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Abstract Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

Abstract A neuromorphic module of an electronic nose (E‐nose) is demonstrated by hybridizing a chemoresistive gas sensor made of a semiconductor metal oxide (SMO) and a single transistor neuron (1T‐neuron) made of a metal‐oxide‐semiconductor field‐effect transistor (MOSFET). By mimicking a biological olfactory neuron, it simultaneously detects a gas and encoded spike signals for in‐sensor neuromorphic functioning. It identifies an odor source by analyzing the complicated mixed signals using a spiking neural network (SNN). The proposed E‐nose does not require conversion circuits, which are essential for processing the sensory signals between the sensor array and processors in the conventional bulky E‐nose. In addition, they do not have to include a central processing unit (CPU) and memory, which are required for von Neumann computing. The spike transmission of the biological olfactory system, which is known to be the main factor for reducing power consumption, is realized with the SNN for power savings compared to the conventional E‐nose with a deep neural network (DNN). Therefore, the proposed neuromorphic E‐nose is promising for application to Internet of Things (IoT), which demands a highly scalable and energy‐efficient system. As a practical example, it is employed as an electronic sommelier by classifying different types of wines.

Recently developed object detectors rely on automatically generated object proposals, instead of using a dense sliding window search scheme; generating good object proposals has therefore become crucial for improving the computational cost and accuracy of object detection performance. In particular, the shape and location errors of object proposals can be directly propagated to object detection unless some additional processes are adopted to refine the shape and location of bounding boxes. In this study, the authors demonstrate an object proposal refinement algorithm that improves the localisation accuracy and refines the shape of object proposals by searching a boundary‐aligned minimum bounding box. They assume that an object consists of several image regions, and that the optimal object proposal is well aligned with image region boundaries. Based on this assumption, they design novel boundary‐region alignment measures and then propose a greedy refinement method based on the proposed measures. Experiments on the PASCAL VOC 2007 dataset show that the proposed method produces highly well‐localised object proposals and truly improves the quality of object proposals.

Background/Aims: In exploring human factors, stereoscopic 3D images have been used to investigate the neural responses associated with excessive depth, texture complexity, and other factors. However, the cortical oscillation associated with the complexity of stereoscopic images has been studied rarely. Here, we demonstrated that the oscillatory responses to three differently shaped 3D images (circle, star, and bat) increase as the complexity of the image increases. Methods: We recorded simultaneous EEG/MEG for three different stimuli. Spatio-temporal and spatio-spectro-temporal features were investigated by non-parametric permutation test. Results: The results showed that N300 and alpha inhibition increased in the ventral area as the shape complexity of the stereoscopic image increased. Conclusion: It seems that the relative disparity in complex stereoscopic images may increase cognitive processing (N300) and cortical load (alpha inhibition) in the ventral area.

A low-cost inertial measurement unit (IMU) and a rolling shutter camera form a conventional device configuration for localization of a mobile platform due to their complementary properties and low costs. This paper proposes a new calibration method that jointly estimates calibration and noise parameters of the low-cost IMU and the rolling shutter camera for effective sensor fusion in which accurate sensor calibration is very critical. Based on the graybox system identification, the proposed method estimates unknown noise density so that we can minimize calibration error and its covariance by using the unscented Kalman filter. Then, we refine the estimated calibration parameters with the estimated noise density in batch manner. Experimental results on synthetic and real data demonstrate the accuracy and stability of the proposed method and show that the proposed method provides consistent results even with unknown noise density of the IMU. Furthermore, a real experiment using a commercial smartphone validates the performance of the proposed calibration method in off-the-shelf devices.

Applications for tracking multiple objects in an image sequence are frequently challenged by various uncertainties, such as occlusion, misdetection, and abrupt camera motion. In practical environments, these uncertainties may occur simultaneously and with no pattern so that they must be jointly considered to achieve reliable tracking. We propose a two-step online multi-object tracking framework that incorporates a confidence-aided relative motion network (RMN) to jointly consider various difficulties. Because of the framework's two-step data association process and the similarity function using RMNs, the proposed method achieves robust performance in the presence of most kinds of uncertainties. In our experiments, the proposed method exhibits a very robust and efficient performance compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Due to the recent explosion in various forms of 3D content, the evaluation of such content from a neuroscience perspective is quite interesting. However, existing investigations of cortical oscillatory responses in stereoscopic depth perception are quite rare. Therefore, we investigated spatiotemporal and spatio-temporo-spectral features at four different stereoscopic depths within the comfort zone.We adopted a simultaneous EEG/MEG acquisition technique to collect the oscillatory responses of eight participants. We defined subject-specific retinal disparities and designed a single trial-based stereoscopic viewing experimental paradigm. In the group analysis, we observed that, as the depth increased from Level 1 to Level 3, there was a time-locked increase in the N200 component in MEG and the P300 component in EEG in the occipital and parietal areas, respectively. In addition, initial alpha and beta event-related desynchronizations (ERD) were observed at approximately 500 to 1000 msec, while theta, alpha, and beta event-related synchronizations (ERS) appeared at approximately 1000 to 2000 ms. Interestingly, there was a saturation point in the increase in cognitive responses, including N200, P300, and alpha ERD, even when the depth increased only within the comfort zone. Meanwhile, the magnitude of low beta ERD decreased in the dorsal pathway as depth increased. From these findings, we concluded that cognitive responses are likely to become saturated in the visual comfort zone, while perceptual load may increase with depth. [ABSTRACT FROM AUTHOR]

