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  • Learning an end-to-end spatial grasp generation and refinement algorithm from simulation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-20
    Peiyuan Ni, Wenguang Zhang, Xiaoxiao Zhu, Qixin Cao

    Novel object grasping is an important technology for robot manipulation in unstructured environments. For most of current works, a grasp sampling process is required to obtain grasp candidates, combined with a local feature extractor using deep learning. However, this pipeline is time–cost, especially when grasp points are sparse such as at the edge of a bowl. To tackle this problem, our algorithm

    更新日期:2020-10-20
  • Snapshot hyperspectral imaging using wide dilation networks
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-16
    Mikko E. Toivonen, Chang Rajani, Arto Klami

    Hyperspectral (HS) cameras record the spectrum at multiple wavelengths for each pixel in an image, and are used, e.g., for quality control and agricultural remote sensing. We introduce a fast, cost-efficient and mobile method of taking HS images using a regular digital camera equipped with a passive diffraction grating filter, using machine learning for constructing the HS image. The grating distorts

    更新日期:2020-10-17
  • Crowd flow estimation from calibrated cameras
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-15
    Igor Almeida, Claudio Jung

    Many crowd analysis methods rely on optical flow techniques to estimate the main moving directions. In this work, we propose a crowd flow filtering approach for calibrated cameras that can be coupled to any generic optical flow method. It projects the input optical flow to the world coordinate system, performs a local motion analysis exploring a Social Forces Model and then projects the filtered flow

    更新日期:2020-10-16
  • Feature-Driven Viewpoint Placement for Model-Based Surface Inspection
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-15
    Dennis Mosbach, Petra Gospodnetić, Markus Rauhut, Bernd Hamann, Hans Hagen

    The goal of visual surface inspection is to analyze an object’s surface and detect defects by looking at it from different angles. Developments over the past years have made it possible to partially automate this process. Inspection systems use robots to move cameras and obtain pictures that are evaluated by image processing algorithms. Setting up these systems or adapting them to new models is primarily

    更新日期:2020-10-16
  • A generalizable approach for multi-view 3D human pose regression
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-08
    Abdolrahim Kadkhodamohammadi, Nicolas Padoy

    Despite the significant improvement in the performance of monocular pose estimation approaches and their ability to generalize to unseen environments, multi-view approaches are often lagging behind in terms of accuracy and are specific to certain datasets. This is mainly due to the fact that (1) contrary to real-world single-view datasets, multi-view datasets are often captured in controlled environments

    更新日期:2020-10-11
  • A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-08
    Navid Nikbakhsh, Yasser Baleghi, Hamzeh Agahi

    Plant leaf segmentation has a very important role in most plant identification methods. Tree leaves segmentation in images with complex background is very difficult when there is no prior information about the leaves and backgrounds. In practice, the parameters of unsupervised image segmentation algorithms must be set for each image to get the best results. In this paper, to overcome this problem,

    更新日期:2020-10-11
  • RSRGAN: computationally efficient real-world single image super-resolution using generative adversarial network
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-08
    Vishal Chudasama, Kishor Upla

    Recently, convolutional neural network has been employed to obtain better performance in single image super-resolution task. Most of these models are trained and evaluated on synthetic datasets in which low-resolution images are synthesized with known bicubic degradation and hence they perform poorly on real-world images. However, by stacking more convolution layers, the super-resolution (SR) performance

    更新日期:2020-10-08
  • A system for the generation of in-car human body pose datasets
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-08
    João Borges, Sandro Queirós, Bruno Oliveira, Helena Torres, Nelson Rodrigues, Victor Coelho, Johannes Pallauf, José Henrique Brito, José Mendes, Jaime C. Fonseca

    With the advent of autonomous vehicles, detection of the occupants’ posture is crucial to tackle the needs of infotainment interaction or passive safety systems. Generative approaches have been recently proposed for human body pose in-car detection, but this type of approaches requires a large training dataset for a feasible accuracy. This requirement poses a difficulty, given the substantial time

