Range Images Research Articles

A Recognition Model Incorporating Geometric Relationships of Ship Components

Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields.

Biophilic analogous patterns for light-responsive architecture during polar night: Examining the photobiological effects of electrical lighting on surface colour configuration

This paper investigates occupants' photobiological responses of biophilic analogous patterns such as electrical lighting and surface colour configuration during dark periods in northern architecture. Biophilic design establishes that the integration of nature-inspired architectural strategies could positively impact occupants’ photobiological responses to enhance well-being in locations with limited daylight access. Photobiological responses are indicated on image-forming (IF) and non-image forming (NIF) effects. IF relates to visual outcomes, reflected on vision and visual tasks, and measured in photopic units. NIF relates to non-visual outcomes such as the biological process of circadian clock stimulation and measured in melanopic units. Limited research addresses the impact of electrical lighting combined with surface colour (SC) configurations to optimize minimum lighting requirements at photopic and melanopic levels in an architectural setting. This research therefore examines eight surface colour configurations tested under three different luminaire positions set at warm, warm-white, and white correlated colour temperature (CCT). The variables generated 72 scenarios, which were captured through high dynamic range images (HDRi), analyzed, and post-processed to display false colour maps and values of illuminance, melanopic-Equivalent Daylight Illuminance (melanopic-EDI) and spatial colour temperature. Results demonstrate the spatial diversification generated by electrical lighting to different visual tasks and circadian stimulation over a day. The study also proved that several coloured surfaces and luminaire settings can generate similar photopic and melanopic lighting conditions despite their perceived spatial colour temperature. This research highlights the photobiological adjustment of biophilic coloured lighting ambiances provided by electrical systems in northern latitudes compromised by extreme photoperiod.

Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments.

In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.

3‐D geophysical modeling of a buried, simple impact crater: Holleford impact structure, Ontario, Canada

AbstractHolleford Crater is a deeply buried, 2.35 km diameter late Proterozoic‐early Cambrian (550 ± 50 Ma) simple impact crater located in southeastern Ontario, Canada. Exploration drilling in the 1950–60s indicated a >450 m deep, simple impact structure with an infill stratigraphy of Cambro‐Ordovician clastic and carbonate sediments and a −2.2 mGal gravity anomaly. We conducted new ground‐based geophysical surveys (magnetics, gravity) and potential field modeling to better resolve the buried impact structure depth, subsurface geometry, and postimpact modification. Geophysical surveys reveal a well‐defined ~3 mGal Bouguer gravity low and <20 nT residual magnetic anomaly over the crater basin. The lack of a well‐defined magnetic anomaly is due to the low magnetic contrast between Mesoproterozoic metasedimentary target rocks and Paleozoic infill sediments. The modeled basement surface shows a deeply buried (>400 m), eroded simple impact structure with a rim‐to‐rim diameter (D) of 1.8–2 km, a residual rim height of about 30 m and a true depth (dt) >400 m. The modeled diameter is smaller than the previous estimate (2.35 km) based on the surface outcrop pattern of Paleozoic strata, which overestimates the buried impact structure dimensions. The modeled basement surface identifies a rim breach and a possible fluvial outflow channel in the southeast impact structure rim. The channel is up to 150 m deep and 400 m in width and has morphology similar to outlet channels produced by fluvial rim dissection of terrestrial impact structures and “tadpole craters” on Mars.

Characterization of two near-infrared genetically encoded voltage indicators.

Efforts starting more than 20 years ago led to increasingly well performing genetically encoded voltage indicators (GEVIs) for optical imaging at wavelengths . Although optical imaging in the wavelength range has many advantages over shorter wavelength approaches for mesoscopic in vivo monitoring of neuronal activity in the mammalian brain, the availability and evaluation of well performing near-infrared GEVIs are still limited. Here, we characterized two recent near-infrared GEVIs, Archon1 and nirButterfly, to support interested tool users in selecting a suitable near-infrared GEVI for their specific research question requirements. We characterized side-by-side the brightness, sensitivity, and kinetics of both near-infrared GEVIs in a setting focused on population imaging. We found that nirButterfly shows seven-fold higher brightness than Archon1 under the same conditions and faster kinetics than Archon1 for population imaging without cellular resolution. But Archon1 showed larger signals than nirButterfly. Neither GEVI characterized here surpasses in all three key parameters (brightness, kinetics, and sensitivity), so there is no unequivocal preference for one of the two. Our side-by-side characterization presented here provides new information for future in vitro and ex vivo experimental designs.

