Exercise Does Not Effect Context-dependent Episodic Memory

Memory has been shown to be strongly associated with the context in which it is encoded, suggesting that the context is central to the memory itself. However, the effect of exercise on context dependent object recognition is not fully known. We then set out to investigate the effect of exercise on context dependent object recognition. In Experiment 1 we showed that a context change reduced object recognition memory but did not significantly disrupt object recognition. In Experiment 2 we assessed whether exercise would the mitigate the effect of context change. We showed that exercise does not significantly improve object recognition nor did it mitigate the effect of context change on object recognition. These results suggest that a discrete context change can significantly disrupt retrieval of object recognition memory. Our results do not agree with the body of literature related to this topic, so further inquisition into these effects should be undertaken to confirm or refute the impact of exercise on contextual object recognition

Monocular SLAM Supported Object Recognition

In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.Comment: Accepted to appear at Robotics: Science and Systems 2015, Rome, Ital

Hydrodynamic object recognition using pressure sensing

Hydrodynamic sensing is instrumental to fish and some amphibians. It also represents, for underwater vehicles, an alternative way of sensing the fluid environment when visual and acoustic sensing are limited. To assess the effectiveness of hydrodynamic sensing and gain insight into its capabilities and limitations, we investigated the forward and inverse problem of detection and identification, using the hydrodynamic pressure in the neighbourhood, of a stationary obstacle described using a general shape representation. Based on conformal mapping and a general normalization procedure, our obstacle representation accounts for all specific features of progressive perceptual hydrodynamic imaging reported experimentally. Size, location and shape are encoded separately. The shape representation rests upon an asymptotic series which embodies the progressive character of hydrodynamic imaging through pressure sensing. A dynamic filtering method is used to invert noisy nonlinear pressure signals for the shape parameters. The results highlight the dependence of the sensitivity of hydrodynamic sensing not only on the relative distance to the disturbance but also its bearing

Deep Affordance-grounded Sensorimotor Object Recognition

It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has recently motivated the "sensorimotor" approach to the challenging task of automatic object recognition, where both information sources are fused to improve robustness. In this work, the aforementioned paradigm is adopted, surpassing current limitations of sensorimotor object recognition research. Specifically, the deep learning paradigm is introduced to the problem for the first time, developing a number of novel neuro-biologically and neuro-physiologically inspired architectures that utilize state-of-the-art neural networks for fusing the available information sources in multiple ways. The proposed methods are evaluated using a large RGB-D corpus, which is specifically collected for the task of sensorimotor object recognition and is made publicly available. Experimental results demonstrate the utility of affordance information to object recognition, achieving an up to 29% relative error reduction by its inclusion.Comment: 9 pages, 7 figures, dataset link included, accepted to CVPR 201

A correspondence-based neural mechanism for position invariant feature processing

Poster presentation: Introduction We here focus on constructing a hierarchical neural system for position-invariant recognition, which is one of the most fundamental invariant recognition achieved in visual processing [1,2]. The invariant recognition have been hypothesized to be done by matching a sensory image of a particular object stimulated on the retina to the most suitable representation stored in memory of the higher visual cortical area. Here arises a general problem: In such a visual processing, the position of the object image on the retina must be initially uncertain. Furthermore, the retinal activities possessing sensory information are being far from the ones in the higher area with a loss of the sensory object information. Nevertheless, with such recognition ambiguity, the particular object can effortlessly and easily be recognized. Our aim in this work is an attempt to resolve such a general recognition problem. ..

The scene superiority effect: object recognition in the context of natural scenes

Four experiments investigate the effect of background scene semantics on object recognition. Although past research has found that semantically consistent scene backgrounds can facilitate recognition of a target object, these claims have been challenged as the result of post-perceptual response bias rather than the perceptual processes of object recognition itself. The current study takes advantage of a paradigm from linguistic processing known as the Word Superiority Effect. Humans can better discriminate letters (e.g., D vs. K) in the context of a word (WORD vs. WORK) than in a non-word context (e.g., WROD vs. WROK) even when the context is non-predictive of the target identity. We apply this paradigm to objects in natural scenes, having subjects discriminate between objects in the context of scenes. Because the target objects were equally semantically consistent with any given scene and could appear in either semantically consistent or inconsistent contexts with equal probability, response bias could not lead to an apparent improvement in object recognition. The current study found a benefit to object recognition from semantically consistent backgrounds, and the effect appeared to be modulated by awareness of background scene semantics

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.Comment: 12 page