    更新日期:2020-10-08
  • Automatic camera calibration by landmarks on rigid objects
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-06
    Vojtěch Bartl, Jakub Špaňhel, Petr Dobeš, Roman Juránek, Adam Herout

    This article presents a new method for automatic calibration of surveillance cameras. We are dealing with traffic surveillance, and therefore, the camera is calibrated by observing vehicles; however, other rigid objects can be used instead. The proposed method is using keypoints or landmarks automatically detected on the observed objects by a convolutional neural network. By using fine-grained recognition

    更新日期:2020-10-07
  • Feature-transfer network and local background suppression for microaneurysm detection
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-04
    Xinpeng Zhang, Jigang Wu, Min Meng, Yifei Sun, Weijun Sun

    Microaneurysm (MA) is the earliest lesion of diabetic retinopathy (DR). Accurate detection of MA is helpful for the early diagnosis of DR. In this paper, an efficient approach is proposed to detect MA, based on feature-transfer network and local background suppression. In order to reduce noise, a feature-distance-based algorithm is proposed to suppress local background. The similarity matrix of feature

    更新日期:2020-10-05
  • Rapid self-localization of robot based on omnidirectional vision technology
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-03
    Tsorng-Lin Chia, Shu-Yin Chiang, Chaur-Heh Hsieh

    In this paper, we propose a self-localization method for a soccer robot using an omnidirectional camera. Based on the projective geometry of the omnidirectional visual system, the image distortion from the original omnidirectional image can be completely corrected, so the robot can quickly localize itself on the playing field. First, we transform the distorted omnidirectional image to a distortion-free

    更新日期:2020-10-04
  • Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-25
    Thiago Rateke, Aldo von Wangenheim

    One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle’s behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle

    更新日期:2020-09-25
  • Enhancing feature fusion for human pose estimation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-24
    Rui Wang, Jiangwei Tong, Xiangyang Wang

    Current human pose estimation methods mainly rely on designing efficient Convolutional Neural Networks (CNN) frameworks. These CNN architectures typically consist of high-to-low resolution sub-networks to learn semantic information, and then followed by low-to-high sub-networks to raise the resolution to locate the keypoints. Because low-level features have high resolution but less semantic information

    更新日期:2020-09-24
  • Real-time and accurate abnormal behavior detection in videos
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-24
    Zheyi Fan, Jianyuan Yin, Yu Song, Zhiwen Liu

    Abnormal crowd behavior detection is a hot research topic in the field of computer vision. In order to solve the problems of high computational cost and the imbalance between positive and negative samples, we propose an efficient algorithm that can detect and locate anomalies in videos. In order to solve the problem of less negative samples, the algorithm uses the spatiotemporal autoencoder to identify

    更新日期:2020-09-24
  • A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-24
    Luan Casagrande, Luiz Antonio Buschetto Macarini, Daniel Bitencourt, Antônio Augusto Fröhlich, Gustavo Medeiros de Araujo

    We propose a combination of image processing methods to detect ceramic tiles defects automatically. The primary goal is to identify faults in ceramic tiles, with or without texture. The process consists of four steps: preprocessing, feature extraction, optimization, and classification. In the second step, gray-level co-occurrence matrix, segmentation-based fractal texture analysis, discrete wavelet

    更新日期:2020-09-24
  • Autocalibration method for scanning electron microscope using affine camera model
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-18
    Andrey V. Kudryavtsev, Valérian Guelpa, Patrick Rougeot, Olivier Lehmann, Sounkalo Dembélé, Peter Sturm, Nadine Le Fort-Piat

    This paper deals with the task of autocalibration of scanning electron microscope (SEM), which is a technique allowing to compute camera motion and intrinsic parameters. In contrast to classical calibration, which implies the use of a calibration object and is known to be a tedious and rigid operation, auto- or selfcalibration is performed directly on the images acquired for the visual task. As autocalibration

    更新日期:2020-09-20
  • Impurities detection in edible bird’s nest using optical segmentation and image fusion
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-16
    Cong Kai Yee; Ying Heng Yeo; Lai Hoong Cheng; Kin Sam Yen