Smart dimming sunglasses for photophobia using spatial light modulator

We present a smart sunglasses system engineered to assist individuals experiencing photophobia, particularly those highly sensitive to light intensity. The system integrates a high dynamic range (HDR) camera and a liquid crystal spatial light modulator (SLM) to dynamically regulate light, adapting to environmental scenes by modifying pixel transmittance through a specialized control algorithm, thereby offering adaptable light management to meet the users’ visual needs. Nonetheless, a conventional occlusion mask on the SLM, intended to block incoming light, emerges blurred and insufficient due to a misaligned focal plane. To address the challenge of imprecise light filtering, we introduce an optimization algorithm that meticulously adjusts the light attenuation process, effectively diminishing excessive brightness in targeted areas without adversely impacting regions with acceptable levels of luminance.

AUTOMATIC COARSE CO-REGISTRATION OF POINT CLOUDS FROM DIVERSE SCAN GEOMETRIES: A TEST OF DETECTORS AND DESCRIPTORS

Abstract. Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird’s eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenario whereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.

Application of a New Type of Coded Aperture for Reconstruction of Laser Plasma Images in the Soft X-ray Range

Application of a New Type of Coded Aperture for Reconstruction of Laser Plasma Images in the Soft X-ray Range

Fast and Accurate Illumination Estimation Using LDR Panoramic Images for Realistic Rendering.

A high dynamic range (HDR) image is commonly used to reveal stereo illumination, which is crucial for generating high-quality realistic rendering effects. Compared to the high-cost HDR imaging technique, low dynamic range (LDR) imaging provides a low-cost alternative and is preferable for interactive graphics applications. However, the limited LDR pixel bit depth significantly bothers accurate illumination estimation using LDR images. The conflict between the realism and promptness of illumination estimation for realistic rendering is yet to be resolved. In this paper, an efficient method that accurately infers illuminations of real-world scenes using LDR panoramic images is proposed. It estimates multiple lighting parameters, including locations, types and intensities of light sources. In our approach, a new algorithm that extracts illuminant characteristics during the exposure attenuation process is developed to locate light sources and outline their boundaries. To better predict realistic illuminations, a new deep learning model is designed to efficiently parse complex LDR panoramas and classify detected light sources. Finally, realistic illumination intensities are calculated by recovering the inverse camera response function and extending the dynamic range of pixel values based on previously estimated parameters of light sources. The reconstructed radiance map can be used to compute high-quality image-based lighting of virtual models. Experimental results demonstrate that the proposed method is capable of efficiently and accurately computing comprehensive illuminations using LDR images. Our method can be used to produce better realistic rendering results than existing approaches.

Combining 3D SONAR with High Resolution Imaging to Improve Vision in Atrial Fibrillation Ablation

Real-time, accurate imaging of improperly functioning tissue that causes atrial fibrillation (AF) is essential for successful catheter ablation procedures. Ablation procedures and success rates may be improved by overlaying intracardiac echocardiography with high resolution cardiac imaging. Data extracted from previous research reports that current pharmacological agents do not address the principal cause of AF. Unlike medication, catheter ablation resolves the issue by treating AF’s source. Therefore, catheter ablation is the better alternative. However, while catheter ablation is preferable to medication, the detail and accuracy of anatomical visualizations used to guide the catheter can be inconsistent. While real time guidance technology updates, the visibility quality is limited. However, detailed preliminary imaging does not accurately reflect the latest changes. This paper discusses the current limitations of catheter ablation and proposes real-time three-dimensional Sound Navigation and Ranging (SONAR) imaging paired with high-resolution cardiac Magnetic Resonance Imaging (MRI) as a novel tool to improve live-imaging localizations and resolution for cardiac ablation procedures.

