High Contrast Background Research Articles

Cutting-edge applications of oleic acid-modified CdSiO3: Ce3+ phosphors: Artificial intelligence enhanced latent fingerprint detection, anti-counterfeiting and optical thermometry

A series of (1–11 mol%) Ce³⁺ doped CdSiO₃ phosphors are synthesized via a solution combustion method, and the sample with the optimal doping concentration underwent surface modification with oleic acid (OA). X-ray diffraction (XRD) analysis confirmed the formation of high-purity, well-crystallized CdSiO3:Ce3+ phosphors. Upon 347 nm UV excitation, the phosphor exhibited intense blue emission at 400 nm, with an optimized Ce³⁺ concentration of 5 mol%, attributed to dipole-dipole interactions. The OA-modified phosphor demonstrated significantly enhanced properties, achieving a color purity (CP) of 97.2% and Internal quantum efficiency (IQE) of 82.5%. The phosphor also exhibited excellent thermal stability, retaining 93.0% of its emission intensity at 423 K and a thermal quenching temperature exceeding 483 K, with a high activation energy (Eₐ) of 0.384 eV. In practical applications, the OA modified phosphor showed exceptional performance in latent fingerprints (LFPs) detection and anti-counterfeiting (AC) across different substrates, offering high resolution, contrast, and minimal background interference. MATLAB based analysis yielded a matching score of 94.52%, surpassing conventional benchmarks, underscoring the phosphor's potential for advanced fingerprint identification. These results demonstrate that the CdSiO₃:5Ce³⁺ phosphor is a promising candidate for applications in white light-emitting diodes (w-LEDs), optical thermometry, forensic analysis and AC technologies.

Dazzling damselfish: investigating motion dazzle as a defence strategy in humbug damselfish (Dascyllus aruanus)

Many animals possess high-contrast body patterns. When moving, these patterns may create confusing or conflicting visual cues that affect a predator’s ability to visually target or capture them, a phenomenon called motion dazzle. The dazzle patterns may generate different forms of optical illusion that can mislead observers about the shape, speed, trajectory and range of the animal. Moreover, it is possible that the disruptive visual effects of the high contrast body patterns can be enhanced when moving against a high contrast background. In this study, we used the humbug damselfish (Dascyllus aruanus) to model the apparent motion cues of its high contrast body stripes against high contrast background gratings of different widths and orientations, from the perspective of a predator. We found with higher frequency gratings, when the background is indiscriminable to a viewer, that the humbugs may rely on the confusing motion cues created by internal stripes. With lower frequency gratings, where the background is likely perceivable by a viewer, the humbugs can rely more on confusing motion cues induced by disruption of edges from both the background and body patterning. We also assessed whether humbugs altered their behaviour in response to different backgrounds. Humbugs remained closer and moved less overall in response to backgrounds with a spatial structure similar to their own striped body pattern, possibly to stay camouflaged against the background and thus avoid revealing themselves to potential predators. At backgrounds with higher frequency gratings, humbugs moved more which may represent a greater reliance on the internal contrast of the fish’s striped body pattern to generate motion dazzle. It is possible that the humbug stripes provide multiple protective strategies depending on the context and that the fish may alter their behaviour depending on the background to maximise their protection.

DiasMorph: a dataset of morphological traits and images of Central European diaspores

We present DiasMorph, a dataset of images and traits of diaspores from 1,442 taxa in 519 genera, and 96 families from Central Europe, totalling 94,214 records. The dataset was constructed following a standardised and reproducible image analysis method. The image dataset consists of diaspores against a high-contrast background, enabling a simple and efficient segmentation process. The quantitative traits records go beyond traditional morphometric measurements, and include colour and contour features, which are made available for the first time in a large dataset. These measurements correspond to individual diaspores, an input currently unavailable in traits databases, and allow for several approaches to explore the morphological traits of these species. Additionally, information regarding the presence and absence of appendages and structures both in the images and diaspores of the assessed taxa is also included. By making these data available, we aim to encourage initiatives to advance on new tools for diaspore identification, further our understanding of morphological traits functions, and provide means for the continuous development of image analyses applications.

