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  • Next-best-view regression using a 3D convolutional neural network
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-23
    J. Irving Vasquez-Gomez, David Troncoso, Israel Becerra, Enrique Sucar, Rafael Murrieta-Cid

    Automated three-dimensional (3D) object reconstruction is the task of building a geometric representation of a physical object by means of sensing its surface. Even though new single-view reconstruction techniques can predict the surface, they lead to incomplete models, specially, for non-commons objects such as antique objects or art sculptures. Therefore, to achieve the task’s goals, it is essential

    更新日期:2021-01-24
  • TriGAN: image-to-image translation for multi-source domain adaptation
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-19
    Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe, Elisa Ricci

    Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single-source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for multi-source domain adaptation (MSDA) based on generative adversarial networks. Our method is inspired by the observation that the appearance

    更新日期:2021-01-20
  • Reciprocal kernel-based weighted collaborative–competitive representation for robust face recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-16
    Shuangxi Wang, Hongwei Ge, Jinlong Yang, Yubing Tong, Shuzhi Su

    The Gaussian kernel function is widely used to encode the nonlinear correlations of the face images. However, some issues greatly limit its superiority, for example, it is sensitive to the parameter setting because of its definition based on the exponential operation, on the other hand, the Gaussian kernel needs costly computational time. Besides, the hidden information such as the distance information

    更新日期:2021-01-18
  • Circular coded target system for industrial applications
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-15
    Jakub Hurník, Aneta Zatočilová, David Paloušek

    Coded targets are used as reference targets with a known location during camera calibration, for robust searching of corresponding features between images during various applications of machine vision like object tracking, robot navigation or 3D measurement. In this paper, a target system for industrial photogrammetric applications is outlined. The methods which have been chosen emphasize maximum robustness

    更新日期:2021-01-15
  • A novel approach for ear recognition: learning Mahalanobis distance features from deep CNNs
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-08
    Ibrahim Omara, Ahmed Hagag, Guangzhi Ma, Fathi E. Abd El-Samie, Enmin Song

    Recently, deep convolutional neural networks (CNNs) have been used for ear recognition with the increasing and available ear image databases. However, most known ear recognition methods may be affected by selecting and weighting features; this is always a challenging issue in ear recognition and other pattern recognition applications. Metric learning can address this issue by using an accurate and

    更新日期:2021-01-10
  • Deep multiple instance learning for airplane detection in high-resolution imagery
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-08
    Mohammad Reza Mohammadi

    Automatic airplane detection in aerial imagery has a variety of applications. Two of the significant challenges in this task are variations in the scale and direction of the airplanes. To solve these challenges, we present a rotation-and-scale-invariant airplane proposal generator. We call this generator symmetric line segments (SLS) that is developed based on the symmetric and regular boundaries of

    更新日期:2021-01-08
  • Edge missing image inpainting with compression–decompression network in low similarity images
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-08
    Zhenghang Wu, Yidong Cui

    Image inpainting technology can patch images with missing pixels. Existing methods propose convolutional neural networks to repair corrupted images. The network extracts effective pixels around the missing pixels and uses the encoding–decoding structure to extract valuable information to repair the vacancy. However, if the missing part is too large to provide useful information, the result will be

    更新日期:2021-01-08
  • Deblur and deep depth from single defocus image
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-07
    Saeed Anwar, Zeeshan Hayder, Fatih Porikli

    In this paper, we tackle depth estimation and blur removal from a single out-of-focus image. Previously, depth is estimated, and blurred is removed using multiple images; for example, from multiview or stereo scenes, but doing so with a single image is challenging. Earlier works of monocular images for depth estimated and deblurring either exploited geometric characteristics or priors using hand-crafted

    更新日期:2021-01-08
  • Numerical joint invariant level set formulation with unique image segmentation result
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-07
    Reza Aghayan

    The level set method is one of the most widely used and powerful techniques in image science such as image/motion segmentation, object tracking, etc. This paper brings up an unstudied issue with discretized level set algorithms about ‘the non-uniqueness’ of segmentation results which is different from the problem of ‘the existence’ of a result. Our solution is to numerically approximate the level set

    更新日期:2021-01-08
  • A novel partition selection method for modular face recognition approaches on occlusion problem
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-07
    Mehmet Koc

    Recognizing the face with partial occlusion is an important problem for many face recognition applications. Since the occluded parts have no contribution to recognize the face, these parts should be excluded when performing the classification. In this paper, we propose a new method to detect and to use the non-occluded parts of face image for modular face recognition approaches. The occlusion of a

