Learning boosted asymmetric classifiers for object detection

http://ieeexplore.ieee.orgObject detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods

An Integrated Simulation Design With Three-Dimensional Motions and a Hydraulic Stewart Simulator

This paper presents an integrated design process and tests of a Stewart simulator with a virtual visualization tool, which uses Virtools to create and generate three-dimensional motions. An inverse kinematic algorithm is written to convert each visualized motion to the displacements of six cylinders in a Stewart motion simulator. Information of the displacements is then transferred through the User Datagram Protocol (UDP) to a personal computer which has the LabVIEW software. An NI USB-6251 data acquisition device is applied to interact with the LabVIEW program and the Stewart hydraulic simulator. The approach presented in this paper to function an old Stewart hydraulic simulator can also be applied to other simulators

The Magnitude of Switching Costs for Corporate Antivirus Software Switching Decision

Today’s businesses environment is forcing companies to become increasingly more efficient in applying Internet technology to conduct transactions. AS the possibility of infection by computer virus is much greater now than ever before, businesses search for appropriate corporate antivirus software to safeguard their computer systems. This paper considers corporate antivirus software switching as one of the major security selection problem and proposes possible avenues for software switching decision and management. In conceptual model, we draw upon switching costs where transaction costs, learning costs, and artificial costs were examined as main costs for software switching decision. Our findings shown only two out of three types of switching costs have influence over corporate antivirus software switching decisions. Despite the existence of switching costs, businesses continue to repeat software switching because the perceived risks of security threats are much greater than the switching cost itself. Furthermore, we examine various approaches to the cost of switching and then propose an index map to evaluate switching decision. Five sets of propositions are advanced to help guide this research

System Design And Integration For Repeated Impact Tests

The design and integration of an impact-testing machine is particularly for the test of an object which is repeatedly dropped down from a specified height. Four linear actuators with two on each of the two magnetic rails are used to lift up an object weighing up to 70 lbs. Each actuator is powered and controlled by an industrial amplifier. A Programmable Logical Controller (PLC) is applied to activate these four actuators simultaneously and repeatedly. Accelerometers using an National Instruments (NI) data acquisition system are used to measure the impact force during the tests. Students gain design and implementation experiences from the developing of the system

Traffic sign detection using a cascade method with fast feature extraction and saliency test

Automatic traffic sign detection is challenging due to the complexity of scene images, and fast detection is required in real applications such as driver assistance systems. In this paper, we propose a fast traffic sign detection method based on a cascade method with saliency test and neighboring scale awareness. In the cascade method, feature maps of several channels are extracted efficiently using approximation techniques. Sliding windows are pruned hierarchically using coarse-to-fine classifiers and the correlation between neighboring scales. The cascade system has only one free parameter, while the multiple thresholds are selected by a data-driven approach. To further increase speed, we also use a novel saliency test based on mid-level features to pre-prune background windows. Experiments on two public traffic sign data sets show that the proposed method achieves competing performance and runs 27 times as fast as most of the state-of-the-art methods

Causal Evidence for the Role of Specific GABAergic Interneuron Types in Entorhinal Recruitment of Dentate Granule Cells

The dentate gyrus (DG) is the primary gate of the hippocampus and controls information flow from the cortex to the hippocampus proper. To maintain normal function, granule cells (GCs), the principal neurons in the DG, receive fine- tuned inhibition from local-circuit GABAergic inhibitory interneurons (INs). Abnormalities of GABAergic circuits in the DG are associated with several brain disorders, including epilepsy, autism, schizophrenia, and Alzheimer disease. Therefore, understanding the network mechanisms of inhibitory control of GCs is of functional and pathophysiological importance. GABAergic inhibitory INs are heterogeneous, but it is unclear how individual subtypes contribute to GC activity. Using cell-type-specific optogenetic perturbation, we investigated whether and how two major IN populations defined by parvalbumin (PV) and somatostatin (SST) expression, regulate GC input transformations. We showed that PV-expressing (PV+) INs, and not SST- expressing (SST+) INs, primarily suppress GC responses to single cortical stimulation. In addition, these two IN classes differentially regulate GC responses to θ and γ frequency inputs from the cortex. Notably, PV+ INs specifically control the onset of the spike series, whereas SST+ INs preferentially regulate the later spikes in the series. Together, PV+ and SST+ GABAergic INs engage differentially in GC input-output transformations in response to various activity patterns

Investigation on the mechanism of peripheral axonal injury in glaucoma

AIM: To compare the angles of longitudinal section of sclera around optic nerve heads and the never fiber layer changes in healthy adults and patients with glaucoma, and to investigate the mechanism of peripheral retinal axonal injury, with the combined knowledge of biomechanics. METHODS: The optical nerves and their peripheral tissue specimen in the 12 eyes from health adult donators and 12 eyes from glaucoma patient donators were dyed by Glees' method to compare the angles of longitudinal section of sclera around optic nerve heads(through optic nerve center), and to observe the anatomical features of the peripheral retinal axons. RESULTS: The mean angle of longitudinal section of sclera around optic nerve in healthy adults was 73.3°, while that in patients with absolute glaucoma was 75.6°. The difference showed no significance(t=1.44, P>0.05). There was a sharp bend in the course of peripheral optical fiber in healthy adults. However, the optic nerve fiber disappeared completely in patients with glaucoma end stage. CONCLUSION: The angle between the medial edge and leading edge of sclera(around optic nerve heads)is an acute angle. The optical fiber in glaucoma end stage disappeared completely. The phenomenon may be related to high intraocular pressure, the sclera shape, the shear modulus of sclera and axons, and “axonal bending-injury” mechanism