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability.

Cortical Zinc Signaling Is Necessary for Changes in Mouse Pupil Diameter That Are Evoked by Background Sounds with Different Contrasts.

Luminance-independent changes in pupil diameter (PD) during wakefulness influence and are influenced by neuromodulatory, neuronal, and behavioral responses. However, it is unclear whether changes in neuromodulatory activity in a specific brain area are necessary for the associated changes in PD or whether some different mechanisms cause parallel fluctuations in both PD and neuromodulation. To answer this question, we simultaneously recorded PD and cortical neuronal activity in male and female mice. Namely, we measured PD and neuronal activity during adaptation to sound contrast, which is a well-described adaptation conserved in many species and brain areas. In the primary auditory cortex (A1), increases in the variability of sound level (contrast) induce a decrease in the slope of the neuronal input-output relationship, neuronal gain, which depends on cortical neuromodulatory zinc signaling. We found a previously unknown modulation of PD by changes in background sensory context: high stimulus contrast sounds evoke larger increases in evoked PD compared with low-contrast sounds. To explore whether these changes in evoked PD are controlled by cortical neuromodulatory zinc signaling, we imaged single-cell neural activity in A1, manipulated zinc signaling in the cortex, and assessed PD in the same awake mouse. We found that cortical synaptic zinc signaling is necessary for increases in PD during high-contrast background sounds compared with low-contrast sounds. This finding advances our knowledge about how cortical neuromodulatory activity affects PD changes and thus advances our understanding of the brain states, circuits, and neuromodulatory mechanisms that can be inferred from pupil size fluctuations.

A novel highly thermal-stable Ba2GdSbO6:Sm3+ phosphor with intrinsic deep-red emission for personal identification and plant cultivation LEDs

The novel Sm3+-activated Ba2GdSbO6 (BGSO) antimonate phosphors with double perovskite structure were first synthesized via a high-temperature solid-state reaction. The products' crystallographic structure, phase purity, micro-morphology, and luminescent properties were analyzed. The Ba2GdSbO6:Sm3+ phosphor could be effectively excited by 406 nm irradiation, and four sharp peaks accompanied by a broad band were presented. The dominant peak was located at 602 nm, corresponding to the typical 4G5/2 → 6H7/2 transition of Sm3+ ions, while the deep-red emission band at 675 nm is owing to the BGSO host intrinsic emission. Impressively, the product shows high thermal stability with its emission intensity of 88.8% at 420 K. Moreover, its thermal quenching temperature exceeds 480 K. The electroluminescent spectrum of the red light-emitting diode (LED) device fabricated by BGSO:0.02Sm3+ overlapped well with the absorption spectra of the phytochromes, suggesting its potential for plant cultivation light source. The sample with the best doping concentration was selected for surface modification with oleic acid (OA). In latent fingerprint and lip print detection, the phosphor showed high resolution, high contrast, and low background interference applied on different substrates. Hence, the BGSO:Sm3+ phosphor can be considered a promising material in plant cultivation LEDs and latent fingerprint and lip print visualization.

A convex selective segmentation model based on a piece-wise constant metric-guided edge detector function

The challenge of segmentation for noisy images, especially those that have light in their backgrounds, still exists in many advanced state-of-the-art segmentation models. Furthermore, it is a tedious activity and significantly difficult to segment such images. In this article, we offer and propose a novel variational model for the concurrent restoration and segmentation of noisy images that have intensity inhomogeneity and high contrast background illumination and light. The suggested concept combines the multi-phase segmentation technology with the statistical approach in terms of local region knowledge and details of circular regions that are, in fact, centered at every pixel to enable in-homogeneous image restoration. Nevertheless, the suggested approach is expressed as a fuzzy set and is resolved using the multiplier alternating direction minimization approach. Through several tests and numerical simulations with plausible assumptions, we have evaluated the correctness, accuracy, and resilience of the suggested model over various kinds of real and synthesized images in the existence of intensity inhomogeneity and light in the background. Additionally, the findings are contrasted with those from cutting-edge two-phase and multi-phase methods, proving the superiority of our proposed approach for images with noise, background light, and inhomogeneity. We observed that the three models considered in the comparison to our proposed model fail to properly segment the object of interest. Furthermore, the Jaccard similarity metric shows the capability of our model to handle accurate segmentation of complex shapes with intensity.

