Obvious Objects Research Articles

Studies Advanced in Salient Object Detection based on Deep Learning

A very important job in the area of computer vision is salient object detection, its goal is to check salient or obvious objects in images or videos. Thanks to rapidly developing networks of convolutive neurons, especially generative adversarial networks, CGAN has become a popular framework for salient object detection, which has promoted breakthroughs in detection precision and velocity of protruding objects. Focusing on the topic of salient object detection, in this paper, we systematically introduce the research progress of salient object detection. Specifically, we first introduce methods for detecting protracted objects based on general object detection algorithms (such as Fast R-CNN, YOLO, and SSD), including their basic ideas, key steps, advantages and disadvantages, etc. We then focus on CGAN-based salient object detection methods, including method classification, typical method introduction, and application examples. Finally, we quantitatively compare the performance of different saliency detection algorithms on common datasets, and discuss the existing research problems and feasible solutions of the topic.

Internal audit as a component of the crime prevention system in Ukraine

The study of the anti-criminogenic functionality of internal audit units is of practical importance for the further improvement of the general crime prevention system in Ukraine.
 The purpose of the article: improvement of the crime prevention system in Ukraine. The task of the article: to outline the relationship between the system goal, objects, structural elements, functions, subject composition of the internal audit and the crime prevention system.
 Disclosure of the functionality in the crime prevention system was carried out using the method of comparative analysis of four aspects: 1) system goal; 2) object; 3) structural elements; 4) functions.
 The goal of crime prevention as an artificial system is the absence of criminally punishable violations of social relations, the preservation of the latter. The primary goal of internal audit is to determine the legality of the activities of the relevant entities.
 Regarding crime prevention measures, three groups of criminogenic objects are distinguished: 1) social; 2) legal; 3) psychogenic. The most obvious objects of crime prevention to which the activities of internal audit subjects in Ukraine are directed are legal and psychogenic.
 Basic elements of the internal audit system:
 1) structural subdivisions of the internal audit of entities in the prescribed cases;
 2) officials entrusted with the authority to carry out an internal audit.
 The objective function of the crime prevention system can be considered to be the deterrence of the population from criminal acts and the termination of criminal offenses that have begun.
 The objective function of internal audit units can be defined as prevention of illegal use of budget funds in the activities of the manager of budget funds and enterprises, institutions and organizations belonging to the sphere of his management.
 The relationship between the functions of the crime prevention system and the internal audit system can be outlined as general to separate.

Figuring out justice in dark times: on law, history, and the visual

ABSTRACT What happens when we approach certain objects heuristically as images? How is one to orient oneself through such images? Might those images challenge our existing knowledge of the history of modernisation and written rationalisation of law after the Middle Ages? In this essay, I begin with certain early modern European artworks - paintings, engravings, woodcuts, and drawings - as well as some other less obvious objects - a striking black background in the portrait of a little-known physician, a compelling account of a nocturnal attempt to figure out justice at critical times, the gripping intensity permeating Dürer's allegories of justice, and so on - and investigate the force those objects may have as images. Overall, the intention is to go beyond treating such objects as impassive historical evidence of the particular effort to conceive law intellectually or, alternatively, as codes for certain preexisting messages to be subsequently decoded. On approaching them differently, we may discover that such objects can sometimes resist our analyses or interpretations forcing us to engage with them in unexpected ways.

Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking

Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

STCNet: spatiotemporal cross network for industrial smoke detection

Industrial smoke emissions present a serious threat to natural ecosystems and human health. Prior works have shown that using computer vision techniques to identify smoke is a low-cost and convenient method. However, translucent smoke detection is a challenging task because of the irregular contours and complex motion state. To overcome these problems, we propose a novel spatiotemporal cross network (STCNet) to recognize industrial smoke emissions. The proposed STCNet involves a spatial pathway to extract appearance features and a temporal pathway to capture smoke motion information. Our STCNet is more targeted and goal oriented for dealing with translucent, nonrigid smoke objects. The spatial path can easily recognize obvious nonsmoking objects such as trees and buildings, and the temporal path can highlight the obscure traces of motion smoke. Our STCNet achieves the mutual guidance of multilevel spatiotemporal information by bidirectional feature fusion on multilevel feature maps. Extensive experiments on public datasets show that our STCNet achieves clear improvements against the best competitors by 6.2%. We also perform in-depth ablation studies on STCNet to explore the impacts of different feature fusion methods for the entire model. The code will be available at https://github.com/Caoyichao/STCNet.