We propose a real-time line matching method for stereo systems. To achieve real-time performance while retaining a high level of matching precision, we first propose a nonparametric transform to represent the spatial relations between neighboring lines and nearby textures as a binary stream. Since the length of a line can vary across images, the matching costs between lines are computed within an overlap area (OA) based on the binary stream. The OA is determined for each line pair by employing the properties of a rectified image pair. Finally, the line correspondence is determined using a winner-takes-all method with a left-right consistency check. To reduce the computational time requirements further, we filter out unreliable matching candidates in advance based on their rectification properties. The performance of the proposed method was compared with state-of-the-art methods in terms of the computational time, matching precision, and recall. The proposed method required 47 ms to match lines from an image pair in the KITTI dataset with an average precision of 95%. We also verified the proposed method under image blur, illumination variation, and viewpoint changes. [ABSTRACT FROM AUTHOR]

In this letter, we propose a robust dynamic point clustering method for detecting moving objects in stereo image sequences, which is essential for collision detection in driver assistance system. If multiple objects with similar motions are located in close proximity, dynamic points from different moving objects may be clustered together when using the position and velocity as clustering criteria. To solve this problem, we apply a geometric constraint between dynamic points using line segments. Based on this constraint, we propose a variable K-nearest neighbor clustering method and three cost functions that are defined between line segments and points. The proposed method is verified experimentally in terms of its accuracy, and comparisons are also made with conventional methods that only utilize the positions and velocities of dynamic points. [ABSTRACT FROM AUTHOR]

Depth map up-sampling methods have achieved remarkable improvement by exploiting sensor fusion techniques where they assume that the depth map discontinuities and image edges coincide, and the depth values of the temporal neighbors are stable during time variation. However, inherent noise of depth data acquired by active range sensors often violates these assumptions, and results in undesirable error propagation. To alleviate the error propagation, this letter presents a new adaptive supporting method of spatially-temporally neighboring samples. On the basis of a spatial-temporal Markov random field model, the weight coefficients of the smoothness terms are adaptively computed according to the reliability of neighboring samples. The experiments show that the proposed method outperforms the previous works in terms of quantitative and qualitative criteria. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for obtaining a highquality 3D depth. The advantages of active pattern projection and passive stereo matching are combined and a system is established. A diffractive optical element (DOE) is developed to project the active pattern. Cross guidance (CG) and auto guidance (AG) are proposed to perform the passive stereo matching in a stereo image in which a DOE pattern is projected. When obtaining the image, the CG emits a DOE pattern periodically and consecutively receives the original and pattern images. In addition, stereo matching is performed using these images. The AG projects the DOE pattern continuously. It conducts cost aggregation, and the image is restored through the process of removing the pattern from the pattern image. The ground truth is generated to estimate the optimal parameter among various stereo matching algorithms. Using the ground truth, the optimal parameter is estimated and the cost computation and aggregation algorithm are selected. The depth is calculated and bad-pixel errors make up 4.45% of the non-occlusion area. [ABSTRACT FROM AUTHOR]

We present a new window-based method for correspondence search using varying support-weights. We adjust the support-weights of the pixels in a given support window based on color similarity and geometric proximity to reduce the image ambiguity. Our method outperforms other local methods on standard stereo benchmarks. [ABSTRACT FROM AUTHOR]

An accurate stereo matching method developed by exploiting the two techniques, discrete-coded structured-light projection and image-guided cost volume filtering, is proposed. The former increases the distinctiveness of pixels by projecting a discrete pattern to the scene, and the latter helps to recover accurate object boundaries. In addition, a previous fast cost volume filtering approach is extended to better preserve slanted surfaces, and a suitable post-processing algorithmis also suggested for the proposed method. The performance of the proposed method is experimentally verified by comparing the results with those of other algorithms qualitatively and quantitatively in an indoor environment. [ABSTRACT FROM AUTHOR]

We present a robust approach for computing disparity maps with a supervised learning-based confidence prediction. This approach takes into consideration following features. First, we analyze the characteristics of various confidence measures in the random forest framework to select effective confidence measures depending on the characteristics of the training data and matching strategies, such as similarity measures and parameters. We then train a random forest using the selected confidence measures to improve the efficiency of confidence prediction and to build a better prediction model. Second, we present a confidence-based matching cost modulation scheme, based on predicted confidence values, to improve the robustness and accuracy of the (semi-) global stereo matching algorithms. Finally, we apply the proposed modulation scheme to popularly used algorithms to make them robust against unexpected difficulties that could occur in an uncontrolled environment using challenging outdoor datasets. The proposed confidence measure selection and cost modulation schemes are experimentally verified from various perspectives using the KITTI and Middlebury datasets.