    The cleanliness of edible bird’s nest (EBN) is among the determinative factors for market acceptance. As it is meant for human consumption, EBN should be free of any impurities or matter which are foreign to it, such as bird feathers, egg fragments and droppings. However, natural variations in composition, density and thickness impose inconsistency to the level of translucency and colour of EBN, resulting

    更新日期:2020-09-16
  • Benchmarking deep network architectures for ethnicity recognition using a new large face dataset
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-14
    Antonio Greco; Gennaro Percannella; Mario Vento; Vincenzo Vigilante

    Although in recent years we have witnessed an explosion of the scientific research in the recognition of facial soft biometrics such as gender, age and expression with deep neural networks, the recognition of ethnicity has not received the same attention from the scientific community. The growth of this field is hindered by two related factors: on the one hand, the absence of a dataset sufficiently

    更新日期:2020-09-14
  • Deep understanding of shopper behaviours and interactions using RGB-D vision
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-13
    Marina Paolanti; Rocco Pietrini; Adriano Mancini; Emanuele Frontoni; Primo Zingaretti

    In retail environments, understanding how shoppers move about in a store’s spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers’ movements, mean that accurately measuring shopper

    更新日期:2020-09-13
  • A study of deep learning approaches for classification and detection chromosomes in metaphase images
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-11
    Maria F. S. Andrade; Lucas V. Dias; Valmir Macario; Fabiana F. Lima; Suy F. Hwang; Júlio C. G. Silva; Filipe R. Cordeiro

    Chromosome analysis is an important approach to detecting genetic diseases. However, the process of identifying chromosomes in metaphase images can be challenging and time-consuming. Therefore, it is important to use automatic methods for detecting chromosomes to aid diagnosis. This work proposes a study of deep learning approaches for classification and detection of chromosome in metaphase images

    更新日期:2020-09-11
  • A real-time and precise ellipse detector via edge screening and aggregation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-10
    Zhenyu Liu; Xia Liu; Guifang Duan; Jianrong Tan

    A fast and precise method for ellipse detection is proposed in this paper. The method aims at clearly removing the lines and curves which are not ellipse edges to improve the ellipse fitting. In arc extraction, the arcs are divided into four categories according to the gradient, and the size constraint is exploited to remove the interference lines. Then, the arc relative position constraints and the

    更新日期:2020-09-10
  • Deep learning based breast cancer detection and classification using fuzzy merging techniques
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-09
    R. Krithiga; P. Geetha

    Automatic identification of abnormal and normal cells is a critical step in computer-assisted pathology, owing to certain heterogeneous characteristics of cancer cells. However, automated nuclei detection is problematic in unevenly shaped, overlapping and touching nuclei. It is, consequently, essential to detect single and overlapping nuclei and distinguish them from single ones for a reasonable quantitative

    更新日期:2020-09-09
  • Detection of 3D bounding boxes of vehicles using perspective transformation for accurate speed measurement
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-04
    Viktor Kocur; Milan Ftáčnik

    Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation

    更新日期:2020-09-04
  • Finding hard faces with better proposals and classifier
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-03
    Xiaoxing Zeng; Xiaojiang Peng; Yali Wang; Yu Qiao

    Recent studies witnessed that deep CNNs significantly improve the performance of face detection in the wild. However, detecting faces with small scales, large pose variations, and occlusions is still challenging. In this paper, to detect challenging faces, we present a boosted faster RCNN (F-RCN) version with an enhanced region proposal network (eRPN) module and newly introduced hard example mining

    更新日期:2020-09-03
  • A neural network ensemble method for effective crack segmentation using fully convolutional networks and multi-scale structured forests
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-09-02
    Sen Wang; Xing Wu; Yinghui Zhang; Xiaoqin Liu; Lun Zhao

    Crack image segmentation has recently become a major research topic in nondestructive inspection. However, the image segmentation methods are not robust to variations such as illumination, weather, noise and the segmentation accuracy which cannot meet the requirements of practical applications. Therefore, a neural network ensemble method is proposed for effective crack segmentation in this paper, which