Shared Representations in Athletes: Segmenting Action Sequences From Taekwondo Reveals Implicit Agreement.

How do athletes represent actions from their sport? How are these representations structured and which knowledge is shared among experts in the same discipline? To address these questions, the event segmentation task was used. Experts in Taekwondo and novices indicated how they would subjectively split videos of Taekwondo form sequences into meaningful units. In previous research, this procedure was shown to unveil the structure of internal action representations and to be affected by sensorimotor knowledge. Without specific instructions on the grain size of segmentation, experts tended to integrate over longer episodes which resulted in a lower number of single units. Moreover, in accordance with studies in figure-skating and basketball, we expected higher agreement among experts on where to place segmentation marks, i.e., boundaries. In line with this hypothesis, significantly more overlap of boundaries was found within the expert group as compared to the control group. This was observed even though the interindividual differences in the selected grain size were huge and expertise had no systematic influence here. The absence of obvious goals or objects to structure Taekwondo forms underlines the importance of shared expert knowledge. Further, experts might have benefited from sensorimotor skills which allowed to simulate the observed actions more precisely. Both aspects may explain stronger agreement among experts even in unfamiliar Taekwondo forms. These interpretations are descriptively supported by the participants’ statements about features which guided segmentation and by an overlap of the group’s agreed boundaries with those of an experienced referee. The study shows that action segmentation can be used to provide insights into structure and content of action representations specific to experts. The mechanisms underlying shared knowledge among Taekwondoists and among experts in general are discussed on the background of current theoretic frameworks.

Fiberglass as an alternative to metals in ship repair

The article presents the intermediate results of a study carried out to determine the prospects for reducing the metal consumption of ship repair. The area of research is ship repair, in particular, the repair of hulls and superstructures of metal ships, and the object is fiberglass used in the repair, in particular, their strength and durability. The studies were carried out using well-known methods - spontaneous peeling, shearing, limiting states. The results obtained made it possible to establish that the use of fiberglass plastics can reduce the metal consumption of ship repairs by three times. In addition, the article describes a method for calculating the thickness of a fiberglass coating, which is equivalent in strength to a metal backup, and also provides expressions for evaluating the calculated resistance of the coating, which determines its durability. In conclusion, it was concluded that it is necessary to expand the study area of the applicability of fiberglass during the repair of ships, not limiting it only to such obvious objects of repair as the hull, superstructure, pipelines.

Bioidentical Hormone Replacement Therapy at a Hormone Clinic Causing Significant Virilization in a Post-Menopausal Women

