Multiple Pictures Research Articles

Optimization and Application Analysis of Phase Correction Method Based on Improved Image Registration in Ultrasonic Image Detection

ABSTRACTIn order to prevent and detect a wide range of disorders, including those of the brain, thoracic, digestive, urogenital, and cardiovascular systems, ultrasound technology is essential for assessing physiological data and tissue morphology. Its capacity to deliver real‐time, high‐frequency scans makes it a handy and non‐invasive diagnostic tool. However, issues like patient movements and probe jitter from human error can provide a large amount of interference, resulting in inaccurate test findings. Techniques for image registration can assist in locating and eliminating unwanted interference while maintaining crucial data. Even though there has been research on improving these techniques in Matlab, there are no specialized systems for interference removal, and the procedure is still time‐consuming, particularly when working with huge quantities of ultrasound images. The phase correlation technique, which converts images into the frequency domain and makes noise suppression easier, is one of the most efficient algorithms now in use since it can tolerate noise with resilience. Nevertheless, little research has been done on using this technique to identify displacement in blood vessel wall ultrasound images. To address these gaps, this work presents an image registration system that uses the phase correlation algorithm. The system provides rotation, zoom registration, picture translation, and displacement detection of the vessel wall in addition to interference removal. Furthermore, batch processing is included to increase the effectiveness of registering multiple ultrasound pictures. Through efficient interference management and streamlined registration, this method offers a workable way to improve the precision and efficacy of ultrasonic diagnostics.

DATRA-MIV: Decoder-Adaptive Tiling and Rate Allocation for MPEG Immersive Video

The emerging immersive video coding standard moving picture experts group (MPEG) immersive video (MIV), which is ongoing standardization by MPEG-Immersive (MPEG-I) group, enables six degrees of freedom in a virtual reality environment that represents both natural and computer-generated scenes using multi-view video compression. The MIV eliminates the redundancy between multi-view videos and merges the residuals into multiple pictures, called an atlas. Thus, bitstreams with encoded atlases are generated and corresponding number of decoders are needed, which is challenging for the lightweight device with a single decoder. This article proposes a decoder-adaptive tiling and rate allocation method for MIV to overcome the challenge. First, the proposed method divides atlases into subpictures considering two aspects: (i) subpicture bitstream extracting and merging into one bitstream to use a single decoder and (ii) separation of each source view from the atlases for rate allocation. Second, the atlases are encoded by versatile video coding (VVC), using an extractable subpicture to divide the atlases into subpictures. Third, each subpicture bitstream is extracted, and asymmetric quality allocation for each subpictures is conducted by considering the residuals in the subpicture. Fourth, mixed-quality subpictures were merged by using the proposed bitstream merger. Fifth, the merged bitstream is decoded by using a single decoder. Finally, the viewing area of the user is synthesized by using the reconstructed atlases. Experimental results with the VVC test model (VTM) show that the proposed method achieves a 21.37% Bjøntegaard delta rate saving for immersive video peak signal-to-noise ratio and a 26.76% decoding runtime saving compared to the VTM anchor configuration. Moreover, it supports bitstreams for multiple decoders and single decoder without re-encoding, transcoding, or a substantial increase of the server-side storage.

Multi-view Image Fusion Using Ensemble Deep Learning Algorithm For MRI And CT Images

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble Deep Learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing–based k -Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. Images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using Deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when compared to other existing algorithms.

Inflammation—A Possible Link between Myocarditis and Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy is a primary genetic disease caused by mutations in the desmosome genes. Ever since the introduction of new imaging techniques, like cardiovascular magnetic resonance, the diagnosis of arrhythmogenic cardiomyopathy has become more challenging as left ventricular or biventricular variants may have resemblance to other cardiomyopathies or myocarditis. Not only this but they may also share an acute phase, which might cause even more confusion and misdiagnoses and influence the prognosis and outcome. In this case report, we present a 31-year-old patient with multiple clinical pictures: his symptoms were acute chest pain, new onset of heart failure and arrhythmia symptoms, which determined a dynamic change in clinical diagnosis and management, ultimately taking into consideration arrhythmogenic cardiomyopathy. Through the article, we try to uncover and explain common pathophysiological pathways shared by arrhythmogenic cardiomyopathy and other clinical entities with a special focus on inflammation. The final question remains: “If there is more than one heart disorder that eventually leads to the same clinical image, one may wonder, is arrhythmogenic cardiomyopathy a syndrome rather than a specific condition?”.

