhouette attributes recognition

Apparel silhouette attributes recognition

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ieee workshop on Applications of Computer vision (WACV)

作者： Wei Zhang Emilio Antunez Salih Gokturk Baris Sumengen Google Inc. Mountain View CA USA

Computer vision and machine learning have great potential to aid in aesthetic judgments and exploration, particularly in the understanding of shapes. This paper presents our work in a well-defined but largely unexplored problem in this field: the automated recognition of apparel silhouette attributes for real-world products. Silhouette attributes, such as v-neck for dresses and open toe for shoes, are very important attributes for understanding the appearance of apparel but difficult to recognize automatically. We propose methods employing multi-modal features and supervised learning to automatically recognize silhouette attributes based on product images and the associated text. These algorithms are extensively tested on a large dataset of dresses, tops, and shoes provided by online retailers. The proposed silhouette recognition approach achieves high recognition accuracy on the nine silhouette categories. Our approach and experiments are also expected to stimulate future research on this topic.

关键词： Feature extraction Shape visualization Footwear Dictionaries Skin

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Indian Classical Dance classification by learning dance pose bases

Indian Classical Dance classification by learning dance pose...

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ieee workshop on Applications of Computer vision (WACV)

作者： Soumitra Samanta Pulak Purkait Bhabatosh Chanda Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

In this paper, we address an interesting application of computer vision technique, namely classification of Indian Classical Dance (ICD). With the best of our knowledge, the problem has not been addressed so far in computer vision domain. To deal with this problem, we use a sparse representation based dictionary learning technique. First, we represent each frame of a dance video by a pose descriptor based on histogram of oriented optical flow (HOOF), in a hierarchical manner. The pose basis is learned using an on-line dictionary learning technique. Finally each video is represented sparsely as a dance descriptor by pooling pose descriptor of all the frames. In this work, dance videos are classified using support vector machine (SVM) with intersection kernel. Our contribution here are two folds. First, to address dance classification as a new problem in computer vision and second, to present a new action descriptor to represent a dance video which overcomes the problem of the “Bags-of-Words” model. We have tested our algorithm on our own ICD dataset created from the videos collected from YouTube. An accuracy of 86.67% is achieved on this dataset. Since we have proposed a new action descriptor too, we have tested our algorithm on well known KTH dataset. The performance of the system is comparable to the state-of-the-art.

关键词： Dictionaries Humans visualization Vectors Kernel Encoding Computer vision

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Intelligent selective perception of visual information in vision systems

Intelligent selective perception of visual information in vi...

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ieee International workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Vitaliy Boyun V.M. Glushkov Institute of Cybernetics National Academy of Science of Ukraine Ukraine

An intelligent selective perception of visual information has been considered. It takes into consideration such structural peculiarities of visual analyzer construction as wide field of view with a low resolution ability of peripheral retina and sharp central vision in a fovea, mechanisms of attention and adaptation and different types of eye movement.

关键词： machine vision Brightness Cameras Sensors visualization Entropy Artificial intelligence

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An outlier rejection scheme for optical flow tracking

An outlier rejection scheme for optical flow tracking

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ieee workshop on machine Learning for Signal Processing

作者： Minghao Yang Jianhua Tao Lihui Shi Kaihui Mu Jianfeng Che National Laboratory of Pattern Recognition Institute of Automation Institute of Equipment Technology of Artillery and Air Defense Chinese Academy of Sciences (CAS) China

ISBN: (纸本)9781457716218

During tracking process in optical flow, some points are normally easy to be lost or become outliers, due to the fact that the object undergoes changes of illumination or becomes partially occluded. The paper presents a novel scheme, Heterogeneity Elimination Individually (HEI), for outlier rejections from the tracking results of optical flow. HEI determines the most poisonous element by the distance between the tracked point and the projected point which is determined by its complements set from the source to the target one. Then HEI eliminates the farthest point every time from the element heterogeneous sequence until all remaining points are within the error tolerance and ensures the accuracy of the optical flow tracking results. Finally, we make an experiment by comparing our method with a popularly used method, RANSAC. The comparison results show the validity and effectiveness of our proposed method for outlier rejection.

关键词： Adaptive optics Cameras Optical signal processing Computer vision Target tracking visualization Real time systems

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