False Points Research Articles

Non-Invasive Eye Tracking Technology Based on Corneal Reflex

In order to ameliorate deficiencies existing in eye tracking devices, a new method of eye tracking technology using the five near-infrared light as the source of corneal reflex is put forward and it overcomes the shortcoming of existing devices, such as complex, having too many restriction on the position and needing to wear special equipment, and so on. This method does not need any wearing equipment and can adapt to the natural head movement, the algorithm designed calculates the position of sight in high accuracy. A new method of pupil edge fitting is put forward to fit circularly and remove false points to get accurate position of pupil center in order to improve mapping accuracy. The grayscale of image is used to initialize eye position quickly and accurately. The theory of cross-ratio invariance is used to make coordinate mapping and calculate the attention coordinate accurately.

A robust multisource image automatic registration system based on the SIFT descriptor

Image registration is an essential step in many remote-sensing (RS) applications. This article presents a study of a multisource image automatic registration system (MIARS) based on the scale-invariant feature transform (SIFT), which has been demonstrated to be the most robust local invariant feature descriptor for automatically registering various RS images. The SIFT descriptor has two shortcomings: it is unsuitable for extremely large images and has an irregular distribution of feature points. Therefore, three steps are proposed for the MIARS: image division, histogram equalization and the elimination of false point matches by a subregion least squares iteration. Image division makes it possible to use the SIFT descriptor to extract control points from an extremely large RS image. Histogram equalization in prematching improves the contrast sensitivity of RS images. The subregion least squares iteration refines the registration accuracy. Images from multisensor systems, including Quickbird, IRS-P6, Landsat/TM, HJ-CCD, HJ-IRS, light detection and ranging (LiDAR) intensity images and aerial data, were selected to test the reliability of the MIARS. The results indicated that better registration accuracy was achieved, which will be very helpful in the future development of a registration model.

Closed Salient Edge Extraction Based on Visual Attention Mechanism

Edge detection is a means of generating compact descriptions which preserve most of the structural information in an image. However, the edge image returned by the edge operators usually contains false edge points or exhibits gaps in edges which are generated by image noise. To solve the problem, a closed salient edge extraction approach was proposed, which makes use of the visual attention model given out by Itti together with the original edge image to obtain the salient edges and then the salient-edge gaps were linked so that the closed salient edge of an image can be extracted. In order to link the edges, a membership function based on the gradient and directions of endpoints of the salient edge image was carried out to control the extension of endpoints. Experimental results showed that the proposed algorithm was able to extract the closed salient edge of an image effectively especially for the image whose background was not very complex.

2-Dimensional Accurate Imaging with UWB Radar Using Indoor Multipath Echoes for a Target in Shadow Regions

UWB (Ultra Wide-Band) pulse radar is promising for surveillance systems because it has an outstanding high range-resolution. To realize an accurate UWB radar imaging system, we propose a new approach that employs multipath echoes from a target in an indoor environment. Using multipath echoes, the proposed system can accurately estimate images, even for targets in a shadow region where the targets are out of sight of the antenna. We apply a simple interferometry technique using the multiple mirror image antennas generated by multipath propagation. We find that this simple method also produces many undesired false image points. To tackle this issue, we also propose an effective false image reduction algorithm to obtain a clear image. Numerical simulations verify that most of the false image points are removed and the target shape is accurately estimated.

Mating Behaviors of Cydia pomonella (Lepidoptera: Tortricidae) as Influenced by Sex Pheromone in Electrostatic Powder

Entostat is an electrostatically charged wax powder that can adhere strongly to insect cuticle, making it an ideal carrier to deliver pheromone for pheromone-based confusion techniques. We investigated the attractiveness of Cydia pomonella (L.) (Lepidoptera: Tortricidae) males treated with pheromone-laden Entostat powder to naive conspecifics as well as mating behaviors of males after such treatment in a laboratory flight tunnel. Male moths exposed to Entostat containing 1% E,E-8,10-dodecadien-1-ol (codlemone) acquired and retained the largest amount of the powder and became more attractive as point sources to naive moths compared with those treated with powder containing 5 or 10% codlemone. All Entostat-exposed males remained as attractive as a 0.1-mg codlemone lure for up to 24 h in flight tunnel investigations. Male moth orientation to normally attractive sources of codlemone was completely disrupted directly after treatment with Entostat powder. Males' ability to orient to 0.1-mg lures recovered progressively over a 6-d postexposure interval; however, their responses never reached the levels observed with untreated control moths. Entostat-exposed moths retained detectable amounts of codlemone up to 4 d. Our laboratory flight tunnel results suggest that the mode of action of Entostat powder as an autodissemination control tactic may be due to creating both attractive false point sources after exposure to the powder as well as directly inhibiting contaminated males' capability to orient to pheromone.