Enhancing medical image analysis: A fusion of fully connected neural network classifier with CNN-VIT for improved retinal disease detection

Diseases of the retina continue to be a leading cause of blindness and visual impairment around the world. In the field of medical image analysis, specifically retinal disease identification, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have showed remarkable potential. In this paper, we present a unique method for detecting retinal diseases by combining the advantages of the Inception-V3, ResNet-50, and Vision Transformer architectures into a single model called a Cascade CNN-ViT. The suggested Cascade CNN-ViT model extracts local features from retinal pictures by leveraging the spatial hierarchy learning capabilities of Inception-V3 and ResNet-50. The Vision Transformer takes these regional characteristics and uses self-attention mechanisms to pick up global context information and long-range interdependence. The model successfully combines fine-grained local information with semantically significant global contextual cues by merging the output representations from the CNNs and Vision Transformer. undertaking comprehensive experiments on a large and varied dataset of multimodal retinal pictures to evaluate the performance of the proposed technique. Cascade CNN-ViT model outperforms standalone CNNs and Vision Transformers, as shown by the experimental findings. The model is also resilient across all classes of retinal diseases and is able to successfully deal with the complications introduced by using multiple picture types. Overall, the power of cascading Inception-V3, ResNet-50, and Vision Transformer topologies for improved retinal illness diagnosis has been demonstrated. Potentially improving the management of retinal illnesses and preserving visual health, the proposed approach could have important consequences for early detection and timely intervention.

Reduced Implicit but not Explicit Knowledge of Cross-Situational Statistical Learning in Developmental Dyslexia.

Although statistical learning (SL) has been studied extensively in developmental dyslexia (DD), less attention has been paid to other fundamental challenges in language acquisition, such as cross-situational word learning. Such investigation is important for determining whether and how SL processes are affected in DD at the word level. In this study, typically developed (TD) adults and young adults with DD were exposed to a set of trials that contained multiple spoken words and multiple pictures of individual objects, with no information about word-referent correspondences provided within a trial. Nonetheless, cross-trial statistical relations could be exploited to learn word-referent mappings. The degree of within-trial reference uncertainty and the novelty of to-be-learned objects (novel or familiar) were varied under different learning conditions. The results show that across all conditions, young adults with DD were significantly impaired in their ability to exploit cross-trial regularities in co-occurring visual-auditory streams to discover word-referent mappings. Observed impairments were most pronounced when within-trial reference uncertainty was the highest. Subjective measures of knowledge awareness revealed greater development of implicit but not explicit knowledge in the TD group than in the DD group. Together, these findings suggest that the SL deficit in DD affects fundamental language learning challenges at the word level and points to greater reliance on explicit processes due to impaired implicit associative learning among individuals with DD. Such a deficit is likely to influence spoken language acquisition, and in turn affect literacy skills, in people with DD.

장애인을 그린 우수 그림책 특성 검토

Objectives The purpose of the study is to re-examine the selection criteria for outstanding picture books about people with disabilities, which were suggested by the previous studies, and make suggestions for a revision.
 Methods The multiple award-winning picture book, I talk like a river was selected for the study and was analyzed. To analyze this picture book, the researchers made codes based on previous studies that examined characteristics of outstanding books about people with disabilities. After the analysis, the researchers discussed what could be added to the previous studies’ findings.
 Results This study found that, as suggested in the previous studies, In I talk like a river, the main character, who is a boy with a disability, described his daily life from his point of view. His story was described with text and illustrations, which drew readers’ empathy for the main character. However, unlike the previous studies’ findings, I talk like a river did not show how people without disabilities change their perspectives on people with disabilities. In addition, it showed a person with a disability changed his perception of his disability regardless of other people who do not have disabilities changed their perceptions or attitudes towards people with disabilities.
 Conclusions This study shows that even though a book does not show people without disabilities accept people with disabilities as one of them, it can be considered an outstanding book about people with disabilities. Such a characteristic we found in this study could be added to the characteristics of outstanding picture books about people with disabilities and considered when selecting books.

