A Learning Framework for Morphological Operators using Counter-Harmonic Mean

We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.Comment: Submitted to ISMM'1

Expanding the Family of Grassmannian Kernels: An Embedding Perspective

Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear subspaces do not obey Euclidean geometry, but lie on a special type of Riemannian manifolds known as Grassmannian. To leverage the techniques developed for Euclidean spaces (e.g, support vector machines) with subspaces, several recent studies have proposed to embed the Grassmannian into a Hilbert space by making use of a positive definite kernel. Unfortunately, only two Grassmannian kernels are known, none of which -as we will show- is universal, which limits their ability to approximate a target function arbitrarily well. Here, we introduce several positive definite Grassmannian kernels, including universal ones, and demonstrate their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance

Wafer scale manufacturing of high precision micro-optical components through X-ray lithography yielding 1800 Gray Levels in a fingertip sized chip

We present a novel x-ray lithography based micromanufacturing methodology that offers scalable manufacturing of high precision optical components. It is accomplished through simultaneous usage of multiple stencil masks made moveable with respect to one another through custom made micromotion stages. The range of spectral flux reaching the sample surface at the LiMiNT micro/nanomanufacturing facility of Singapore Synchrotron Light Source (SSLS) is about 2 keV to 10 keV, offering substantial photon energy to carry out deep x-ray lithography. In this energy range, x-rays penetrate through resist materials with only little scattering. The highly collimated rectangular beam architecture of the x-ray source enables a full 4″ wafer scale fabrication. Precise control of dose deposited offers determined chain scission in the polymer to required depth enabling 1800 discrete gray levels in a chip of area 20 mm$^{2}$ and with more than 2000 within our reach. Due to its parallel processing capability, our methodology serves as a promising candidate to fabricate micro/nano components of optical quality on a large scale to cater for industrial requirements. Usage of these fine components in analytical devices such as spectrometers and multispectral imagers transforms their architecture and shrinks their size to pocket dimension. It also reduces their complexity and increases affordability while also expanding their application areas. Consequently, equipment based on these devices is made available and affordable for consumers and businesses expanding the horizon of analytical applications. Mass manufacturing is especially vital when these devices are to be sold in large quantities especially as components for original equipment manufacturers (OEM), which has also been demonstrated through our work. Furthermore, we also substantially improve the quality of the micro-components fabricated, 3D architecture generated, throughput, capability and availability for industrial application. Manufacturing 1800 Gray levels or more through other competing techniques is either limited due to multiple process steps involved or due to unacceptably long time required owing to their pencil beam architecture. Our manufacturing technique presented here overcomes both these shortcomings in terms of the maximum number of gray levels that can be generated, and the time required to generate the same

Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes

Publisher Correction: Deep learning enables fast and dense single-molecule localization with high accuracy

In the version of this Article initially published, Jacob H. Macke and Jonas Ries were not listed as corresponding authors. Their contact information and designation as corresponding authors are now included. The error has been corrected in the online version of the Article

Integrated Physiological, Biochemical, and Molecular Analysis Identifies Important Traits and Mechanisms Associated with Differential Response of Rice Genotypes to Elevated Temperature

In changing climate, heat stress caused by high temperature poses a serious threat to rice cultivation. A multiple organizational analysis at physiological, biochemical and molecular level is required to fully understand the impact of elevated temperature in rice. This study was aimed at deciphering the elevated temperature response in eleven popular and mega rice cultivars widely grown in India. Physiological and biochemical traits specifically membrane thermostability (MTS), antioxidants, and photosynthesis were studied at vegetative and reproductive phases which were used to establish a correlation with grain yield under stress. Several useful traits in different genotypes were identified which will be important resource to develop high temperature tolerant rice cultivars. Interestingly, Nagina22 emerged as best performer in terms of yield as well as expression of physiological and biochemical traits at elevated temperature. It showed lesser relative injury, lesser reduction in chlorophyll content, increased super oxide dismutase, catalase and peroxidase activity, lesser reduction in net photosynthetic rate (PN), high transpiration rate (E) and other photosynthetic/ fluorescence parameters contributing to least reduction in spikelet fertility and grain yield at elevated temperature. Further, expression of 14 genes including heat shock transcription factors and heat shock proteins was analyzed in Nagina22 (tolerant) and Vandana (susceptible) at flowering phase, strengthening the fact that N22 performs better at molecular level also during elevated temperature. This study shows that elevated temperature response is complex and involves multiple biological processes which are needed to be characterized to address the challenges of future climate extreme conditions

Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit

Neural information processing systems foundation. All rights reserved. Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance

Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification

Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad