Instance-Level Salient Object Segmentation

Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation.Comment: To appear in CVPR201

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Silicon nanophotonic ring resonators sensors integrated in reaction tubes

Enzyme-linked immunosorbent assays (ELISA) are the most popular immunoassay techniques performed every day in hospitals and laboratories and they are used as a diagnostic tool in medicine and plant pathology, as well as a quality-control check in various industries. However, complex labeling techniques are required to be able to perform the assay and non-specific binding and endpoint timing are difficult to optimize. These issues could be solved by label-free techniques such as silicon nanophotonic microring resonator sensors, but this platform requires complex microfluidics, which is very much removed from the daily practice in e. g. hospital labs, which still relies to a large degree on platforms like 96-well microtiter plates or reaction tubes. To address these issues, here, we propose the combination of a simple and compatible reaction tube platform with label free silicon-on-insulator (SOI) photonic biosensors, where the flow is through the sensor chip as opposed to over the chip as in more conventional approaches. This device allows real time detection and analysis. Its great flexibility and small footprint make it ideal for an easy handling in any laboratory