Multiple motion picture recording in light-in-flight recording by holography with an angular multiplexing technique.

Light-in-flight recording by holography (LIF holography) is an ultrafast imaging technique for recording light pulse propagation as a motion picture. In this study, we propose and demonstrate multiple motion picture recordings of light pulse propagation by use of LIF holography with angular multiplexing. We set incident angles of reference light pulses to remove the difficulty in adjusting the optical path length difference between an object light pulse and reference light pulses and the complexity of the optical system. In the experiment, by using LIF holography with angular multiplexing, we succeeded in recording a propagating light pulse as two motion pictures with durations of 129.6ps without an inseparable superimposition of the reconstructed images. In addition, cross talk between the recorded images, noise caused by cross-terms in an image plane, and the number of motion pictures that can be recorded are discussed.

タイポグリセミアを用いたMulti-model CAPTCHAの提案と評価

We propose a Multi-model CAPTCHA that is composed of one sentence with typo and multiple pictures. This CAPTCHA utilizes human's three abilities (inferring the meaning of the sentence with typo, understanding the meaning of pictures, and linking the meaning of the sentence and corresponding picture), and it is based on a hypothesis, "the longer of the sentence with typo, the time could be longer for a machine to link the meaning of the sentence and corresponding picture (= the shorter of the time of linking, the higher of the probability of human)." We found three findings from our experiment: (1) Examinees were able to link the meaning of the sentence to corresponding picture even there were some pictures with similar composition, (2) Typoglycemia is likely to be appeared on a sentence constructed by short and familiar words, (3) The time for linking was not exponentially increased even the length of the sentence was getting long (= It was not getting difficult for users in this situation).

Recent Advances in Imaging of Light Propagation with Light-in-Flight Recording by Holography

In the last few decades, there have been several advances in ultrafast imaging of light propagation with light-in-flight recording by holography (LIF holography), which can capture light propagation as a motion picture with a single shot in principle. Here, we review the recent advances in LIF holography by considering the perspectives of various development of functional imaging techniques and evaluation of LIF holography with numerical simulation methods. The methods for recording multiple motion pictures such as a space-division multiplexing, a pixel-by-pixel-based space-division multiplexing, and an angular multiplexing technique are added extend the capability of LIF holography. The numerical simulation models used for investigating the image characteristics of LIF hologram are discussed. Finally, a summary and conclusion of recent advances in LIF holography is presented.

Новые подходы в постановке и решениях проблемы сознания

Background. In scientific and theoretical terms, the theme of consciousness as a subject of psychological research is regularly updated. Since psychology established its scientific status, at each historical transition from one economic structure to another, general scientific knowledge has been updated. At the same time, the central issue of consciousness is being permanently changed: from methodological issues (introspection) to structural-functional and objectified knowledge (analytical philosophy of consciousness, cognitive science, neuroscience). In this process, the so-called “hard problem of consciousness” comes to the fore today (D. Chalmers, А. Revonsuo). Objective. The article seeks to outline the necessity for and explain new methodological approaches to the formulation of and conceptual solution to the problem of consciousness (G. Akopov), moving from the contexts of interdisciplinary discourse to psychological discourse. Methods. On the basis of previous author's reviews and research (G. Akopov) on the problems of consciousness, a certain range of philosophical, neuroscientific and psychological definitions and theories of consciousness is presented. The collected material, but for all its limitations in comparison with the hard-to-observe set of ideas, approaches and concepts that exist today in modern English-language literature on the problems of consciousness, allows us to identify a number of key points in an excessively multiple picture, more or less logically compatible. The typological approach we proposed in 2015 (an expanded version in 2021) allows us to streamline the entire set of ideas about consciousness. In accordance with the typology on the identification criterion, the concept of categories and categorical spaces of consciousness in various dimensions is specified. Results. A new methodology in the formulation and solution of the problem of consciousness has been formalized: categorical and typological approaches. The possibility of constructing categorical systems of various dimensions based on the typological identification of consciousness is demonstrated. The communicative nature of consciousness, implicitly represented in certain definitions of consciousness, is shown on the material of a number of existing approaches to identifying consciousness. Conclusion. Typological and categorical approaches make it possible to streamline and systematize the existing set of statements and solutions to the problem of consciousness. The relevant system of categorical spaces of various dimensions defines new opportunities for promising research, amplified by the thesis about the existence of non-verbal languages of consciousness in the socio-communicative, interpersonal and intrapersonal communicative dimensions. The new methodology, coupled with instrumental developments, makes it possible to pose and solve the topical problem of diversity and unity in human consciousness.

