Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an - also unlabeled - distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformation

Adaptive scene dependent filters for segmentation and online learning of visual objects

Steil JJ, Götting M, Wersing H, Körner E, Ritter H. Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing. 2007;70(7-9):1235-1246

Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the “stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells’ input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an – also unlabeled – distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations

Structural analysis of the adenovirus type 2 E3/19K protein using mutagenesis and a panel of conformation-sensitive monoclonal antibodies

The E3/19K protein of human adenovirus type 2 (Ad2) was the first viral protein shown to interfere with antigen presentation. This 25 kDa transmembrane glycoprotein binds to major histocompatibility complex (MHC) class I molecules in the endoplasmic reticulum (ER), thereby preventing transport of newly synthesized peptide–MHC complexes to the cell surface and consequently T cell recognition. Recent data suggest that E3/19K also sequesters MHC class I like ligands intracellularly to suppress natural killer (NK) cell recognition. While the mechanism of ER retention is well understood, the structure of E3/19K remains elusive. To further dissect the structural and antigenic topography of E3/19K we carried out site-directed mutagenesis and raised monoclonal antibodies (mAbs) against a recombinant version of Ad2 E3/19K comprising the lumenal domain followed by a C-terminal histidine tag. Using peptide scanning, the epitopes of three mAbs were mapped to different regions of the lumenal domain, comprising amino acids 3–13, 15–21 and 41–45, respectively. Interestingly, mAb 3F4 reacted only weakly with wild-type E3/19K, but showed drastically increased binding to mutant E3/19K molecules, e.g. those with disrupted disulfide bonds, suggesting that 3F4 can sense unfolding of the protein. MAb 10A2 binds to an epitope apparently buried within E3/19K while that of 3A9 is exposed. Secondary structure prediction suggests that the lumenal domain contains six β-strands and an α-helix adjacent to the transmembrane domain. Interestingly, all mAbs bind to non-structured loops. Using a large panel of E3/19K mutants the structural alterations of the mutations were determined. With this knowledge the panel of mAbs will be valuable tools to further dissect structure/function relationships of E3/19K regarding down regulation of MHC class I and MHC class I like molecules and its effect on both T cell and NK cell recognition

Toward Self-Referential Autonomous Learning of Object and Situation Models

Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms