Markerless Image Alignment Method for Pressure-Sensitive Paint Image

We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.

Accurate and High-speed FBG Demodulator Using STM32 and DOG Algorithm

A multi-channel and high-speed FBG demodulator based STM32 is designed in this paper. The wavelength detection accuracy is improved using a difference of Gaussian (DoG) peak detection algorithm. The 16-channel FBG wavelengths are demodulated simultaneously and that the demodulation frequency can reach 1kHz. The experimental results show that the temperature sensitivity is 13.17 pm/ ºC. And meanwhile, the standard deviation and the average error of the FBG wavelength demodulated at the same temperature is 1.9 pm and 2.1 pm respectively.

Stimulus Dependence of Interocular Suppression

Suppression is assessed using a variety of methods with different stimuli that vary in color, contrast, size, and luminance. We hypothesized that stimulus variation may yield different spatial extents of suppression. Here, to evaluate the role of stimulus characteristics, we measured the suppression zone using a binocular rivalry paradigm in normal observers by systematically varying the parameters of dichoptic Difference of Gaussian stimuli. The stimuli consist of a constantly visible horizontal reference seen by one eye while two vertical suppressors were presented to the other eye. With a keypress, the suppressors appeared for 1 second, to induce a robust transient suppression zone in the middle part of the reference. Subjects adjusted the width between the suppressors to determine the zone. The zone decreased significantly with increasing spatial frequency and lower contrast. The horizontal zone was larger than the vertical zone by a factor of 1.4. The zone was smaller with negative contrast stimuli compared to positive contrast polarity but independent of eye dominance, luminance and colored filters. We then fit a model to determine the optimal parametric definition of the suppression zone and found that the zone consists of two parts: a stimulus-dependent and a fixed non-stimulus dependent zone.

Aversive Conditioning of Spatial Position Sharpens Neural Population-level Tuning in Visual Cortex and Selectively Reduces Alpha-band Activity

Processing capabilities for many low-level visual features are experientially malleable, aiding sighted organisms in adapting to dynamic environments. Explicit instructions to attend a specific visual field location influence retinotopic visuocortical activity, amplifying responses to stimuli appearing at cued spatial positions. It remains undetermined, however, both how such prioritization affects surrounding non-prioritized locations, and if a given retinotopic spatial position can attain enhanced cortical representation through experience rather than instruction. This work examined visuocortical response changes as human observers learned, through differential classical conditioning, to associate specific on-screen locations with aversive outcomes. Using dense-array EEG and pupillometry, we tested the pre-registered hypotheses of either sharpening or generalization around an aversively associated location following a single conditioning session. Specifically, competing hypotheses tested if mean response changes would take the form of a gaussian (generalization) or difference-of-gaussian (sharpening) distribution over spatial positions, peaking at the viewing location paired with a noxious noise. Occipital 15 Hz steady-state visual evoked potential (ssVEP) responses were selectively heightened when viewing aversively paired locations and displayed a non-linear, difference-of-gaussian profile across neighboring locations, consistent with suppressive surround modulation of non-prioritized positions. Measures of alpha band (8 – 12.8 Hz) activity and pupil diameter also exhibited selectively heightened responses to noise-paired locations but did not evince any difference across the non-paired locations. These results indicate that visuocortical spatial representations are sharpened in response to location-specific aversive conditioning, while top-down influences indexed by alpha power reduction exhibit all-or-none modulation.Significance StatementIt is increasingly recognized that early visual cortex is not a static processor of physical features, but is instead constantly shaped by perceptual experience. It remains unclear, however, to what extent the cortical representation of many fundamental features, including visual field location, is malleable by experience. Using EEG and an aversive classical conditioning paradigm, we observed sharpening of visuocortical responses to stimuli appearing at aversively associated locations along with location-selective facilitation of response systems indexed by pupil diameter and EEG alpha power. These findings highlight the experience-dependent flexibility of retinotopic spatial representations in visual cortex, opening avenues towards novel treatment targets in disorders of attention and spatial cognition.

Small Infrared Target Detection via a Mexican-Hat Distribution

Although infrared small target detection has been broadly used in airborne early warning, infrared guidance, surveillance and tracking, it is still an open issue due to the low signal-to-noise ratio, less texture information, background clutters, and so on. Aiming to detect a small target in an infrared image with complex background clutters, this paper carefully studies the characteristics of a target in an IR image filtered by the difference of Gaussian filter, concluding that the intensity of the adjacent region around a small infrared target roughly has a Mexican-hat distribution. Based on such a conclusion, a raw infrared image is sequentially processed with the modified top-hat transformation and the difference of Gaussian filter. Then, the adjacent region around each pixel in the processed image is radially divided into three sub-regions. Next, the pixels that distribute as the Mexican-hat are determined as the candidates of targets. Finally, a real small target is segmented out by locating the pixel with the maximum intensity. Our experimental results on both real-world and synthetic infrared images show that the proposed method is so effective in enhancing small targets that target detection gets very easy. Our method achieves true detection rates of 0.9900 and 0.9688 for sequence 1 and sequence 2, respectively, and the false detection rates of 0.0100 and 0 for those two sequences, which are superior over both conventional detectors and state-of-the-art detectors. Moreover, our method runs at 1.8527 and 0.8690 s per frame for sequence 1 and sequence 2, respectively, which is faster than RLCM, LIG, Max–Median, Max–Mean.

Breast Cancer Classification using SVM Classifier

Early detection of breast cancer is believed to enhance the chance of survival. Mammography is the best available breast imaging technique at present which uses low-dose x-rays for detecting the breast cancer early before the symptoms are experienced. The most commonly present abnormalities in mammograms that may indicate the breast malignancy are masses and microcalcifications. The prime objective of this research is to increase the diagnostic accuracy of the detection of breast cancer malignancy in Computer Aided Diagnosis (CAD) systems by developing image processing algorithms and to categorize the women into different risk groups. The evaluation of SVM classifier has been considered. Initially, tumors have been detected from mammograms with the aid of morphological processing of breast images. Then classification is done by SVM classifier using the most dominant features namely GLRLM and Difference of Gaussian (DoG) features, which have been extracted from the selected region. The algorithm has achieved an accuracy of 89.11% using SVM classifier.