    更新日期:2020-09-02
  • Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-08-31
    Mohammad Reza Mohebbian; Javad Rasti

    Eye-gaze tracking through camera is commonly used in a number of areas, such as computer user interface systems, sports science, psychology, and biometrics. The robustness of the head and camera rotation tracking algorithm has been a critical problem in recent years. In this paper, Haar-like features and a modified version of the group method of data handling, as well as segmented regression, are used

    更新日期:2020-08-31
  • GraspFusionNet: a two-stage multi-parameter grasp detection network based on RGB–XYZ fusion in dense clutter
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-08-20
    Weiming Wang; Wenhai Liu; Jie Hu; Yi Fang; Quanquan Shao; Jin Qi

    Robotic grasping of diverse range of novel objects is a great challenge in dense clutter, which is also critical to many applications. However, current methods are vulnerable to perception uncertainty for dense stacked objects, resulting in limited accuracy of multi-parameter grasp prediction. In this paper, we propose a two-stage grasp detection pipeline including sampling and predicting stages. The

    更新日期:2020-08-20
  • An efficient, dense and long-range marker system for the guidance of the visually impaired
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-08-18
    Juan Manuel Sáez; Miguel Angel Lozano; Francisco Escolano; Javier Pita Lozano

    In this paper, we address the problem of making a mobile/smartphone camera sensitive to distant fiducial markers. To this end, we carefully design a novel visual marker that is both dense and readable from large distances. The main novelty of the proposed marker is the combination of a quaternary color-based coding system with robust methods for reading the color patterns included in each frame once

    更新日期:2020-08-18
  • Time-efficient spliced image analysis using higher-order statistics
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-08-07
    Ankit Kumar Jaiswal; Rajeev Srivastava

    Image forgery is gaining huge momentum as changing the content is no longer arduous. One of the leading techniques of this category is image splicing. This technique generates a composite image formed by combining regions of images. Once the image is forged, it becomes nearly impossible for the human expert to substantiate. Hence, for detecting and localizing the spliced region in the forged image

    更新日期:2020-08-07
  • Pedestrian detection using multi-scale squeeze-and-excitation module
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-08-03
    Yongwoo Lee; Hyekyoung Hwang; Jitae Shin; Byung Tae Oh

    Computer vision systems are major research items for autonomous vehicles. However, it is often challenging to understand the road scene, especially when objects are small and overlapping. To address these problems, this paper proposes a deep learning-based pedestrian detection method for small and overlapping objects. The proposed method adopts a parallel feature pyramid network with multi-scale feature

    更新日期:2020-08-03
  • Detection and pose estimation of auto-rickshaws from traffic images
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-30
    Blossom Treesa Bastian; Jiji Charangatt Victor

    In intelligent transport systems, detection and identification of vehicle types enact a substantial role. In this context, this paper addresses the detection and pose classification of a specific vehicle type: auto-rickshaws which have been heavily neglected by the publicly available vehicle datasets, but remains the most commonly used and cheap form of transportation in south Asian countries. Here

    更新日期:2020-07-30
  • Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19.
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-28
    Hanan Farhat,George E Sakr,Rima Kilany

    Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration

    更新日期:2020-07-28
  • OCLU-NET for occlusal classification of 3D dental models
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-25
    Mamta Juneja; Ridhima Singla; Sumindar Kaur Saini; Ravinder Kaur; Divya Bajaj; Prashant Jindal

    With the emergence in modern dentistry, the study of dental occlusion has been a subject of major interest. The aim of the present study is to investigate the capabilities of deep learning for the classification of dental occlusion using 3D images that has an exciting impact in several fields of dental anatomy. In present work, the 3D stereolithography (STL) files depicting the dental structures are

    更新日期:2020-07-25
  • Fast and efficient difference of block means code for palmprint recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-24
    Jumma Almaghtuf; Fouad Khelifi; Ahmed Bouridane

