11,332 research outputs found
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353
Information recovery from rank-order encoded images
The time to detection of a visual stimulus by the primate eye is recorded at
100 ā 150ms. This near instantaneous recognition is in spite of the considerable
processing required by the several stages of the visual pathway to recognise and
react to a visual scene. How this is achieved is still a matter of speculation.
Rank-order codes have been proposed as a means of encoding by the primate
eye in the rapid transmission of the initial burst of information from the sensory
neurons to the brain. We study the efficiency of rank-order codes in encoding
perceptually-important information in an image. VanRullen and Thorpe built a
model of the ganglion cell layers of the retina to simulate and study the viability
of rank-order as a means of encoding by retinal neurons. We validate their model
and quantify the information retrieved from rank-order encoded images in terms
of the visually-important information recovered. Towards this goal, we apply
the āperceptual information preservation algorithmā, proposed by Petrovic and
Xydeas after slight modification. We observe a low information recovery due
to losses suffered during the rank-order encoding and decoding processes. We
propose to minimise these losses to recover maximum information in minimum
time from rank-order encoded images. We first maximise information recovery by
using the pseudo-inverse of the filter-bank matrix to minimise losses during rankorder
decoding. We then apply the biological principle of lateral inhibition to
minimise losses during rank-order encoding. In doing so, we propose the Filteroverlap
Correction algorithm. To test the perfomance of rank-order codes in
a biologically realistic model, we design and simulate a model of the foveal-pit
ganglion cells of the retina keeping close to biological parameters. We use this
as a rank-order encoder and analyse its performance relative to VanRullen and
Thorpeās retinal model
Context Based Visual Content Verification
In this paper the intermediary visual content verification method based on
multi-level co-occurrences is studied. The co-occurrence statistics are in
general used to determine relational properties between objects based on
information collected from data. As such these measures are heavily subject to
relative number of occurrences and give only limited amount of accuracy when
predicting objects in real world. In order to improve the accuracy of this
method in the verification task, we include the context information such as
location, type of environment etc. In order to train our model we provide new
annotated dataset the Advanced Attribute VOC (AAVOC) that contains additional
properties of the image. We show that the usage of context greatly improve the
accuracy of verification with up to 16% improvement.Comment: 6 pages, 6 Figures, Published in Proceedings of the Information and
Digital Technology Conference, 201
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeonās navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Design, Manufacture, and Structural Dynamic Analysis of a Biomimetic Insect-Sized Wing for Micro Air Vehicles
The exceptional flying characteristics of airborne insects motivates the design of biomimetic wing structures that can exhibit a similar structural dynamic behavior. For this purpose, this investigation describes a method for both manufacturing a biomimetic insect-sized wing using the photolithography technique and analyzing its structural dynamic response. The geometry of a crane fly forewing (family Tipulidae) is acquired using a micro-computed tomography scanner. A computer-aided design model is generated from the measurements of the reconstructed scanned model of the insect wing to design the photomasks of the membrane and the venation network required for the photolithography procedure. A composite material wing is manufactured by patterning the venation network using photoresist SU-8 on a Kapton film for the assembling of the wing. A single material artificial wing is fabricated using the photoresist SU-8 for both the membrane and the network of veins. Experiments are conducted using a modal shaker and a digital image correlation (DIC) system to determine the natural frequencies and the mode shapes of the artificial wing from the fast Fourier transform of the displacement response of the wing. The experimental results are compared with those from a finite element (FE) model of the wing. A numerical simulation of the fluid-structure interaction is conducted by coupling the FE model of the artificial wing with a computational fluid dynamics model of the surrounding airflow. From these simulations, the deformation response and the coefficients of drag and lift of the artificial wing are predicted for different freestream velocities and angles of attack. Wind-tunnel experiments are conducted using the DIC system to determine the structural deformation response of the artificial wing under different freestream velocities and angles of attack. The vibration modes are dominated by a bending and torsional deformation response. The deformation along the span of the wing increases nonlinearly from the root of the wing to the tip of the wing with Reynolds number. The aerodynamic performance, defined as the ratio of the coefficient of lift to the coefficient of drag, of the artificial wing increases with Reynolds number and angle of attack up to the critical angle of attack
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