Mitigating Investor Risk-Seeking Behavior in a Down Real Estate Market

Using an extension of the prospect theory known as false reference points, this study examines the behavior of real estate investors after experiencing a loss. The results confirm our central hypothesis that when investors attempt to avoid the pain of regret by changing the lens through which they view losses, they become more likely to hold onto bad investments. This unwillingness to sell bad investments in the short run causes investors to be more likely to experience heightened levels of unavoidable regret in the long run. The results hold across demographic characteristics but are slightly more pronounced for men and international investors, specifically those from Asia.

On the nonexistence of exceptional branch points for weak relative minima in Plateau’s problem

We define the notion of an exceptional branch point for a minimal surface in \({{\mathbb R}^3}\) and show that it is closely related to the notion of false branch point. We prove that even in the presence of an exceptional branch point we may, under certain conditions, reduce Dirichlet’s energy and therefore also reduce area.

Short-Term Effect of Acupuncture on Intraocular Pressure in Healthy Subjects

To evaluate the short-term effect of acupuncture on the intraocular pressure (IOP) in healthy subjects. A randomised controlled double-blinded trial was performed. 48 healthy volunteers (94 eyes) were randomly allocated to three groups: acupuncture group-19 subjects (38 eyes) were submitted to a 20 min session of acupuncture (GB1, GB14 and BL1); sham group-14 subjects (27 eyes) were submitted to a 20 min session of acupuncture with needles inserted in false points; and control group-15 subjects (29 eyes) who underwent no intervention. All subjects had the IOP measured by a masked investigator using Goldmann applanation tonometry immediately before intervention, as well as 20 min and 24 h after. The mean (SD) IOP in the acupuncture group was 17.9 (3.3) mm Hg at baseline, 16.4 (3.9) mm Hg at 20 min and 16.3 (3.3) mm Hg at 24 h. The mean (SD) IOP in the sham group was 18.6 (3.3) mm Hg at baseline, 17.7 (2.6) mm Hg at 20 min and 15.9 (3.6) mm Hg at 24 h. The mean (SD) IOP in the control group was 16.9 (3.5) mm Hg at baseline, 16.5 (3.8) mm Hg at 20 min and 15.8 (3.3) mm Hg at 24 h. There was no statistically significant difference in the IOP variation (post-intervention minus baseline measurements) between groups after 20 min (p=0.13) and 24 h (p=0.21). Acupuncture in the studied points did not produce significant short-term effect on the IOP of healthy individuals in comparison with control groups.

Medical equipments high precise detection technology basing on morphology-harris operator

Medical equipments related to life safety of human, it is important to detect by a high precise method. Image mosaic which based on Harris corner operator is a commonly used method in this area; Harris operator has low calculation burden, it is simple and stable, so it is more effective comparing with other feature point extracted operators. But in this algorithm, corner points can only be detected in a single-scale, there may be losing information of corner points, causing corner point location offset, extracting false corner points because of noise. In order to solve this question, the acquired images should be processed by dilation and erosion operation firstly, then do image mosaic. Results show that image noise can be eliminated effectively after those morphological processes, as well as the false positive noise generated by image glitch. The success rate of image mosaic and detection accuracy can be greatly improved through the Morphology-Harris operator. Measurement of precision instruments which based on this new method will improve the measurement accuracy, and the research in this area will promote the further development of machine vision technology.

Detecting feature from spatial point processes using Collective Nearest Neighbor

In a spatial point set, clustering patterns (features) are difficult to locate due to the presence of noise. Previous methods, either using grid-based method or distance-based method to separate feature from noise, suffer from the parameter choice problem, which may produce different point patterns in terms of shape and area. This paper presents the Collective Nearest Neighbor method (CLNN) to identify features. CLNN assumes that in spatial data clustered points and noise can be viewed as two homogenous point processes. The one with the higher intensity is considered as a feature and the one with the lower intensity is treated as noise. As a result, they can be separated according to the difference in intensity between them. With CLNN, points are first classified into feature and noise based on the kth nearest distance (the distance between a point and its kth nearest neighbor) at various values of k. Then, CLNN selects those classifications in which the separated classes (i.e. features and noise) are homogenous Poisson processes and cannot be further divided. Finally, CLNN identifies clustered points by averaging the selected classifications. Evaluation of CLNN using simulated data shows that CLNN reduces the number of false points significantly. The comparison between CLNN, the shared nearest neighbor, the spatial scan and the classification entropy method shows that CLNN produced the fewest false points. A case study using seismic data in southwestern China showed that CLNN is able to identify foreshocks of the Songpan earthquake ( M = 7.2), which may help to locate the epicenter of the Songpan earthquake.