Post-Synthetic Modification of a Fluorescent Covalent Organic Framework as a Multifunctional Sensor for Efficient Detection of 5-Hydroxytryptamine and Its Metabolite and Latent Fingerprint Identification

In this study, a functionalized vinyl-linked 2D fluorescent covalent organic framework (COF) (TMT-TA-COOH) is constructed by post-synthetic modification (PSM) of a TMT-TA COF, which is composed of trimethyltriazine (TMT) and terephthalaldehyde (TA). The fluorescent COF (TMT-TA) has the characteristics of good crystallization and stability, but the dispersibility in water is poor, which requires further PSM. After PSM, the obtained TMT-TA-COOH not only exhibits good dispersibility in water but also displays stronger binding ability to analytes, which presents high sensitivity for quantitative identification of 5-hydroxytryptamine (5-HT) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA) in water. Moreover, TMT-TA-COOH can specifically recognize 5-HT in serum and 5-HIAA in urine with a fast response, good anti-interference performance, and real-time visualization, which enables the TMT-TA-COOH to be a tool for early detection of neurological diseases and biomarkers for carcinoid tumors. In addition, TMT-TA-COOH is also a good candidate for latent fingerprint (LFP) identification, which has the characteristics of high contrast and low background interference. This work not only provides a promising platform for the sensing application of fluorescent COFs in aqueous solution but also opens up a new way for the detection of LFP information.

Physical visualization and squalene-based scanning electrochemical microscopy imaging of latent fingerprints on PVDF membrane.

Fingerprints have long been the gold standard for personal identification in forensic science. However, realizing the high-resolution enhancement of eccrine LFPs is difficult using the traditional methods and the label-free detection of fingerprint residue information is also challenging. Herein, we propose two enhancement strategies for LFPs on PVDF membrane (LFPs/PVDF) using blue-black ink staining and scanning electrochemical microscopy (SECM). The blue-black ink staining method was used for the first time to develop three types (sebaceous, natural and eccrine) of LFPs/PVDF based on the difference in wettability between the fingerprint residues and PVDF membrane. The enhanced fingerprints clearly displayed levels 1-3 features with high contrast and low background interference. Furthermore, we achieved chemical imaging of the LFP/PVDF samples, where their possible visualization mechanisms were ascribed to the electrochemical reactivity of squalene and difference in wettability between the LFP and PVDF membrane, which was first proposed and investigated by SECM imaging and water contact angle (WCA) measurements, respectively. Significantly, SECM imaging not only provided fingerprint patterns without any labelling but also revealed the spatial distribution information of squalene in LFPs simultaneously. In addition, it was also demonstrated that LFPs deposited on various surfaces were first successfully transferred to the PVDF membrane, and then further developed with both methods, making them general for personal identity-related applications. Taken together, the blue-black ink staining method can easily and quickly obtain level 3 features of LFPs/PVDF and the SECM approach can non-invasively image the topography and chemical information of LFPs/PVDF, and thus they can be potentially selected according to various requirements in forensic scenarios.

Eu-Doped Gd2MoB2O9 Phosphors for Latent Fingerprints Detection

The luminescent powders from rare-earth ions doped oxides have been successfully used for latent fingerprint detection due to high efficiency, high contrast and low background interface. The Gd2MoB2O9:Eu3+ phosphors with varying compositions of Eu3+ions were synthesized by solid state reaction method. These phosphors were investigated by X-ray diffraction (XRD), diffuse reflectance spectroscopy, photoluminescence (PL), and decay curves studies. Multiphoton relaxation decay rate and coupling strength was evaluated using phonon side band spectrum showing 0.4 mol% doped samples show less non radiative decay rate. The 0.40 mol% of Eu3+ ion concentration doped in the Gd2MoB2O9 host lattice show highest photoluminescence emission intensity at 613 nm, in orange red region, monitored at 394 nm excitation. The Gd2MoB2O9:Eu3+ phosphors samples were optimized to 900 °C sintering temperature and optimum milling time at 3 min in the present work. The latent fingerprints detection that developed from Gd2MoB2O9:Eu3+ phosphors in this work exhibited clearly ridge characteristics under UV illumination. The results revealed thatGd2MoB2O9:Eu3+ phosphors are potential practical forensic science applications.