    Over the past two decades, researchers in the field of biometrics have presented a wide variety of coding-based palmprint recognition methods. These approaches mainly rely on extracting the texture features, e.g. line orientations, and phase information, using different filters. In this paper, we propose a new efficient palmprint recognition method based on the Different of Block Means. In the proposed

    更新日期:2020-07-24
  • 3D video semantic segmentation for wildfire smoke
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-21
    Guodong Zhu; Zhenxue Chen; Chengyun Liu; Xuewen Rong; Weikai He

    Wildfires are a serious threat to ecosystems and human life. Usually, smoke is generated before the flame, and due to the diffusing nature of the smoke, we can detect smoke from a distance, so wildfire smoke detection is especially important for early warning systems. In this paper, we propose a 3D convolution-based encoder–decoder network architecture for video semantic segmentation in wildfire smoke

    更新日期:2020-07-21
  • Semi-supervised learning using adversarial training with good and bad samples
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-19
    Wenyuan Li; Zichen Wang; Yuguang Yue; Jiayun Li; William Speier; Mingyuan Zhou; Corey Arnold

    In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial networks (GANs) can be used for multiple purposes in SSL . Triple-GAN, which aims to jointly optimize model components by incorporating three players, generates suitable image-label pairs to compensate for the lack of labeled

    更新日期:2020-07-19
  • ARF-Crack: rotation invariant deep fully convolutional network for pixel-level crack detection
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-18
    Fu-Chen Chen; Mohammad R. Jahanshahi

    Autonomous detection of structural defect from images is a promising, but also challenging task to replace manual inspection. With the development of deep learning algorithms, several studies have adopted deep convolutional neural networks (CNN) or fully convolutional networks (FCN) to detect cracks in pixel-level. However, a fundamental property of cracks, that they are rotation invariant, has never

    更新日期:2020-07-18
  • An efficient and globally optimal method for camera pose estimation using line features
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-18
    Qida Yu; Guili Xu; Yuehua Cheng

    The accurate estimation of camera pose using numerous line correspondences in real time is a challenging task. This paper presents a non-iterative approach to solve the Perspective-n-Line (PnL) problem. The method can provide high speed and global optimality, as well as linear complexity. A nonlinear least squares (non-LLS) objective function is first formulated by parameterizing the rotation matrix

    更新日期:2020-07-18
  • A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-16
    Yadavendra; Satish Chand

    In contemporary times, machine learning is being used in almost every field due to its better performance. Here, we consider different machine learning methods such as logistic regression, random forest, support vector classifier (SVC), AdaBoost classifier, bagging classifier, voting classifier, and Xception model to classify the breast cancer tumor and evaluate their performances. We used a standard

    更新日期:2020-07-16
  • Efficient use of recent progresses for Real-time Semantic segmentation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-07-14
    Safae El Houfi; Aicha Majda

    Different approaches were proposed to design deep CNNs for semantic segmentation. Usually, they are built upon an encoder–decoder architecture and require computationally expensive operations on high-resolution activation maps. Since for real-time segmentation the costs are critical, efficient approaches compromise spatial information to achieve real-time segmentation but with a considerable drop in

    更新日期:2020-07-14
  • Multi-source domain adaptation for image classification
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-27
    Morvarid Karimpour; Shiva Noori Saray; Jafar Tahmoresnezhad; Mohammad Pourmahmood Aghababa

    In recent years, domain adaptation and transfer learning are known as promising techniques with admirable performance to deal with problems with distribution difference between the training (source domain) and test (target domain) data. In this paper, a novel unsupervised multi-source transductive transfer learning approach, referred to as multi-source domain adaptation for image classification (MDA)

    更新日期:2020-06-27
  • Root identification in minirhizotron imagery with multiple instance learning
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-24
    Guohao Yu; Alina Zare; Hudanyun Sheng; Roser Matamala; Joel Reyes-Cabrera; Felix B. Fritschi; Thomas E. Juenger

