Domain Adaptive Neural Networks for Object Recognition

We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks

SenseCam image localisation using hierarchical SURF trees

The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further

FPGA-based module for SURF extraction

We present a complete hardware and software solution of an FPGA-based computer vision embedded module capable of carrying out SURF image features extraction algorithm. Aside from image analysis, the module embeds a Linux distribution that allows to run programs specifically tailored for particular applications. The module is based on a Virtex-5 FXT FPGA which features powerful configurable logic and an embedded PowerPC processor. We describe the module hardware as well as the custom FPGA image processing cores that implement the algorithm's most computationally expensive process, the interest point detection. The module's overall performance is evaluated and compared to CPU and GPU based solutions. Results show that the embedded module achieves comparable disctinctiveness to the SURF software implementation running in a standard CPU while being faster and consuming significantly less power and space. Thus, it allows to use the SURF algorithm in applications with power and spatial constraints, such as autonomous navigation of small mobile robots

Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

Angular variation of the magnetoresistance of the superconducting ferromagnet UCoGe

We report a magnetoresistance study of the superconducting ferromagnet UCoGe. The data, taken on single-crystalline samples, show a pronounced structure at $B^* = 8.5$~T for a field applied along the ordered moment $m_0$. Angle dependent measurements reveal this field-induced phenomenon has an uniaxial anisotropy. Magnetoresistance measurements under pressure show a rapid increase of $B^*$ to 12.8~T at 1.0~GPa. We discuss $B^*$ in terms of a field induced polarization change. Upper critical field measurements corroborate the unusual S-shaped $B_{c2}(T)$-curve for a field along the $b$-axis of the orthorhombic unit cell.Comment: 6 pages, 5 figures; accepted for publication in Phys. Rev.

Superconductivity and magnetic order in the non-centrosymmetric Half Heusler compound ErPdBi

We report superconductivity at $T_c = 1.22$ K and magnetic order at $T_N = 1.06$ K in the semi-metallic noncentrosymmetric Half Heusler compound ErPdBi. The upper critical field, $B_{c2}$, has an unusual quasi-linear temperature variation and reaches a value of 1.6 T for $T \rightarrow 0$. Magnetic order is found below $T_c$ and is suppressed at $B{_M} \sim 2.5$ T for $T \rightarrow 0$. Since $T_c \simeq T_N$, the interaction of superconductivity and magnetism is expected to give rise to a complex ground state. Moreover, electronic structure calculations show ErPdBi has a topologically nontrivial band inversion and thus may serve as a new platform to study the interplay of topological states, superconductivity and magnetic order.Comment: 6 pages, 5 figures; accepted for publication in Europhysics Letter

Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7