Segmentation-assisted detection of dirt impairments in archived film sequences

A novel segmentation-assisted method for film dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection for a wide variety of test material

Real-Time Illegal Parking Detection System Based on Deep Learning

The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detection system. Illegal vehicles captured by camera are firstly located and classified by the famous Single Shot MultiBox Detector (SSD) algorithm. To improve the performance, we propose to optimize SSD by adjusting the aspect ratio of default box to accommodate with our dataset better. After that, a tracking and analysis of movement is adopted to judge the illegal vehicles in the region of interest (ROI). Experiments show that the system can achieve a 99% accuracy and real-time (25FPS) detection with strong robustness in complex environments.Comment: 5pages,6figure

WIYN Open Cluster Study 1: Deep Photometry of NGC 188

We have employed precise V and I photometry of NGC 188 at WIYN to explore the cluster luminosity function (LF) and study the cluster white dwarfs (WDs). Our photometry is offset by V = 0.052 (fainter) from Sandage (1962) and Eggen & Sandage (1969). All published photometry for the past three decades have been based on these two calibrations, which are in error by 0.05 +- 0.01. We employ the Pinsonneault etal (1998) fiducial main sequence to derive a cluster distance modulus of 11.43 +- 0.08. We report observations that are >= 50% complete to V = 24.6 and find that the cluster central-field LF peaks at M_I ~ 3 to 4. This is unlike the solar neighborhood LF and unlike the LFs of dynamically unevolved portions of open and globular clusters, which rise continuously until M_I ~ 9.5. Although we find that >= 50% of the unresolved cluster objects are multiple systems, their presence cannot account for the shape of the NGC 188 LF. For theoretical reasons (Terlevich 1987; Vesperini & Heggie 1997) having to do with the survivability of NGC 188 we believe the cluster is highly dynamically evolved and that the missing low luminosity stars are either in the cluster outskirts or have left the cluster altogether. We identify nine candidate WDs, of which we expect three to six are bona fide cluster WDs. The luminosities of the faintest likely WD indicates an age (Bergeron, Wesemael, & Beauchamp 1995) of 1.14 +- 0.09 Gyrs. This is a lower limit to the cluster age and observations probing to V = 27 or 28 will be necessary to find the faintest cluster WDs and independently determine the cluster age. While our age limit is not surprising for this ~6 Gyr old cluster, our result demonstrates the value of the WD age technique with its very low internal errors. (abridged)Comment: 26 pages, uuencoded gunzip'ed latex + 16 postscrip figures, to be published in A

The Solar Neighborhood XLII. Parallax Results from the CTIOPI 0.9-m Program --- Identifying New Nearby Subdwarfs Using Tangential Velocities and Locations on the H-R Diagram

Parallaxes, proper motions, and optical photometry are presented for 51 systems made up 37 cool subdwarf and 14 additional high proper motion systems. Thirty-seven systems have parallaxes reported for the first time, 15 of which have proper motions of at least 1"/yr. The sample includes 22 newly identified cool subdwarfs within 100 pc, of which three are within 25 pc, and an additional five subdwarfs from 100-160 pc. Two systems --- LSR 1610-0040 AB and LHS 440 AB --- are close binaries exhibiting clear astrometric perturbations that will ultimately provide important masses for cool subdwarfs. We use the accurate parallaxes and proper motions provided here, combined with additional data from our program and others to determine that effectively all nearby stars with tangential velocities greater than 200 km s$^{-1}$ are subdwarfs. We compare a sample of 167 confirmed cool subdwarfs to nearby main sequence dwarfs and Pleiades members on an observational Hertzsprung-Russell diagram using $M_V$ vs.~$(V-K_{s})$ to map trends of age and metallicity. We find that subdwarfs are clearly separated for spectral types K5--M5, indicating that the low metallicities of subdwarfs set them apart in the H-R diagram for $(V-K_{s})$ = 3--6. We then apply the tangential velocity cutoff and the subdwarf region of the H-R diagram to stars with parallaxes from {\it Gaia} Data Release 1 and the MEarth Project to identify a total of 29 new nearby subdwarf candidates that fall clearly below the main sequence.Comment: accepted for publication in Astronomical Journa

Efficient Asymmetric Co-Tracking using Uncertainty Sampling

Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to update the object model. However, these approaches are heavily invested in the efficiency and effectiveness of their detectors. Evaluating a massive number of samples for each frame (e.g., obtained by a sliding window) forces the detector to trade the accuracy in favor of speed. Furthermore, misclassification of borderline samples in the detector introduce accumulating errors in tracking. In this study, we propose a co-tracking based on the efficient cooperation of two detectors: a rapid adaptive exemplar-based detector and another more sophisticated but slower detector with a long-term memory. The sampling labeling and co-learning of the detectors are conducted by an uncertainty sampling unit, which improves the speed and accuracy of the system. We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response. Experiments demonstrate the efficiency and effectiveness of the proposed tracker against its baselines and its superior performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201

PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

Four new T dwarfs identified in PanSTARRS 1 commissioning data

A complete well-defined sample of ultracool dwarfs is one of the key science programs of the Pan-STARRS 1 optical survey telescope (PS1). Here we combine PS1 commissioning data with 2MASS to conduct a proper motion search (0.1--2.0\arcsec/yr) for nearby T dwarfs, using optical+near-IR colors to select objects for spectroscopic followup. The addition of sensitive far-red optical imaging from PS1 enables discovery of nearby ultracool dwarfs that cannot be identified from 2MASS data alone. We have searched 3700 sq. deg. of PS1 y-band (0.95--1.03 um) data to y$\approx$19.5 mag (AB) and J$\approx$16.5 mag (Vega) and discovered four previously unknown bright T dwarfs. Three of the objects (with spectral types T1.5, T2 and T3.5) have photometric distances within 25 pc and were missed by previous 2MASS searches due to more restrictive color selection criteria. The fourth object (spectral type T4.5) is more distant than 25 pc and is only a single-band detection in 2MASS. We also examine the potential for completing the census of nearby ultracool objects with the PS1 3$\pi$ survey.Comment: 25 pages, 8 figures, 5 table, AJ accepted, updated to comply with Pan-STARRS1 naming conventio