    更新日期:2021-01-08
  • Visual localization and servoing for drone use in indoor remote laboratory environment
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-05
    Fawzi Khattar, Franck Luthon, Benoit Larroque, Fadi Dornaika

    In this paper, we present a localization system for the use of drone in a remote laboratory. The objective is to allow a drone to inspect remote electronic instruments autonomously, as well as to return to its base and land on a platform for the recharge of its batteries. In addition, the drone should be able to detect the presence of a teacher in the laboratory and to center the human face in the

    更新日期:2021-01-05
  • Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-03
    Rui Miao, Zihang Jiang, Qinye Zhou, Yizhou Wu, Yuntian Gao, Jie Zhang, Zhibin Jiang

    In narrow overlap welding, serious defects in the weld will lead to band breakage accident, and the whole hot dip galvanizing unit will be shut down. Laser vision inspection hardware is used to collect real-time image of weld surface, and image defect recognition and evaluation system is developed to real-time detect quality. Firstly, region division is implemented. In view of the characteristics of

    更新日期:2021-01-03
  • Development of a character CAPTCHA recognition system for the visually impaired community using deep learning
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-03
    Xiaohui Zhang, Xinhua Liu, Thompson Sarkodie-Gyan, Zhixiong Li

    This study proposed an assistive system to recognize the special character CAPTCHAs for the visually impaired community in the Chinese region. To improve the recognition precision, a convolutional neural network (CNN), which is named Captchanet for recognition, was proposed. Firstly, a ten-layer network architecture was designed and three improved training strategies were proposed for the deep learning

    更新日期:2021-01-03
  • Classification of materials using a pulsed time-of-flight camera
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-03
    Shinan Lang, Jizhong Zhang, Yiheng Cai, Xiaoqing Zhu, Qiang Wu

    We propose an innovative method of material classification based on the imaging model of pulsed time-of-flight (ToF) camera integrated with the unique signature that describes physical properties of each material named reflection point spread function (RPSF). First, the optimization method reduces the effect of material surface interreflection, which would affect RPSF and lead to decreased accuracy

    更新日期:2021-01-03
  • Inception recurrent convolutional neural network for object recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-03
    Md Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari

    Deep convolutional neural network (DCNN) is an influential tool for solving various problems in machine learning and computer vision. Recurrent connectivity is a very important component of visual information processing within the human brain. The idea of recurrent connectivity is rarely applied within convolutional layers, the exceptions being a couple of DCNN architectures including recurrent convolutional

    更新日期:2021-01-03
  • A cognitive vision method for the detection of plant disease images
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-03
    Junde Chen, Jinxiu Chen, Defu Zhang, Y. A. Nanehkaran, Yuandong Sun

    Food security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural information. In this paper, we propose a novel approach to identify plant diseases. The method is divided into two parts: starting with the enhancement of the artificial

    更新日期:2021-01-03
  • Monocular 3D reconstruction of sail flying shape using passive markers
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-02
    Luiz Maciel, Ricardo Marroquim, Marcelo Vieira, Kevyn Ribeiro, Alexandre Alho

    We present a method to recover the 3D flying shape of a sail using passive markers. In the navigation and naval architecture domain, retrieving the sail shape may be of immense value to confirm or contest simulation results, and to aid the design of new optimal sails. Our acquisition setup is very simple and low-cost, as it is only necessary to fix a series of printable markers on the sail and register

    更新日期:2021-01-02
  • Hessian-polar context: a descriptor for microfilaria recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-02
    Faroq AL-Tam, António dos Anjos, Hamid Reza Shahbazkia

    This paper presents a new effective descriptor for microfilaria. Since microfilaria is a thin elastic object, the proposed descriptor handles it locally. At each medial point of the microfilaria, the local structure of the microfilaria votes for a given shape. Accumulating these votes in the polar domain yields a rich descriptor. Experimental results show the effectiveness of the proposed approach

    更新日期:2021-01-02
  • Optimal feature level fusion for secured human authentication in multimodal biometric system
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-12-21
    Himanshu Purohit, Pawan K. Ajmera

    The rising demand for high security and reliable authentication schemes, led to the development of the unimodal biometric system so that the multimodal biometric system has emerged. The multimodal biometric system will use more than one biometric trait of an individual for identification and security purpose. Fusion plays a major role in the multimodal biometric system. Several fusion techniques are

    更新日期:2020-12-21
  • A novel and intelligent vision-based tutor for Yogāsana : e-YogaGuru
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-25
    Geetanjali Kale, Varsha Patil, Mousami Munot