Abstract Introduction: Bioidentical hormones (BH) are compounds that have the exact chemical and molecular composition as hormones produced in our bodies. The term typically refers to testosterone, progesterone, and androgens. Compounding pharmacies market these products as being individually tailored, safer, and more effective than modified hormones synthesized under FDA supervision. These compounded products are not subject to FDA approval for dose, purity, safety, or efficacy, which poses a risk to patients. Our case highlights the dangers of unmonitored BH replacement therapy (BHRT) use and the importance of patient education regarding these products. Clinical Case: A 48 year-old woman presented to our clinic for evaluation of elevated testosterone levels. She underwent a total hysterectomy and bilateral oophorectomies 8 years prior for heavy menses. She was briefly placed on estradiol replacement which was discontinued due to tobacco abuse. She presented to a “hormone clinic” where she was told that her testosterone levels were low and was started on BHRT pellets. The composition of these pellets were not disclosed on the manufacturer’s website. They were to be inserted every 3 months. The patient had received two pellet insertions prior to her initial visit. The patient reported feeling miserable with nausea, headaches, hot flashes, severe hirsutism, hair loss, severe aggression, increased libido, and clitoromegaly (the size of a ping-pong ball). Physical exam was notable for hirsutism of the upper lip, cheeks, and chin. No obvious foreign objects were identified in the buttocks. Genital exam was declined. Lab results prior to pellet insertion showed: total testosterone 6 ng/dL (N: 8-60 ng/dL), FSH 96.4 mU/ml (post-menopausal: 20-100 mU/ml) and estradiol < 5 pg/mL (post-menopausal < 41 pg/mL). After the initial pellet insertion: total testosterone 320 ng/dL, FSH 85.3 mU/mL, and estradiol 36.2 pg/mL. Five months after the second pellet insertion, total testosterone was 112 ng/dL, DHEAS 15.2 ug/dL (N: 35.4-256.0 ug/dL), FSH 74.9 mU/mL, and estradiol 46 pg/mL. Ultrasound of the buttocks revealed 1 ovoid hypoechoic structure in each buttock. The patient underwent surgical removal of the pellets. Post-procedural total testosterone was < 12 ng/dL (N < 40 ng/dL). Her aggression and hirsutism improved, but libido became low and clitoromegaly remained unchanged. Conclusion: The misunderstanding that BHRT is safer and more effective than synthesized, FDA-regulated hormones can lead to harmful adverse effects as seen in our patient with significant signs and symptoms of virilization. Patient education regarding the safety and efficacy of unregulated use of these compounds is crucial along with heightened physician awareness for appropriate patient guidance.

Fine-Grained 3D-Attention Prototypes for Few-Shot Learning.

In the real world, a limited number of labeled finely grained images per class can hardly represent the class distribution effectively. Due to the more subtle visual differences in fine-grained images than simple images with obvious objects, that is, there exist smaller interclass and larger intraclass variations. To solve these issues, we propose an end-to-end attention-based model for fine-grained few-shot image classification (AFG) with the recent episode training strategy. It is composed mainly of a feature learning module, an image reconstruction module, and a label distribution module. The feature learning module mainly devises a 3D-Attention mechanism, which considers both the spatial positions and different channel attentions of the image features, in order to learn more discriminative local features to better represent the class distribution. The image reconstruction module calculates the mappings between local features and the original images. It is constrained by a designed loss function as auxiliary supervised information, so that the learning of each local feature does not need extra annotations. The label distribution module is used to predict the label distribution of a given unlabeled sample, and we use the local features to represent the image features for classification. By conducting comprehensive experiments on Mini-ImageNet and three fine-grained data sets, we demonstrate that the proposed model achieves superior performance over the competitors.

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network.

Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.

Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images

A key issue in saliency detection of the foggy images in the wild for human tracking is how to effectively define the less obvious salient objects, and the leading cause is that the contrast and resolution is reduced by the light scattering through fog particles. In this paper, to suppress the interference of the fog and acquire boundaries of salient objects more precisely, we present a novel saliency detection method for human tracking in the wild. In our method, a combination of object contour detection and salient object detection is introduced. The proposed model can not only maintain the object edge more precisely via object contour detection, but also ensure the integrity of salient objects, and finally obtain accurate saliency maps of objects. Firstly, the input image is transformed into HSV color space, and the amplitude spectrum (AS) of each color channel is adjusted to obtain the frequency domain (FD) saliency map. Then, the contrast of the local-global superpixel is calculated, and the saliency map of the spatial domain (SD) is obtained. We use Discrete Stationary Wavelet Transform (DSWT) to fuse the cues of the FD and SD. Finally, a fully convolutional encoder–decoder model is utilized to refine the contour of the salient objects. Experimental results demonstrate that the presented model can remove the influence of fog efficiently, and the performance is better than 16 state-of-the-art saliency models.