Mono- and Rare Trinuclear Zn(II) Complexes with Near-Infrared Emissive Ligands: Anion-Responsive Nuclearity Control, Interconversion, Solid-State NIR Emission, and Latent Fingerprint Imaging

The ability to predictably control the nuclearity of metal complexes is vital in developing functional coordination compounds and metal–organic frameworks. However, consistent nuclearity control achieving complexes of different nuclearities using a series of ligands is difficult, especially when the metal ion involved does not show any crystal field stabilization energy for a particular geometry. Herein, a series of three mononuclear (M1–M3) and the corresponding trinuclear (T1–T3) Zn(II) complexes have been synthesized from a series of near-infrared (NIR)-emitting ligands (HL1–HL3) through counterion control (ZnCl2 vs Zn(OAc)2). The structural analyses revealed a regular distorted tetrahedral geometry for the mononuclear complexes, M1–M3. However, the complexes T1–T3 showed a rare trinuclear structure, in which two deprotonated ligand molecules coordinate with three Zn(II) centers such that a central octahedral Zn ion is connected to two terminal tetrahedral Zn ions through phenolate and acetate bridging groups. Intramolecular π–π stacking interactions between the constituting ligands play significant roles in forming such a rare geometry. The plausible reasons for the counterion-controlled nuclearity have been proposed along with successful interconversion of trinuclear complexes to mononuclear complexes at 65 °C. The complexes M1–M3 and T1–T3 exhibited substituent and nuclearity-dependent photophysical properties. In the solid state, two of the mononuclear complexes, M1 and M2, displayed NIR emissions (661–670 nm), while the trinuclear complexes T1–T3 emitted yellow/orange fluorescence (572–607 nm). The complexes M2 and T1 were successfully tested as latent fingerprint (LFP) imaging agents on several non-infiltrating and semi-infiltrating substrates, including everyday household objects, using the powder dusting method. In all these cases, highly fluorescent LFP images with high sensitivity, selectivity, and contrast and low background interference have been obtained.

Invited Session V: GABAergic function and dysfunction in visual perception: Abnormal processing of visual context in schizophrenia

People with schizophrenia (SZ) exhibit persistent deficits in visual processing including reduced contrast sensitivity and poor detection of complex image structure, such as contours. Over recent years accumulating psychophysical evidence indicates that vision in SZ is associated with a reduced influence of spatial context which in turn has been attributed to reduced spatial suppression in visual cortex. Compared to controls, people with SZ do not experience the same reduction in perceived contrast that occurs when targets are embedded in a high contrast background. This appears to be a cortical phenomenon: reduced influence of context is not limited to contrast but carries over to other image attributes (such as orientation) but not others (such as luminance). Finally, although patients poor processing of higher-order image structure has been attributed to differences in perceptual grouping, work from my lab indicates that abnormal processing of context may also contribute to patients' poor sensitivity for contours. It does this by (a) reducing their sensitivity to local orientation and (b) reducing their ability to discount irrelevant background structure. Taken together these results suggest that abnormal processing of context contributes to a range of visual processing deficits in SZ.

Global Sparsity-Weighted Local Contrast Measure for Infrared Small Target Detection

Local contrast measure (LCM) proves effective in infrared (IR) small target detection. Existing LCM-based methods focus on mining local features of small targets to improve detection performance. As a result, they struggle to reduce false alarms while maintaining detection rates, especially with high-contrast background interference. To address this issue, this letter proposes global sparsity-weighted local contrast measure (GSWLCM), which fuses both global and local features of small targets. First, robust local contrast measure (RLCM) is proposed to remove low-contrast backgrounds and extract candidate targets. Then, to suppress high-contrast backgrounds, we customize the random walker (RW) to extract candidate target pixels, construct the global histogram and calculate global sparsity. Finally, GSWLCM fusing global and local features is calculated and the target is detected by adaptive threshold segmentation. Extensive experimental results show that the proposed method is effective in suppressing high-contrast backgrounds and has better detection performance than several state-of-the-art methods.