A new species of alkali-sink Paruroctonus Werner, 1934 (Scorpiones, Vaejovidae) from California’s San Joaquin Valley

Herein a new species of Paruroctonus Werner, 1934 is described from alkali-sink habitats in the San Joaquin Desert of central California, Paruroctonus tularesp. nov. It can be differentiated from other Paruroctonus by a combination of morphological features including scalloped pedipalp fingers in males, specific setal counts and morphometric ratios, and specific patterns of fuscous pigmentation. It also inhabits a unique distribution allopatric with all other Paruroctonus species except P. variabilis Hjelle, 1982. Photographs of a large series of live P. tularesp. nov. from across their range and detailed images of several morphological features are provided, their distribution is modeled, a haplotype network is presented, and details about their habitat, ecology, and conservation are discussed.

Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision

Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.

CIECAM16-based Tone Mapping of High Dynamic Range Images

CIECAM16-based Tone Mapping of High Dynamic Range Images

Natureness‐preserving tone mapping operator based on improved guided filter and adaptive Gamma curve

AbstractMapping high dynamic range (HDR) images to low dynamic range (LDR) display devices while performing enhancement processing is a challenging task. To address the issues of chromatic drift, the imbalance between preserving naturalness and enhancing details during the mapping process, this paper proposed a tone mapping operator (TMO) based on an improved guided filter with multi‐layered processing. Firstly, the operator tackled the problem of insufficient extraction of detail layer information in complex scene images by redefining the formula of the guided filter and introducing adaptive regularization parameters. This enhancement enabled more effective extraction of details compared to traditional fixed‐parameter guided filters. Secondly, the algorithm employed a centre‐surround approach to subdivide and processed the detail layers individually, effectively suppressing halos and artefacts. Thirdly, image entropy was utilized to guide the fusion and enhancement processing of the detail layers, preventing excessive or insufficient enhancement. Finally, this paper proposed an adaptive gamma correction algorithm and a chromatic consistency algorithm to improve overall contrast and hue saturation. The results demonstrate convincing performance in terms of subjective visual evaluation and image quality assessment.

Revision of the Quaternary calcareous nannofossil biochronology of Arctic Ocean sediments

Despite extensive chronological studies, the relationship between the age and sub-seafloor depth of Arctic Ocean sediments remains ambiguous. This prevents confident identification of paleoceanographic changes in the Arctic during the Quaternary. Currently, age-depth models derived from uranium-series decay in Arctic sediments diverge by hundreds of thousands of years compared to those built on known evolutionary appearances and extinctions of calcareous nannoplankton, a group of globally valuable age-markers. Here we report on high-resolution biostratigraphic analysis of late Quaternary sediments in six cores from the central Arctic Ocean (CAO). We applied paired light microscope (LM) and scanning electron microscope (SEM) imaging to improve nannofossil diagnosis. We argue that low abundances and poor preservation have led to misidentification of the true stratigraphic depth of the critical Pleistocene nannofossil bio-events that have underpinned age models for many Arctic sedimentary records for decades. The revised calcareous nannofossil biochronology provides a radically different geochronological framework for CAO sediments – indicating that what had previously been identified as Marine Isotope Stage (MIS) 7 (191–243 ka) in many sedimentary records is older than MIS 12 (424–478 ka). Furthermore, it suggests that previously inferred sub-stages of MIS 5 could represent full interglacial periods rather than interstadials. The results help reconcile the different dating approaches and provide a transformative step towards resolving the disparity in Quaternary Arctic age-depth models, bringing us one step closer to accurate paleoceanographic reconstructions based on sediment cores.

On the optical performance of incoherent digital holography for extended 3D objects