Image matching problem for certain cases of perspective photography

The modern state of matching problem for digital images is addressed. Image matching presumes the process of overlapping of two or more images shot at different time instants from different perspective views or, maybe, from different sensors. In addition the most precise geometric correspondence should be reached. The main stages of the matching problem are considered in detail. The advantages and disadvantages of the solutions proposed in this field by different researchers are analyzed. The solutions developed earlier that are the most successful from the authors’ opinion are employed, and original solutions of certain problems are also proposed. For reliable control and rejection of false points, the authors have developed original algorithms of metric filtering and iterative stochastic filtering SwarmSAC. The objective of this paper is to develop and present a methodology of identification and matching of fragments of digital images received for a wide range of shooting conditions.

The dilemma of acupuncture and modern research why sham acupuncture does not exist: Historical and texthermeneutic perspectives

This paper focuses on key elements of acupuncture and surveys basic assumptions underlying the acupuncture doctrine which are accepted by clinical acupuncture research. To assess the treatment efficacy of an acupuncture intervention, acupuncture at real points (verum) is frequently compared to acupuncture at false points (sham). The basic assumption here is that verum and sham acupuncture are indeed valid concepts. A prerequisite of the validity of these concepts though is that it must be possible to localize verum acupuncture points in an exact and reproducible manner. But, from the vague descriptions of the pathways of the “vessels” and “acupuncture loci” in the early sources of Chinese Medicine, how did we arrive at the exact anatomical depictions of the “meridians” and “acupuncture points” in modern textbooks? By tracing the idea of the “meridian” and its course and the acupuncture point and its localization through the history of Chinese Medicine from the earliest primary sources to modern textbooks, it becomes evident that there is no exact definition of the “classic” or real acupuncture point and that reproducible localization of acupuncture points is not possible. Thus, from a historical and texthermeneutic perspective the concept of both the real and the sham acupuncture point remain elusive. The ideas and practices of Chinese Medicine are part of a historical and cultural process with a remarkable continuity in tradition. If, however, consciously or unconsciouly removed from this context and subjected to the imperatives of modern research methodology, both modern research and the traditions face a dilemma.

Reconciling perspectives of software logic testing

AbstractMany software logic test coverage criteria have emerged over the past several years; however, they are scattered throughout the literature. The goal of this paper is to describe the various logic tests and explain their rationale in a centralized location in order to aid software testers in their decisions about which to implement. Each logic test is examined in terms of its minimum and maximum test sizes, subsumption relationship to other logic tests, and its fault detection capability. It is shown that although semantic tests generally have a smaller test size, syntactic tests are better in terms of fault detection capability. Furthermore, it is shown that the syntactic tests subsume their semantic counterparts. Two new software faults are introduced to Kuhn's fault hierarchy, namely the term insertion fault and the term negation fault, and the fault detection capability of the logic tests with respect to these faults is examined. A new software testing criterion called multiple unique true point/near false point (MUTP/NFP) is also presented, with a description of its benefits and limitations. Specifically, a detailed comparison of the MUTP/NFP criterion and the CUTPNFP/MNFP/MUTP criterion is given. Copyright © 2008 John Wiley & Sons, Ltd.

Electroacupuncture analgesia in dogs: is there a difference between uni- and bi-lateral stimulation?

ObjectiveTo compare the analgesic effect of uni- and bi-lateral electroacupuncture (EA) in response to thermal and mechanical nociceptive stimuli and to investigate the cardiorespiratory, endocrine, and behavioral changes in dogs submitted to EA. Study designProspective, randomized cross-over experimental study. AnimalsEight adult, clinically healthy, cross-breed dogs, weighing 13 ± 4 kg. MethodsDogs underwent electrostimulation at false acupoints (T-false); bilateral EA at acupoints, stomach 36, gall bladder 34 and spleen 6 (T-EA/bil); unilateral EA at the same points (T-EA/uni) or were untreated (T-control). All animals received acepromazine (0.05 mg kg−1) IV; and heart rate, pulse oximetry, indirect arterial blood pressure, respiratory rate, Pe′CO2, rectal temperature, and plasma cortisol concentration were measured before, during, and after EA. Analgesia was tested using thoracic and abdominal cutaneous thermal and mechanical stimuli, and an interdigital thermal stimulus. Behavior was classified as calm or restless. Analysis of variance for repeated measures followed by Tukey's test was used for analysis of the data. ResultsThere were no cardiorespiratory differences among the treatments. The cutaneous pain threshold was higher after EA, compared with false points. The latency period was shorter and analgesia was more intense in T-EA/bil than T-EA/uni, when both were compared with T-false and T-control. Six out of eight animals treated with EA were calm during treatment, and 5/8 and 4/8 of the T-false and T-control animals, respectively, were restless. Latency to interdigital thermal stimulation increased in T-EA/bil compared with the others. There was no difference in plasma cortisol concentrations among the treatments. ConclusionsBilateral EA produced a shorter latency period, a greater intensity, and longer duration of analgesia than unilateral stimulation, without stimulating a stress response. Clinical relevanceBilateral EA produces a better analgesic effect than unilateral EA.