Randomized controlled trial of motion-assisted exposure therapy for posttraumatic stress disorder after mild traumatic brain injury, with and without an eye movement task

Background and Purpose: PTSD and mTBI are persistent and frequently comorbid after combat, yet current therapies often achieve only modest impact. A novel exposure-based “walk and talk” cognitive therapy, Motion-Assisted, Multi-Modal Memory Desensitization and Reconsolidation (3MDR), featuring participant-selected music and pictures and an eye movement (EM) task in an immersive virtual environment, has shown efficacy in treatment-resistant male veterans, but has not been studied in women or after TBI. The EM task is adapted from eye movement desensitization and reprocessing (EMDR) therapy, but dismantling studies of EMDR have questioned EM benefit. This pilot study assesses 3MDR in male and female veterans with comorbid PTSD and mTBI, and the impact of EM on response. We hypothesized that 3MDR would prove efficacious, both with (EM+) and without EM (EM-).Design: Participants with probable PTSD (PCL-5 ≥ 34) and mTBI were randomized to EM + or EM-across 10 sessions. Participants provided songs and pictures that they rated on impactfulness. While walking in the 3MDR virtual environment, participants started with a song to bring them back to the time of their trauma, and then traversed two hallways, actively walking toward emotionally evocative pictures that they then discussed with their therapist. Key words or feelings they expressed were superimposed over the picture, then read aloud, whereupon EM + participants recited numbers flashing on a ball crisscrossing the picture. These procedures were repeated for multiple pictures per session. A song to return the participant to present day closed each session. Change in PCL-5 score from pre-to post-intervention was the primary outcome, with additional measures at 3 and 6 months.Results: Sixteen (80%) of 20 participants completed the intervention (8 EM+, 8 EM-); 9 (6 EM+, 3 EM-) had resolution of PTSD diagnosis and two improved significantly without resolution. Average PCL-5 score declined from 52.0 (95% confidence intervals: 46.3, 57.7) at baseline to 33.6 (24.3, 42.9) post-intervention (p < 0.01). The EM + group achieved statistically significant improvement (p = 0.01) while the EM-did not (p = 0.10).Conclusion: For veterans with PTSD and comorbid mTBI, 3MDR is effective, and the EM component may add value. Confirmation with larger studies is important.

One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks

Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector’s optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN.

Vision-Based Autonomous Landing on Unprepared Field With Rugged Surface

Landing on unprepared terrains is a challenging task for fully autonomous micro unmanned aerial vehicles (UAVs). Most of the existing methods mainly rely on manual control when the terrain of the target area is unknown in advance. In this article, we propose an autonomous landing method based on a learning-based multi-view stereo (MVS) system. UAV acquires multiple RGB pictures of the terrain after cruise, and then extracts speed-up robust features (SURF) and perform structure from motion (SFM) to obtain sparse feature point clouds. By utilizing the generated initial depth map, we further propose a novel 3-D reconstruction algorithm named PatchmatchNet-A, which can help to obtain precise and stable estimates of the depth information. In PatchmatchNet-A, we also use a new activation function, adjustable-arctangent linear units (ALU), to improve the accuracy and robustness. We have tested our algorithms in a DTU dataset and found that it can speed up the processing by around 25% while guaranteeing competitive performance. Flight experiments have also been conducted to verify the effectiveness of the whole landing system by using a commercial M210V2 quadcopter.

