799 research outputs found
BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM
Object Recognition has been a field of interest to many researchers. In fact, it has been
referred to as the most important problem in machine or computer vision. Researchers
have developed many algorithms to solve the problem of object recognition that are
machine vision motivated. On the other hand, biology has motivated researchers to study
the visual system of humans and animals such as monkeys and map it into a
computational model. Some of these models are based on the feed-forward mechanism
of information communication in cortex where the information is communicated
between the different visual areas from the lower areas to the top areas in a feed-forward
manner; however, the performance of these models has been affected much by the
increase of clutter in the scene as well as occlusion. Another mechanism of information
processing in the cortex is called the feedback mechanism, where the information from
the top areas in the visual system is communicated to the lower areas in a feedback
manner; this mechanism has also been mapped into computational models. All these
models which are based on the feed-forward or feedback mechanisms have shown
promising results. However, during the testing of these models, there have been some
issues that affect their performance such as occlusion that prevents objects from being
visible. In addition, scenes that contain high amounts of clutter in them, where there are
so many objects, have also affected the performance of these models. In fact, the
performance has been reported to drop to 74% when systems that are based on these
models are subjected to one or both of the issues mentioned above. The human visual
system, naturally, utilizes both feed-forward and feedback mechanisms in the operation
of perceiving the surrounding environment. Both feed-forward and feedback
mechanisms are integrated in a way that makes the visual system of the human
outperforms any state-of-the-art system. In this research, a proposed model of object
recognition based on the integration concept of the feed-forward and feedback
mechanisms in the human visual system is presented
Automatic age estimation system for face images
Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
This paper describes a novel system for automatic classification of images
obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial
type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The
IIF protocol on HEp-2 cells has been the hallmark method to identify the
presence of ANAs, due to its high sensitivity and the large range of antigens
that can be detected. However, it suffers from numerous shortcomings, such as
being subjective as well as time and labour intensive. Computer Aided
Diagnostic (CAD) systems have been developed to address these problems, which
automatically classify a HEp-2 cell image into one of its known patterns (eg.
speckled, homogeneous). Most of the existing CAD systems use handpicked
features to represent a HEp-2 cell image, which may only work in limited
scenarios. We propose a novel automatic cell image classification method termed
Cell Pyramid Matching (CPM), which is comprised of regional histograms of
visual words coupled with the Multiple Kernel Learning framework. We present a
study of several variations of generating histograms and show the efficacy of
the system on two publicly available datasets: the ICPR HEp-2 cell
classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126
Analysis of a Sorter Cascade Applied to Control a Wheelchair
The precise eye state detection is a fundamental stage for various activities that require human-machine interaction (HMI). This chapter presents an analysis of the implementation of a system for navigating a wheelchair with automation (CRA), based on facial expressions, especially eyes closed using a Haar cascade classifier (HCC). Aimed at people with locomotor disability of the upper and lower limbs, the state detection was based on two steps: the capture of the image, which concentrates on the detection actions and image optimization; actions of the chair, which interprets the data capture and sends the action to the chair. The results showed that the model has excellent accuracy in identification with robust performance in recognizing eyes closed, bypassing well occlusion issues and lighting with about 98% accuracy. The application of the model in the simulations opens the implementation and marriage opportunity with the chair sensor universe aiming a safe and efficient navigation to the user
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