Design of green emitting CaZrO3:Tb3+ nanophosphor: Luminescence based platform for real-time ultrasensitive detection of latent fingerprints and anti-counterfeiting applications

Rare earth doped phosphors are widely applied to the numerous fields, such as luminescent paints, white-light emitting devices, photovoltaics, security inks, etc., owing to their inherent properties, such as sharp emission profiles, substantial stoke shifts, and long luminescence life time. Here, green emitting CaZrO3:Tb3+ (x = 1–11 mol %) nanophosphors have been successfully prepared by solution combustion route using Cordia myxa (lasura) leaves extract as fuel. The powder X-ray diffraction patterns shows orthorhombic crystal structure. Surface morphology of the samples showed porous and flakes type with large void spaces. Photoluminescence spectra exhibits a series of sharp peaks in the wavelength range of 475–650 nm, which ascribed to 5D4→7F6-3 transitions of the Tb3+ ions. The Commission Internationale de I'Eclairage chromaticity coordinates are evaluated from the photoluminescence emission spectra and exhibited co-ordinates (0.32, 0.40) located within the plane green region of the diagram. Highest color purity was achieved for 5 mol % of Tb3+ ions doped CaZrO3 nanophosphors with an average correlated color temperature value ~6025 K. Thermoluminescence properties of the prepared nanophosphors exposed with ultraviolet light exhibit two glow peaks at ~166 and 328 °C. A linear and sub-linear responses of the thermoluminescence intensity after ultraviolet light exposure on samples confirms that the samples are more suitable for dosimetry applications. Latent fingerprints are developed using optimized CaZrO3:Tb3+(5 mol %) nanophosphor exhibit high sensitivity and selectivity, high contrast and low background interference on different surfaces. More importantly, the single information encryption strategy was designed and used for high-level anti-counterfeiting. The results clearly indicate that the optimized nanophosphor has great potential for real time applications in white light emitting diodes, dosimetry and advanced forensic applications.

Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.

Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.

Improved Fuzzy C-Means for Infrared Small Target Detection

Infrared (IR) small target detection has been extensively studied due to its importance in IR search and tracking (IRST) systems. Existing methods have some limitations in suppressing cluttered high-contrast backgrounds, which may result in more false detections. In this letter, on the one hand, we propose an improved fuzzy C-mean (IFCM) clustering to accurately segment IR small targets and complex backgrounds. On the other hand, an IFCM-based descriptor fusing multiple features (IFCM-MF) is proposed to suppress complex backgrounds. First, a sliding window is designed to quickly extract the candidate pixels. Then, to better enhance the target and suppress the background, we construct an IFCM-based local window and calculate the IFCM-MF. Finally, IR small targets are detected by adaptive thresholding operation. The experimental results show that our method can better suppress cluttered high-contrast backgrounds and significantly improve the detection performance with high speed.

Metasurface-based total internal reflection microscopy

Recent years have seen a tremendous progress in the development of dielectric metasurfaces for visible light applications. Such metasurfaces are ultra-thin optical devices that can manipulate optical wavefronts in an arbitrary manner. Here, we present a newly developed metasurface which allows for coupling light into a microscopy coverslip to achieve total internal reflection (TIR) excitation. TIR fluorescence microscopy (TIRFM) is an important bioimaging technique used specifically to image cellular membranes or surface-localized molecules with high contrast and low background. Its most commonly used modality is objective-type TIRFM where one couples a focused excitation laser beam at the edge of the back focal aperture of an oil-immersion objective with high numerical aperture (N.A.) to realize a high incident-angle plane wave excitation above the critical TIR angle in sample space. However, this requires bulky and expensive objectives with a limited field-of-view (FOV). The metasurface which we describe here represents a low cost and easy-to-use alternative for TIRFM. It consists of periodic 2D arrays of asymmetric structures fabricated in TiO2 on borosilicate glass. It couples up to 70% of the incident non-reflected light into the first diffraction order at an angle of 65° in glass, which is above the critical TIR angle for a glass-water interface. Only ∼7% of the light leaks into propagating modes traversing the glass surface, thus minimizing any spurious background fluorescence originating far outside the glass substrate. We describe in detail design and fabrication of the metasurface, and validate is applicability for TIRFM by imaging immunostained human mesenchymal stem cells over a FOV of 200 µm x 200 µm. We envision that these kinds of metasurfaces can become a valuable tool for low-cost and TIRFM, offering high contrast, low photodamage, and high surface selectivity in fluorescence excitation and detection.