Digital Holography (DH) technology has grown fast over the years, offering the possibility to encode complex information from three-dimensional real-world objects onto two-dimensional digital supports. Accessing the object as an entire numerical complex wavefront raised large expectations in the fields of imaging and display. Holographic vision through fog, smoke, and flames in the far infrared (IR) range would be, in principle, applicable to the fields of automotive, video surveillance, and homeland security. However, the need for bulky, high-coherence laser sources has dampened the enthusiasm around the development of holographic cameras for use in such a context. Here, we investigate self-interference digital holography (SIDH) as a method for obtaining refocusable wavefronts by using low-coherence light sources, a category including light bulbs, arc lamps, light-emitting diodes, low-coherence IR lasers, heat sources, and sunlight. We develop and analyze a model to study the optical performance of SIDH in terms of reconstruction distance range, lateral magnification, resolution, and limiting factors. Then, we propose a configuration for reshaping the system Point Spread Function (PSF) and enhancing resolution by tuning the visibility of high-frequency fringes. The model is experimentally verified by imaging various scenes under incoherent illumination in the visible range. As a last testbed, we provide a first proof of concept SIDH imaging in the far IR range by retrieving and refocusing the complex wavefront back-scattered by point sources illuminated by low-coherence IR light.

Wavelet-based network for high dynamic range imaging

High dynamic range (HDR) imaging from multiple low dynamic range (LDR) images has been suffering from ghosting artifacts caused by scene and objects motion. Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. Comprehensive empirical evidence shows that ghosting artifacts caused by large foreground motion are mainly low-frequency signals and the details are mainly high-frequency signals. In this work, we propose a novel frequency-guided end-to-end deep neural network (FHDRNet) to conduct HDR fusion in the frequency domain, and Discrete Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details. Using a U-Net as the backbone, we propose two novel modules: merging module and frequency-guided upsampling module. The merging module applies the attention mechanism to the low-frequency components to deal with the ghost caused by large foreground motion. The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details. In addition, a new RAW dataset is created for training and evaluating multi-frame HDR imaging algorithms in the RAW domain. Extensive experiments are conducted on public datasets and our RAW dataset, showing that the proposed FHDRNet achieves state-of-the-art performance.

Exploring the influence of wavelength and polarization of illuminating light on polarization imaging for birefringent objects concealed behind scattering structure

The effect of wavelength and polarization of illuminating light in polarization imaging for birefringent objects placed behind a scattering structure was investigated experimentally. Although the obtained images of degree of polarization, azimuthal angle, and ellipticity angle were strongly affected by scattering, the spatial distribution of the birefringent object was more clearly visualized in the longer wavelength combined with circularly polarized light illumination condition. This result indicates that polarization imaging in the near-infrared range using circularly polarized light illumination should be a powerful tool for visualizing birefringent object with scattering, and will be applicable to biomedical sensing and remote sensing.

Bioinspired, vertically stacked, and perovskite nanocrystal-enhanced CMOS imaging sensors for resolving UV spectral signatures.

Imaging and identifying target signatures and biomedical markers in the ultraviolet (UV) spectrum is broadly important to medical imaging, military target tracking, remote sensing, and industrial automation. However, current silicon-based imaging sensors are fundamentally limited because of the rapid absorption and attenuation of UV light, hindering their ability to resolve UV spectral signatures. Here, we present a bioinspired imaging sensor capable of wavelength-resolved imaging in the UV range. Inspired by the UV-sensitive visual system of the Papilio xuthus butterfly, the sensor monolithically combines vertically stacked photodiodes and perovskite nanocrystals. This imaging design combines two complementary UV detection mechanisms: The nanocrystal layer converts a portion of UV signals into visible fluorescence, detected by the photodiode array, while the remaining UV light is detected by the top photodiode. Our label-free UV fluorescence imaging data from aromatic amino acids and cancer/normal cells enables real-time differentiation of these biomedical materials with 99% confidence.

A Portable Three-Layer Compton Camera for Wide-Energy-Range Gamma-ray Imaging: Design, Simulation and Preliminary Testing

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in the active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy. (2) Methods: To realize gamma-ray imaging in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, is developed using the scintillation detector that consists of an silicon photomultiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on the two interaction events is applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of the detectors is established based on the Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources have been successfully obtained with our prototype in laboratory tests and compared with simulation studies. (3) Results: The proportion of effective imaging events accounts for about 2%, which allows our prototype to realize the reconstruction of the distribution of a 0.05 μSv/h 137Cs source in 10 s. The angular resolution for resolving two 137Cs point-like sources is 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrates the imaging capability for high incident energy. (4) Conclusions: We conclude that the prototype has a good imaging performance in a wide energy range (0.3~7 MeV), which shows potential in several MeV gamma-ray imaging applications.