Visualization of Digital Genres: Instagraming in English Political Communication

The article depicts the emergence of a new digital hybrid genre –instagraming.The research objective is to analyze the design of the genre Instagram at the text level and to determine the main linguistic and non-linguistic features of the genre design in the field of English political Internet-communication. Research methods and techniques.The material of the research includes 144 English publications obtained from the official accounts of Joe Biden and Boris Johnson. The qualitative and quantitative methods are used in the research namely: the methods of information analysis, synthesis, comparison, linguistic description,content analysis, and the semantic-syntactic method. The Voyant Tools program is used for data analysis. The content analysis of publications of the political leaders of the USA and UK on Instagram shows the difference in the number of their posts which indicates different attitudes of the politicians and their teams in the use of the social network when achieving their political goals. The genrestructure depends on the development of information technology, as well as on the type of device into which it is entered and which affects the order of heading placement, the format of the page, as well as the position of a text comment in relation to a video or photo. Results and discussion. The results obtained testify that both verbal and nonverbal constituencies of instagraming present inseparable parts of this genre. It is possible to speak about the hybridization of the digital genre as an inseparable feature. Conclusions. One can state that instagraming might be considered as a sort of container of multiple texts, combining video, audio and picture semiotic systems together. Additionally instagraming is rendered as a Goroshko, O., Poliakova, T. (2022). Visualization of Digital Genres:Instagraming in English Political Communication.Social Communications: Theory and Practice,Vol.14(1),148–163.DOI: 10.51423/2524-0471-2022-14-1-9149eISSN 2522-9125 pISSN 252—0471https://new.comteka.com.ua/subgenre providing communicativespace for various genres of political communication on the Net. One can argue that social media genre research requires the combination of linguistic and paralinguistic methods of analysis that develops a methodological base for linguistics 2.0 –a new approach in linguistic and communication studies of the ХХІ century.Keywords:digital genre, genre 2.0, Instagram, instagraming, social media, visualization, English-speaking political communication.

Anti-phishing technique based on dynamic image captcha using multi secret sharing scheme

With the cutting-edge improvement of web, online abuses have been increasing rapidly. Phishing is the most widely recognized abuses performed by digital crooks nowadays. It is an activity to steal private data (for example, client names, passwords and Visa data) in an electronic correspondence. It is a sort of fraud with the end goal of monetary benefit and other fake exercises. It utilizes phony websites that resemble genuine ones. Phishing messages might contain links to sites that are contaminated with malware. In this paper, “an anti-phishing approach using multi secret sharing scheme” is implemented as an answer to this problem. Here, Dynamic Image CAPTCHA based verification using multi secret sharing is performed. Image CAPTCHA is divided into two pieces called shares. Multiple secret pictures are revealed by overlapping the same set of shares at different angles. In the proposed approach, shares are of different modes i.e., user’s share is imprinted on a physical transparency while server’s share is in digital mode. By using the proposed approach, websites and end clients can cross confirm their identity.

Is there an emotionality effect in older adults’ source memory?

ABSTRACT The goal of our research was to investigate whether older adults show a source memory enhancement for emotionally valenced sources. Additionally, building on research on the socioemotional selectivity theory and the age-related positivity effect, we tested whether older adults show a larger enhancement for positive compared to negative (and neutral) sources than younger adults. In Experiment 1 (nold = 25, nyoung = 27), we used one positive, one negative, and one neutral picture to manipulate source valence (many-to-one mapping of items to sources), whereas, in Experiment 2 (nold = 62, nyoung = 62), we used multiple pictures per source valence category (one-to-one mapping of items to sources) to counteract potential habituation effects. In both experiments, sources had medium and matching arousal levels. Items were neutral words superimposed on the source pictures. To support an implicit, natural information processing, participants rated the words in terms of pleasantness. We analyzed memory data with a multinomial processing tree model to disentangle memory processes from guessing bias. Across both experiments, an age-related positivity effect occurred in participants’ pleasantness ratings. This effect, however, did not carry over to older adults’ source memory. That is, in source memory, we found a general emotionality effect for younger but not for older adults and no age-related positivity effect. We propose that due to older adults’ pronounced difficulties in remembering the item-to-source link (i.e., associative deficit), even a greater focus on an inherently emotional source might be insufficient to boost source memory.