Novel red photoluminescence sensor based on Europium ion doped calcium hydroxy stannate CaSn(OH)6:Eu+3 for latent fingerprint detection

Herein, we have synthesized a novel CaSn(OH)6:Eu3+ nanoparticles modified with trisodium citrate (TSC) by using sol-gel technique for latent fingerprint technology. The particles are characterized by powder X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, optical absorption and photoluminescence (PL) studies as well as energy-dispersive spectroscopy (EDS). The morphology of the particles has been determined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The synthesized nanoparticles CaSn(OH)6:Eu3+ phosphors emitted high intensity fluorescence peak at 616 nm upon excitation at 285 nm, which will provides latent fingerprint images with a high resolution, high contrast, high selectivity, high efficiency and low background interference on various substrates. The results show that CaSn(OH)6:Eu3+ employed in analytical detection of visualization of latent fingerprints (LFPs) for individual identification in forensic science.

Drone-based photogrammetry for the construction of high-resolution models of individual trees

Key messageUAV-based photogrammetry can provide detailed, accurate, and consistent structural models of open-grown trees across different species and growth forms. Environmental and ambient light conditions impact the results.Our empirical knowledge of the complicated and highly dynamic three-dimensional structure of trees is limited. Drone-based photogrammetry offers a powerful and effective tool for capturing, analyzing, and monitoring adult trees, particularly individuals growing outside of the closed canopy forest (subsequently termed “open-grown”) in both natural and built environments. Here, we test the accuracy and consistency of high-resolution models of individual open-grown trees of three structurally distinct species. To validate model accuracy, we compared model estimates with direct measurements of branch diameter and internode length. We also examined the consistency of models of the same individual given different ambient light conditions by comparing corresponding measurements from two models of each tree. We use readily available equipment and software, so our protocol can be rapidly adopted by professional and citizen scientists alike. We found that the models captured diameter and interior internode length well (r2 = 0.87 and 0.98, respectively). Difference between measurements from different models of the same tree was less than 3 cm for diameters and interior internode lengths of most size classes. Thin distal branches were not captured well: measurements of terminal internodes in most size classes were underestimated by 50–60 cm. Models from overcast days were more accurate than models from sunny days (p = 0.0056), and a high-contrast background helps capture thin branches. Our approach constructs accurate and consistent three-dimensional models of individual open-grown trees that can provide the foundation for a wide range of physiological, behavioral, and growth studies.

Unsupervised Oil Tank Detection by Shape-Guide Saliency Model

In this letter, a novel oil tank detection framework based on a shape-guide saliency (SGS) model is proposed. Beyond the low-level visual stimuli, SGS focuses more on simulating the selective visual searching, which is dominated by the goal in human minds. Using a top–down strategy, SGS breaks the limitation of the low-level visual features and introduces the high-level task concept to measure saliency. For the oil tank detection, SGS model skillfully extracts the contour shape cue (CSC) as the target-oriented information and uses CSC to guide the selective saliency value calculation. Specifically, a sparse reconstruction with the target-specific dictionary is implemented to generate the saliency map. This saliency map only assigns high values to oil tank regions instead of highlighting all high-contrast regions. Consequently, SGS model is capable of accurately locating oil tanks and eliminating the interferences of high-contrast backgrounds. Experimental results on a remote sensing data set demonstrate that the proposed SGS model outperforms five class-independent saliency models. Comparisons with the state-of-the-art oil tank detection approaches demonstrate the effectiveness of the proposed method.