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algorithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assignment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate association cost; hence, much of the procedure time is saved. In the 2-stage association algorithm, a large number of false location points are eliminated from candidate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.

Fingerprint minutiae filtering using ARTMAP

Fingerprints are widely used for unique personal identification based on minutiae matching. Minutiae are the terminations and bifurcations of ridges in a fingerprint image. Generally fingerprint images are of low quality due to the presence of noise and contrast deficiency resulting in discontinuity in ridges producing false minutiae points. It is worth noting that there is a fundamental difference between a neural network (NN) approach for minutiae location and minutiae filtering. In this paper, the spurious minutiae points and the bug pixels introduced during the thinning process are eliminated based on the neighborhood pixel information. A new minutiae filtering algorithm using a NN is introduced to improve the accuracy of the extraction algorithm proposed in the literature. Each minutia, as detected by the algorithm, is classified through ARTMAP NN whose output indicates whether it is a termination, a bifurcation or a false minutia. Experimental results show that the efficiency of minutiae classification has significantly improved using the proposed filtering algorithm.

Registration of partially overlapping surfaces by rejection of false point correspondences

We present a new algorithm for the registration of three-dimensional partially overlapping surfaces. It is based on an efficient scheme for the rejection of false point correspondences (correspondence outliers) and does not require initial pose estimation or feature extraction. An initial list of corresponding points is first derived using the regional properties of vertices on both surfaces. From these point correspondences, pairs of corresponding rigid triplets are formed. The normal vectors at the vertices of each corresponding triplet are used to compute the candidate rotations. By clustering the candidate rotation axes and candidate rotation angles separately, a large number of false correspondences are eliminated and an approximate rotation is decided, from which an approximate translation is also obtained. Finally, the optimal transformation parameters are determined by further refining the estimated parameters in an iterative manner. Mathematical analysis and experimental results show that the registration process is fast and accurate even when the objects are regularly shaped and contain many regionally similar surface patches.

Simplex maximization of the correlation coefficient for DNA sizing analysis by capillary electrophoresis

The separation of DNA molecules in polymeric solution by capillary electrophoresis involves the optimization of several variables, such as polymer solution concentration, electric field separation, temperature, etc. The optimization of each variable individually usually is a time-consuming process and the results may reach a false optimum point. Chemometric methods are suitable to be applied in such cases in which a number of variables can be optimized simultaneously. The simplex is a chemometric method that can perform such a task easily and efficiently. In this study, a simplex method was carried out to maximize the correlation coefficient (r(2)) of a logarithmic plot of mobility (mu) vs. base pair (bp), which was obtained from the separation of DNA fragments of size between 75 and 4072 bp. The simplex showed three vertexes with r(2) > 0.98 and the vertex 21 showing the highest resolution. For the fragments between 201 and 2036 bp, the r(2) increased to 0.992 with and relative standard deviation (RSD) lower than 0.2% (inter- and intra-day variation). The precision of the method in determining the size of a PCR DNA fragment was carried out using a 1 kbp DNA ladder. With the addition of an internal standard to the sample, the precision could be further improved.

Convexity and logical analysis of data

A Boolean function is called k-convex if for any pair x,y of its true points at Hamming distance at most k, every point “between” x and y is also true. Given a set of true points and a set of false points, the central question of Logical Analysis of Data is the study of those Boolean functions whose values agree with those of the given points. In this paper we examine data sets which admit k-convex Boolean extensions. We provide polynomial algorithms for finding a k-convex extension, if any, and for finding the maximum k for which a k-convex extension exists. We study the problem of uniqueness, and provide a polynomial algorithm for checking whether all k-convex extensions agree in a point outside the given data set. We estimate the number of k-convex Boolean functions, and show that for small k this number is doubly exponential. On the other hand, we also show that for large k the class of k-convex Boolean functions is PAC-learnable.