Illumination invariant facial recognition using a piecewise-constant lighting model

In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.

Programmable image processing chip

A new high performance, programmable image processing chip targeted at video and HDTV applications is described. This was initially developed for image small object recognition but has much broader functional application including 1D and 2D FIR filtering as well as neural network computation. The core of the circuit is made up of an array of twenty one multiplication-accumulation cells based on systolic architecture. Devices can be cascaded to increase the order of the filter both vertically and horizontally. The chip has been fabricated in a 0.6 µ, low power CMOS technology and operates on 10 bit input data at over 54 Megasamples per second. The introduction gives some background to the chip design and highlights that there are few other comparable devices. Section 2 gives a brief introduction to small object detection. The chip architecture and the chip design will be described in detail in the later sections.

MRZ-99030 – a novel modulator of Aβ aggregation: II – reversal of Aβ oligomer-induced deficits in long-term potentiation (LTP) and cognitive performance in rats and mice.

β-amyloid1-42 (Aβ1-42) is a major endogenous pathogen underlying the aetiology of Alzheimer's disease (AD). Recent evidence indicates that soluble Aβ oligomers, rather than plaques, are the major cause of synaptic dysfunction and neurodegeneration. Small molecules that suppress Aβ aggregation, reduce oligomer stability or promote off-pathway non-toxic oligomerization represent a promising alternative strategy for neuroprotection in AD. MRZ-99030 was recently identified as a dipeptide that modulates Aβ1-42 aggregation by triggering a non-amyloidogenic aggregation pathway, thereby reducing the amount of intermediate toxic soluble oligomeric Aβ species. The present study evaluated the relevance of these promising results with MRZ-99030 under pathophysiological conditions i.e. against the synaptotoxic effects of Aβ oligomers on hippocampal long term potentiation (LTP) and two different memory tasks. Aβ1-42 interferes with the glutamatergic system and with neuronal Ca2+ signalling and abolishes the induction of LTP. Here we demonstrate that MRZ-99030 (100–500 nM) at a 10:1 stoichiometric excess to Aβ clearly reversed the synaptotoxic effects of Aβ1-42 oligomers on CA1-LTP in murine hippocampal slices. Co-application of MRZ-99030 also prevented the two-fold increase in resting Ca2+ levels in pyramidal neuron dendrites and spines triggered by Aβ1-42 oligomers. In anaesthetized rats, pre-administration of MRZ-99030 (50 mg/kg s.c.) protected against deficits in hippocampal LTP following i.c.v. injection of oligomeric Aβ1-42. Furthermore, similar treatment significantly ameliorated cognitive deficits in an object recognition task and under an alternating lever cyclic ratio schedule after the i.c.v. application of Aβ1-42 and 7PA2 conditioned medium, respectively. Altogether, these results demonstrate the potential therapeutic benefit of MRZ-99030 in AD.

A biologically inspired appearance model for robust visual tracking

In this work, we propose a biologically inspired appearance model for robust visual tracking. Motivated in part by the success of the hierarchical organization of the primary visual cortex (area V1), we establish an architecture consisting of five layers: whitening, rectification, normalization, coding and polling. The first three layers stem from the models developed for object recognition. In this paper, our attention focuses on the coding and pooling layers. In particular, we use a discriminative sparse coding method in the coding layer along with spatial pyramid representation in the pooling layer, which makes it easier to distinguish the target to be tracked from its background in the presence of appearance variations. An extensive experimental study shows that the proposed method has higher tracking accuracy than several state-of-the-art trackers.

Object tracking using SIFT features and mean shift

A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation–maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.

Brief review of invariant texture analysis methods

This paper considers invariant texture analysis. Texture analysis approaches whose performances are not affected by translation, rotation, affine, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classified into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented first. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The focus of possible future work is also suggested.