Image two-classification algorithm based on continuous-time quantum walk

To further improve the performance of image retrieval, we propose an image two-classification algorithm based on continuous-time quantum walk (CTQW) model. First, a weighted undirected complete graph is constructed based on a new weighted edge method, that is, the images in the image set are used to represent the nodes on the weighted complete graph, and every two images are connected to represent the edge on the weighted complete image, the weighted value of which represents the similarity between adjacent images. Then, the CTQW is executed to realize the image two-classification based on the quantum walk model of the weighted complete graph, that is, the CTQW is performed from two types of unrelated images, and the two-classification of images is realized that the relevant and unrelated labels are assigned to each image according to the obtained probability distribution two-classification. Finally, the experimental results show that the proposed two-classification algorithm based on CTQW has a good classification effect from the visual effect, and the evaluation indexes of image classification, such as the average precision rate, the recall rate, and the accuracy rate, are all higher than 0.85. In addition, the proposed image two-classification algorithm based on CTQW can be extended to image multiclassification if the quantum walk is performed using multiple irrelevant pictures as the starting point. Similarly, the extended image multiclassification method also has a better classification effect through the experiments and the evaluation indexes of image classification.

Single inclusive jet production in pA collisions at NLO in the small-x regime

We present the first complete next-to-leading-order (NLO) prediction with full jet algorithm implementation for the single inclusive jet production in pA collisions at forward rapidities within the color glass condensate (CGC) effective theory. Our prediction is fully differential over the final state physical kinematics, which allows the implementation of any infra-red safe observable including the jet clustering procedure. The NLO calculation is organized with the aid of the observable originated power counting proposed in [1] which gives rise to the novel soft contributions in the CGC factorization. We achieve the fully-differential calculation by constructing suitable subtraction terms to handle the singularities in the real corrections. The subtraction contributions can be exactly integrated analytically. We present the NLO cross section with the jets constructed using the anti-kT algorithm. The NLO calculation demonstrates explicitly the validity of the CGC factorization in jet production. Furthermore, as a byproduct of the subtraction method, we also derive the fully analytic cross section for the forward jet production in the small-R limit. We show that in the small-R approximation, the forward jet cross section can be factorized into a semi-hard cross section that produces a parton and the semi-inclusive jet functions (siJFs). We argue that this feature holds for generic jet production and jet substructure observables in the CGC framework. Last, we show numerical analyses of the derived formula to validate our calculations. We justify when the small-R approximation is appropriate. Like forward hadron production, the obtained NLO result also exhibits the negativity of the cross section in the large jet transverse regime, which signals the need for the threshold resummation. A sketch of the threshold resummation in the CGC framework is presented based on the multiple emission picture and it is found to agree with the approach using the rapidity renormalization group equation developed in [2].

Gain compensation across LIDAR scans

High-end Terrestrial Lidar Scanners are often equipped with RGB cameras that are used to colorize the point samples. Some of these scanners produce panoramic HDR images by encompassing the information of multiple pictures with different exposures. Unfortunately, exported RGB color values are not in an absolute color space, and thus point samples with similar reflectivity values might exhibit strong color differences depending on the scan the sample comes from. These color differences produce severe visual artifacts if, as usual, multiple point clouds colorized independently are combined into a single point cloud. In this paper we propose an automatic algorithm to minimize color differences among a collection of registered scans. The basic idea is to find correspondences between pairs of scans, i.e. surface patches that have been captured by both scans. If the patches meet certain requirements, their colors should match in both scans. We build a graph from such pair-wise correspondences, and solve for the gain compensation factors that better uniformize color across scans. The resulting panoramas can be used to colorize the point clouds consistently. We discuss the characterization of good candidate matches, and how to find such correspondences directly on the panorama images instead of in 3D space. We have tested this approach to uniformize color across scans acquired with a Leica RTC360 scanner, with very good results.