Seamline network generation based on foreground segmentation for orthoimage mosaicking

For multiple orthoimages mosaicking, the detection of an optimal seamline in an overlapped region and the generation of a seamline network are two key issues for creating a seamless and pleasant large-scale digital orthophoto map. In this paper, a novel system is proposed to generate the large-scale orthophoto by mosaicking multiple orthoimages via Graph cuts. The proposed system is comprised of two parts. In the first part, to ensure that the detected seamline avoids crossing the obvious objects, a novel foreground segmentation-based approach is proposed to detect the optimal seamline for two adjacent images. The foreground objects are segmented from the overlapped region at the superpixel level followed by the pixel-level seamline optimization. In the second part, we propose a novel seamline network generation approach to produce the large-scale orthophoto by mosaicking multiple orthoimages. The pairwise and junction regions extracted from the initial network are refined using two-label and multi-label Graph cuts, respectively. The key advantage of our proposed seamline network is that junction points can be automatically and optimally found using the multi-label Graph cuts. The experimental results on two groups of orthoimages show that our proposed system can generate high-quality seamline networks with less artifacts, and that it outperforms the state-of-the-art algorithm and the commercial software based on visual comparison and statistical evaluation.

Edge-Enhanced Optimal Seamline Detection for Orthoimage Mosaicking

In this letter, we propose a novel algorithm to detect the optimal seamline by using the edge-enhanced energy function for orthoimage mosaicking. First, we generate the energy cost map by using traditional intensity and gradient difference. Then, to ensure that the detected optimal seamlines avoid crossing the obvious objects, the energy map is enhanced in the regions of valid edge segments around the obvious objects. The edge segments are separately detected from input images, and the invalid edge segments are removed by using texture complexity index, image difference, and consistency constraint. Finally, we detect the optimal seamline from this edge-enhanced energy cost map via graph cuts. Experimental results on several groups of challenging orthoimages show that the proposed algorithm is capable of creating high-quality seamlines for orthoimage mosaicking and outperforms state-of-the-art algorithms and the commercial software based on the visual comparison and statistical evaluation.

Influence of Annealing Temperature on Structural, Morphological, Optical and Electrical Properties of Sol-Gel SnO<sub>2</sub> Thin Films

SnO2 thin films were prepared on glass substrates by sol-gel spin coating method using stannous chloride dihydrate and ethyl alcohol absolute as raw materials at annealing temperature 450-550 °C. The crystal phase was measured by X-ray diffraction (XRD) and showed tetragonal rutile structure with a preferential orientation of (110). Atomic force microscope (AFM) and Scanning Electron Microscope (SEM) images revealed the homogeneous grains distribution, and SEM images showed the obvious rectangular objects corresponding to tetragonal structure. Optical properties were observed by the transmittance in ultraviolet-visible (UV-Vis) region and optical energy gap, which revealed the transmittance over 75% and energy gap between 3.84 eV and 3.89 eV. Finally, I-V characteristics were tested to research electrical properties, and found the gradual non-linear property and the increase of resistance.

Superpixel-based optimal seamline detection in the gradient domain via graph cuts for orthoimage mosaicking

ABSTRACTThis paper presents an optimal seamline detection method for orthoimage mosaicking. To ensure that the detected optimal seamlines avoid crossing many obvious objects, we first design a simple but effective criterion in the gradient domain in lieu of the traditionally used intensity domain to measure the visibility of the seam. Thereafter, we fuse this new criterion into the graph cuts energy minimisation framework to globally find the last optimal seamlines. Instead of finding the optimal solutions of seamlines in overlap regions via graph cuts among the entire set of pixels, we first find them among superpixels created from input images and then refine them in the pixel level, which greatly improves the efficiency of the global graph cuts energy optimisation because the number of elements in graph cuts dramatically decreases. Experimental results on orthoimages show that our proposed method is capable of finding high-quality seamlines for orthoimage mosaicking, and outperforms state-of-the-art algorithms and software.

Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking.

This paper presents a novel method of seamline determination for remote sensing image mosaicking. A two-level optimization strategy is applied to determine the seamline. Object-level optimization is executed firstly. Background regions (BRs) and obvious regions (ORs) are extracted based on the results of parametric kernel graph cuts (PKGC) segmentation. The global cost map which consists of color difference, a multi-scale morphological gradient (MSMG) constraint, and texture difference is weighted by BRs. Finally, the seamline is determined in the weighted cost from the start point to the end point. Dijkstra’s shortest path algorithm is adopted for pixel-level optimization to determine the positions of seamline. Meanwhile, a new seamline optimization strategy is proposed for image mosaicking with multi-image overlapping regions. The experimental results show the better performance than the conventional method based on mean-shift segmentation. Seamlines based on the proposed method bypass the obvious objects and take less time in execution. This new method is efficient and superior for seamline determination in remote sensing image mosaicking.

