Inner and Inter Label Propagation: Salient Object Detection in the Wild

In this paper, we propose a novel label propagation based method for saliency detection. A key observation is that saliency in an image can be estimated by propagating the labels extracted from the most certain background and object regions. For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme. For images of complex scenes, we further deploy a 3-cue-center-biased objectness measure to pick out and propagate foreground labels. A co-transduction algorithm is devised to fuse both boundary and objectness labels based on an inter propagation scheme. The compactness criterion decides whether the incorporation of objectness labels is necessary, thus greatly enhancing computational efficiency. Results on five benchmark datasets with pixel-wise accurate annotations show that the proposed method achieves superior performance compared with the newest state-of-the-arts in terms of different evaluation metrics.Comment: The full version of the TIP 2015 publicatio

Memories for Life: A Review of the Science and Technology

This paper discusses scientific, social and technological aspects of memory. Recent developments in our understanding of memory processes and mechanisms, and their digital implementation, have placed the encoding, storage, management and retrieval of information at the forefront of several fields of research. At the same time, the divisions between the biological, physical and the digital worlds seem to be dissolving. Hence opportunities for interdisciplinary research into memory are being created, between the life sciences, social sciences and physical sciences. Such research may benefit from immediate application into information management technology as a testbed. The paper describes one initiative, Memories for Life, as a potential common problem space for the various interested disciplines

Combining shape and contour features to improve tool wear monitoring in milling processes

[EN] In this paper, a new system based on combinations of a shape descriptor and a contour descriptor has been proposed for classifying inserts in milling processes according to their wear level following a computer vision based approach. To describe the wear region shape we have proposed a new descriptor called ShapeFeat and its contour has been characterized using the method BORCHIZ that, to the best of our knowledge, achieves the best performance for tool wear monitoring following a computer vision-based approach. Results show that the combination of BORCHIZ with ShapeFeat using a late fusion method improves the classification performance significantly, obtaining an accuracy of 91.44% in the binary classification (i.e. the classification of the wear as high or low) and 82.90% using three target classes (i.e. classification of the wear as high, medium or low). These results outperform the ones obtained by both descriptors used on their own, which achieve accuracies of 88.70 and 80.67% for two and three classes, respectively, using ShapeFeat and 87.06 and 80.24% with B-ORCHIZ. This study yielded encouraging results for the manufacturing community in order to classify automatically the inserts in terms of their wear for milling processes.S

Myosin VIIA is required for aminoglycoside accumulation in cochlear hair cells.

Myosin VIIA is expressed by sensory hair cells and has a primary structure predicting a role in membrane trafficking and turnover, processes that may underlie the susceptibility of hair cells to aminoglycoside antibiotics. [3H]Gentamicin accumulation and the effects of aminoglycosides were therefore examined in cochlear cultures of mice with different missense mutations in the myosin VIIA gene, Myo7a, to see whether myosin VIIA plays a role in aminoglycoside ototoxicity. Hair cells from homozygous mutant Myo7a(sh1) mice, with a mutation in a non-conserved region of the myosin VIIA head, respond rapidly to aminoglycoside treatment and accumulate high levels of gentamicin. Hair cells from homozygous mutant Myo7a(6J) mice, with a mutation at a highly conserved residue close to the ATP binding site of the myosin VIIA head, do not accumulate [3H]gentamicin and are protected from aminoglycoside ototoxicity. Hair cells from heterozygotes of both alleles accumulate [3H]gentamicin and respond to aminoglycosides. Although aminoglycoside uptake is thought to be via apical surface-associated endocytosis, coated pit numbers on the apical membrane of heterozygous and homozygous Myo7a(6J) hair cells are similar. Pulse-chase experiments with cationic ferritin confirm that the apical endocytotic pathway is functional in homozygous Myo7a(6J) hair cells. Transduction currents can be recorded from both heterozygous and homozygous Myo7a(6J) hair cells, suggesting it is unlikely that the drug enters via diffusion through the mechanotransducer channel. The results show that myosin VIIA is required for aminoglycoside accumulation in hair cells. Myosin VIIA may transport a putative aminoglycoside receptor to the hair cell surface, indirectly translocate it to sites of membrane retrieval, or retain it in the endocytotic pathway

Intelligent multimedia indexing and retrieval through multi-source information extraction and merging

This paper reports work on automated meta-data\ud creation for multimedia content. The approach results\ud in the generation of a conceptual index of\ud the content which may then be searched via semantic\ud categories instead of keywords. The novelty\ud of the work is to exploit multiple sources of\ud information relating to video content (in this case\ud the rich range of sources covering important sports\ud events). News, commentaries and web reports covering\ud international football games in multiple languages\ud and multiple modalities is analysed and the\ud resultant data merged. This merging process leads\ud to increased accuracy relative to individual sources

Exploiting novel properties of space-filling curves for data analysis

Using space-filling curves to order multidimensional data has been found to be useful in a variety of application domains. This paper examines the space-filling curve induced ordering of multidimensional data that has been transformed using shape preserving transformations. It is demonstrated that, although the orderings are not invariant under these transformations, the probability of an ordering is dependent on the geometrical configuration of the multidimensional data. This novel property extends the potential applicability of space-filling curves and is demonstrated by constructing novel features for shape matching