    In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron

    更新日期:2020-06-24
  • Boosting binary masks for multi-domain learning through affine transformations
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-18
    Massimiliano Mancini; Elisa Ricci; Barbara Caputo; Samuel Rota Bulò

    In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain learning is to produce a single model performing a task in all the domains together. Recent works showed how we can address this problem by masking the internal weights of a given original convnet through learned binary variables

    更新日期:2020-06-18
  • WatchNet++: efficient and accurate depth-based network for detecting people attacks and intrusion
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-17
    M. Villamizar; A. Martínez-González; O. Canévet; J.-M. Odobez

    We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not able to distinguish people from other elements in the scene, we propose WatchNet++ that is a depth-based and sequential network that localizes people

    更新日期:2020-06-17
  • Graph-based topic models for trajectory clustering in crowd videos
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-12
    Manal Al Ghamdi; Yoshihiko Gotoh

    Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However, such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of

    更新日期:2020-06-12
  • I-ME: iterative model evolution for learning from weakly labeled images and videos
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-12
    Ozge Yalcinkaya; Eren Golge; Pinar Duygulu

    A significant bottleneck in building large-scale systems for image and video categorization is the requirement of labeled data. Manual labeling effort could be overcome by using the massive amount of web data. However, this type of data is collected through searching on the category names and is likely to inherit noise. In this study, (1) the primary objective is to improve utilizing weakly labeled

    更新日期:2020-06-12
  • GC-NET for classification of glaucoma in the retinal fundus image
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-12
    Mamta Juneja; Niharika Thakur; Sarthak Thakur; Archit Uniyal; Anuj Wani; Prashant Jindal

    Glaucoma is the second-most dominant cause for irreversible blindness, resulting in damage to the optic nerve. Ophthalmologist diagnoses this disease using a retinal examination of the dilated pupil. Since the diagnosis is a manual and laborious procedure, an automated approach for faster diagnosis is desirable. Convolutional neural networks (CNN) could allow automation of the diagnosis procedure due

    更新日期:2020-06-12
  • Two-stream FCNs to balance content and style for style transfer
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-06-08
    Duc Minh Vo; Akihiro Sugimoto

    Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning-based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced

    更新日期:2020-06-08
  • Using CNN with Bayesian optimization to identify cerebral micro-bleeds
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-30
    Piyush Doke; Dhiraj Shrivastava; Chichun Pan; Qinghua Zhou; Yu-Dong Zhang

    This article studies the problem of detecting cerebral micro-bleeds (CMBs) using a convolutional neural network (CNN). Cerebral micro-bleeds (CMBs) are increasingly recognized neuroimaging findings, occurring with cerebrovascular diseases, dementia, and normal aging. Naturally enough, it becomes necessary to detect CMBs in the early stages of life. The focus of this article is to infuse new techniques

    更新日期:2020-05-30
  • Analysis of microtomographic images in automatic defect localization and detection
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-27
    Mariusz Marzec; Piotr Duda; Zygmunt Wróbel

    The paper presents a fast method of fully automatic localization and classification of defects in aluminium castings based on computed microtomography images. In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (\(\mu \)-CT). In addition

    更新日期:2020-05-27
  • DC-Gnet for detection of glaucoma in retinal fundus imaging
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-18
    Mamta Juneja; Sarthak Thakur; Anuj Wani; Archit Uniyal; Niharika Thakur; Prashant Jindal

    Glaucoma is a retinal disease caused due to increased intraocular pressure in the eyes. It is the second most dominant cause of irreversible blindness after cataract, and if this remains undiagnosed, it may become the first common cause. Ophthalmologists use different comprehensive retinal examinations such as ophthalmoscopy, tonometry, perimetry, gonioscopy and pachymetry to diagnose glaucoma. But

    更新日期:2020-05-18
  • Photo-realistic dehazing via contextual generative adversarial networks
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-16
    Shengdong Zhang; Fazhi He; Wenqi Ren