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index.

Neurite Outgrowth Stimulated by Oxytocin Is Modulated by Inhibition of the Calcium Voltage-Gated Channels.

Neuropeptide oxytocin contributes to the regulation of the neuron differentiation and cell morphology. However, the precise mechanisms are not yet fully understood. Oxytocin receptor function and its coupling to calcium entry are obvious objects of interest in relation to the neuron morphology. Postsynaptic scaffolding proteins including SHANK proteins interact with other synaptic molecules and change dendritic morphology. SH-SY5Y neuroblastoma cell line represents a useful neurobiological in vitro model to study the short-term oxytocin effects on neurite outgrowth and underlying mechanisms. In the present study, we show that oxytocin induces an increase in the intracellular calcium in SH-SY5Y cells. Specificity of the calcium influx was verified by blockade of the oxytocin receptors with oxytocin receptor antagonist L-371,257. Neurite outgrowth stimulated by oxytocin was inhibited by specific voltage-gated calcium channel blockers. The exposure of SH-SY5Y cells to oxytocin resulted in a significant increase in the gene expression of SHANK1 and SHANK3 proteins. Overall, the present data indicate that oxytocin may contribute to the regulation of scaffolding proteins expression known to be associated with clusters of calcium channels at the cell membrane. It appears that oxytocin stimulated neurite outgrowth is, at least, in part dependent on the voltage-gated calcium channels.

Perceptual Load Affects Eyewitness Accuracy and Susceptibility to Leading Questions.

Load Theory (Lavie, 1995, 2005) states that the level of perceptual load in a task (i.e., the amount of information involved in processing task-relevant stimuli) determines the efficiency of selective attention. There is evidence that perceptual load affects distractor processing, with increased inattentional blindness under high load. Given that high load can result in individuals failing to report seeing obvious objects, it is conceivable that load may also impair memory for the scene. The current study is the first to assess the effect of perceptual load on eyewitness memory. Across three experiments (two video-based and one in a driving simulator), the effect of perceptual load on eyewitness memory was assessed. The results showed that eyewitnesses were less accurate under high load, in particular for peripheral details. For example, memory for the central character in the video was not affected by load but memory for a witness who passed by the window at the edge of the scene was significantly worse under high load. High load memories were also more open to suggestion, showing increased susceptibility to leading questions. High visual perceptual load also affected recall for auditory information, illustrating a possible cross-modal perceptual load effect on memory accuracy. These results have implications for eyewitness memory researchers and forensic professionals.

Optimal seamline detection for multiple image mosaicking via graph cuts

While mosaicking images, especially captured from the scenes of large depth differences with respective to cameras at varying locations, the detection of seamlines within overlap regions is a key issue for creating seamless and pleasant image mosaics. In this paper, we propose a novel algorithm to efficiently detect optimal seamlines for mosaicking aerial images captured from different viewpoints and for mosaicking street-view panoramic images without a precisely common center in a graph cuts energy minimization framework. To effectively ensure that the seamlines are optimally detected in the laterally continuous regions with high image similarity and low object dislocation to magnificently conceal the parallax between images, we fuse the information of image color, gradient magnitude, and texture complexity into the data and smooth energy terms in graph cuts. Different from the traditional frame-to-frame optimization for sequentially detecting seamlines for mosaicking multiple images, our method applies a novel multi-frame joint optimization strategy to find seamlines within multi-overlapped images at one time. In addition, we propose simple but effective strategies to semi-automatically guide the seamlines by exploiting simple human–computer interaction strongly constraining the image regions that the seamlines will or won’t pass through, which is often ignored by many existing seamline detection methods. Experimental results on a large set of aerial, oblique and street-view panoramic images show that the proposed method is capable of creating high-quality seamlines for multiple image mosaicking, while not crossing majority of visually obvious foreground objects and most of overlap regions with low image similarity to effectively conceal the image parallax at different extents.