The Whole World in Your Hand: Active and Interactive Segmentation

Object segmentation is a fundamental problem in computer vision and a powerful resource for development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided by the presence of a hand or arm in the proximity of the object to be segmented. The first approach is suitable for a robotic system, where the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's perspective, with instrumentation to help detect and segment objects that are held in the wearer's hand. The third method operates when observing a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it as a seed for segmentation. We show that object segmentation can serve as a key resource for development by demonstrating methods that exploit high-quality object segmentations to develop both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities (object recognition and localization)

Event-driven visual attention for the humanoid robot iCub.

Fast reaction to sudden and potentially interesting stimuli is a crucial feature for safe and reliable interaction with the environment. Here we present a biologically inspired attention system developed for the humanoid robot iCub. It is based on input from unconventional event-driven vision sensors and an efficient computational method. The resulting system shows low-latency and fast determination of the location of the focus of attention. The performance is benchmarked against an instance of the state of the art in robotics artificial attention system used in robotics. Results show that the proposed system is two orders of magnitude faster that the benchmark in selecting a new stimulus to attend

Modeling speech imitation and ecological learning of auditory-motor maps.

Classical models of speech consider an antero-posterior distinction between perceptive and productive functions. However, the selective alteration of neural activity in speech motor centers, via transcranial magnetic stimulation, was shown to affect speech discrimination. On the automatic speech recognition (ASR) side, the recognition systems have classically relied solely on acoustic data, achieving rather good performance in optimal listening conditions. The main limitations of current ASR are mainly evident in the realistic use of such systems. These limitations can be partly reduced by using normalization strategies that minimize inter-speaker variability by either explicitly removing speakers' peculiarities or adapting different speakers to a reference model. In this paper we aim at modeling a motor-based imitation learning mechanism in ASR. We tested the utility of a speaker normalization strategy that uses motor representations of speech and compare it with strategies that ignore the motor domain. Specifically, we first trained a regressor through state-of-the-art machine learning techniques to build an auditory-motor mapping, in a sense mimicking a human learner that tries to reproduce utterances produced by other speakers. This auditory-motor mapping maps the speech acoustics of a speaker into the motor plans of a reference speaker. Since, during recognition, only speech acoustics are available, the mapping is necessary to "recover" motor information. Subsequently, in a phone classification task, we tested the system on either one of the speakers that was used during training or a new one. Results show that in both cases the motor-based speaker normalization strategy slightly but significantly outperforms all other strategies where only acoustics is taken into account

Perbandingan Cross-product Dan Subset Query Pada Multiple Relasi Dengan Metode Cost-based

Ada beberapa model query yang digunakan untuk mengakses data pada 2 tabel atau lebih dalam basis data relasi. Dua model query yang umum antara lain adalah cross product dan subset query, dimana kedua model ini dapat menghasilkan data yang sama. Namun perlu diperhatikan cara mana yang lebih optimal sehingga pada akhirnya didapatkan query dengan waktu akses yang paling minimum. Dengan menggunakan basis data Oracle 10g Express Edition akan dilakukan penelitian untuk mencari model query yang lebih optimal dengan metode cost-based. Parameter yang akan dibandingkan adalah harga/biaya dan waktu yang dihasilkan pada perencanaan eksekusi. Penelitian dilakukan dengan pengelompokkan uji data seperti jumlah data, jumlah relasi, akses tabel penuh atau sebagian dan pengindeksan

Incremental Robot Learning of New Objects with Fixed Update Time

8 pages, 3 figuresWe consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presented algorithm addresses the problem of having an unbalanced proportion of training examples per class, which occurs when new objects are presented to the system for the first time. We evaluate our algorithm on both a machine learning benchmark dataset and two challenging object recognition tasks in a robotic setting. Empirical evidence shows that our approach achieves comparable or higher classification performance than its batch counterpart when classes are unbalanced, while being significantly faster

SAI: A sensible artificial intelligence that plays with handicap and targets high scores in 9x9 Go

We develop a new framework for the game of Go to target a high score, and thus a perfect play. We integrate this framework into the Monte Carlo tree search - policy iteration learning pipeline introduced by Google DeepMind with AlphaGo. Training on 9×9 Go produces a superhuman Go player, thus proving that this framework is stable and robust. We show that this player can be used to effectively play with both positional and score handicap. We develop a family of agents that can target high scores against any opponent, recover from very severe disadvantage against weak opponents, and avoid suboptimal moves

iCub World: Friendly Robots Help Building Good Vision Data-Sets

CVPR2013 Workshop: Ground Truth - What is a good dataset?. Portland, USA (June 28, 2013In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform. Our set up allows for rapid acquisition and annotation of data with corresponding ground truth. While more constrained in its scopes -- the iCub world is essentially a robotics research lab -- we demonstrate how the proposed data-set poses challenges to current recognition systems. The iCubWorld data-set is publicly available. The data-set can be downloaded from: http://www.iit.it/en/projects/data-sets.html

Bentonite Clay Modified Concrete

Replacing cement with pozzolanic materials to some extent in construction is found to be one of the sustainable approaches in the construction industry. Pozzolanic materials of industrial origin like fly ash and Ground Granulated Blast furnace Slag will have to be replaced with natural pozzolanic materials once the world moves towards renewable energy sources. Bentonite is one such pozzolanic clay material that is rich in SiO2 content. A little research was made to assess the performance of bentonite modified concrete. Based on those, an improvement in the fresh, hardened, durability properties was reported. This chapter presents the current scenario on the development of bentonite modified concrete. It also reviews the literature about the physical & chemical properties of bentonite, bentonite blended cement mortar, bentonite modified cement concrete, and reinforced concrete. The history and development of Bentonite modified concrete were also briefly presented in this chapter

Tuning adsorption properties of GaxIn2−xO3 catalysts for enhancement of methanol synthesis activity from CO2 hydrogenation at high reaction temperature

Light olefins can be produced from CO2 hydrogenation in a single reactor using a combination of a methanol synthesis catalyst and a methanol-to-olefin (MTO) catalyst. However, commercial methanol synthesis catalysts are active at low temperatures (200–260 °C), while MTO reaction is feasible at higher temperatures (>300 °C). Herein, we report the CO2 hydrogenation to methanol at high temperatures (320–400 °C) over GaxIn2−xO3 catalysts. By tuning the Ga/In ratios, phase, crystallinity, pore structure, morphology, electronic properties as well as adsorptive properties of GaxIn2−xO3 catalysts can be modified. At the lowest temperature (320 °C), the pure In2O3 shows the highest methanol yield. However, the maximum methanol yield declines significantly with increasing reaction temperatures. Incorporation of Ga into the In2O3 crystal lattices at x = 0.4 (Ga0.4In1.6O3) maximizes the methanol yield at higher reaction temperatures of 340–360 °C. This enhancement can be attributed to an increased binding energy of adsorptive molecules with the catalyst surface to promote the hydrogenation of CO2 to methanol. Further increasing Ga content (x > 0.4) leads to greatly strengthen the binding for adsorptive molecules, resulting in a lower methanol yield and the formation of methane. The surface chemisorbed oxygen is found to be a key factor determining the CO yield