    Single image dehazing is a challenging task due to its ambiguous nature. In this paper we present a new model based on generative adversarial networks (GANs) for single image dehazing, called as dehazing GAN. In contrast to estimating the transmission map and the atmospheric light separately as most existing deep learning methods, dehazing GAN restores the corresponding hazy-free image directly from

    更新日期:2020-05-16
  • Hyper-parameter optimization of deep learning model for prediction of Parkinson’s disease
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-09
    Sukhpal Kaur; Himanshu Aggarwal; Rinkle Rani

    Neurodegenerative disorder such as Parkinson’s disease (PD) is among the severe health problems in our aging society. It is a neural disorder that affects people socially as well as economically. It occurs due to the failure of the brain’s dopamine-producing cells to produce enough dopamine to enable the motor movement of the body. This disease primarily affects vision, speech, movement problems, and

    更新日期:2020-05-09
  • Detection of tomato organs based on convolutional neural network under the overlap and occlusion backgrounds
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-05-06
    Jun Sun; Xiaofei He; Minmin Wu; Xiaohong Wu; Jifeng Shen; Bing Lu

    Traditional detection methods are not sensitive to small-sized tomato organs (flowers and fruits), because the immature green tomatoes are highly similar to the background color. The overlap among fruits and the occlusion of stems and leaves on tomato organs can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a tomato organ recognition

    更新日期:2020-05-06
  • Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-30
    A. Pampouchidou; M. Pediaditis; E. Kazantzaki; S. Sfakianakis; I. A. Apostolaki; K. Argyraki; D. Manousos; F. Meriaudeau; K. Marias; F. Yang; M. Tsiknakis; M. Basta; A. N. Vgontzas; P. Simos

    There is a growing interest in computational approaches permitting accurate detection of nonverbal signs of depression and related symptoms (i.e., anxiety and distress) that may serve as minimally intrusive means of monitoring illness progression. The aim of the present work was to develop a methodology for detecting such signs and to evaluate its generalizability and clinical specificity for detecting

    更新日期:2020-04-30
  • On evolutionary computation techniques for multi-view triangulation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-29
    Nirmal S. Nair; Madhu S. Nair

    Multi-view triangulation is an essential step in recovering three-dimensional structure from a set of images. It is a well-studied problem in computer vision with many suboptimal and optimal methods based on different optimality criteria. In this paper, we assess the ability of evolutionary computation (EC) methods in finding highly accurate solutions to this problem. We use an overlaying Luus–Jaakola

    更新日期:2020-04-29
  • A morphological approach to piecewise constant active contour model incorporated with the geodesic edge term
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-28
    Song Yu; Wu Yiquan

    Traditional level set-based image segmentation method has to solve the level set evolution equation which is the Euler–Lagrange equation of the energy functional defined on the image domain. Solving level set evolution equation is very time-consuming, and reinitialization is usually needed. The level set evolution equation can also be solved by mathematical morphology. The morphological implementation

    更新日期:2020-04-28
  • Parameter selection framework for stereo correspondence
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-27
    Phuc Hong Nguyen; Chang Wook Ahn

    In this paper, we propose a method to select parameter values for stereo matching methods. The proposed method was trained in a supervised manner, and an evolutionary algorithm is used to select optimized parameter values for a given domain and a cost function constructed to measure the goodness level of candidate parameter values. Performance of the proposed method is compared to that of five current

    更新日期:2020-04-27
  • Multi-scale crowd feature detection using vision sensing and statistical mechanics principles
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-21
    Banafshe Arbab-Zavar; Zoheir A. Sabeur

    Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorize various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate

    更新日期:2020-04-21
  • Utilization of a convolutional method for Alzheimer disease diagnosis
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-04-18
    Hanane Allioui; Mohamed Sadgal; Aziz Elfazziki

    With the increasing number of cases as well as care costs, Alzheimer’s disease has gained more interest in several scientific communities especially medical and computer science. Clinical and analytical tests are widely accepted techniques for detecting Alzheimer cases. However, early detection can help prevent damage to brain tissue and heal it with proper treatment. Interpreting brain images is considered

    更新日期:2020-04-18
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