    Recent days have stamped enormous upsurge about health awareness in society. Self tutoring systems for supervising the performed exercises offer numerous advantages and are therefore emerging as an entity of dire necessity in health-sector. Considering the significantly increasing global acceptance of ‘\({{Yog\bar{a}sana}}\)’ as one of the most preferred exercise, this paper proposes a novel and an

    更新日期:2020-11-25
  • Measurement and inspection of electrical discharge machined steel surfaces using deep neural networks
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-21
    Jamal Saeedi, Matteo Dotta, Andrea Galli, Adriano Nasciuti, Umang Maradia, Marco Boccadoro, Luca Maria Gambardella, Alessandro Giusti

    We propose an industrial measurement and inspection system for steel workpieces eroded by electrical discharge machining, which uses deep neural networks for surface roughness estimation and defect detection. Specifically, a convolutional neural network (CNN) is used as a regressor in order to obtain steel surface roughness and a CNN based on spatial pooling pyramid is applied for defect classification

    更新日期:2020-11-22
  • A weighted feature transfer gan for medical image synthesis
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-21
    Shuaizhen Yao, Jianhua Tan, Yi Chen, Yanhui Gu

    Recent studies have shown that CycleGAN is a highly influential medical image synthesis model. However, the lack of sufficient constraints and the bottleneck layer in auto-encoder network usually lead to blurry image and meaningless features, which may affect medical judgment. In order to synthesize accurate and meaningful medical images, weighted feature transfer GAN (WFT-GAN) is proposed to improve

    更新日期:2020-11-22
  • A pruning method based on the measurement of feature extraction ability
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-21
    Honggang Wu, Yi Tang, Xiang Zhang

    As the network structure of convolutional neural network (CNN) becomes deeper and wider, network optimization, such as pruning, has received ever-increasing research focus. This paper propose a new pruning strategy based on Feature Extraction Ability Measurement (FEAM), which is a novel index of the feature extraction ability from both theoretical analysis and practical operation. Firstly, FEAM is

    更新日期:2020-11-22
  • Vision-based relative pose determination of cooperative spacecraft in neutral buoyancy environment
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-20
    Guohua Jia, Chaoqing Min, Kedian Wang, Zhanxia Zhu

    Neutral buoyancy systems simulate the microgravity environment by taking advantage of buoyancy forces of water to offset the gravity of test bodies. Functional verification of space robots in neutral buoyancy system is of great importance for ground tests. The relative pose determination of a spacecraft plays an essential role in the on-orbit operation of space robots. In order to meet the requirement

    更新日期:2020-11-21
  • Learnable spatiotemporal feature pyramid for prediction of future optical flow in videos
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-17
    Laisha Wadhwa, Snehasis Mukherjee

    The success of deep learning-based techniques in solving various computer vision problems motivated the researchers to apply deep learning to predict the optical flow of a video in the next frame. However, the problem of predicting the motion of an object in the next few frames remains an unsolved and less explored problem. Given a sequence of frames, predicting the motion in the next few frames of

    更新日期:2020-11-17
  • Calibrating a profile measurement system for dimensional inspection in rail rolling mills
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-09
    Álvaro F. Millara, Julio Molleda, Rubén Usamentiaga, Daniel F. García

    Modern, high-speed, railway transportation requires rails to conform strictly to requirements specified in various standards. One key requirement is the conformance of the dimensions of the rail cross section to those of the corresponding rail model, within tight tolerances. This paper deals with a system for dimensional quality inspection during the manufacture of railway rails. Optical triangulation

    更新日期:2020-11-09
  • Adaptive superpixel-based multi-object pedestrian recognition
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-07
    Tianhe Yu, Chengdong Wang, Xiao Liu, Ming Zhu

    This paper proposed an adaptive multi-object pedestrian recognition algorithm based on SLIC. First, we used SLIC to superpixel the pre-segmentation processing on the image. Then, the hash distance is added as the superpixel point aggregation parameter based on the traditional superpixel measurement unit of LAB color space distance and position distance. Finally, we identified the clustering subject

    更新日期:2020-11-09
  • Hierarchical fusion network for periocular and iris by neural network approximation and sparse autoencoder
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-04
    Faisal Algashaam, Kien Nguyen, Jasmine Banks, Vinod Chandran, Tuan-Anh Do, Mohamed Alkanhal

    The eye region is one of the most attractive sources for identification and verification due to the representative availability of such biometric modalities as periocular and iris. Many score-level fusion approaches have been proposed to combine these two modalities targeting to improve the robustness. The score-level approaches can be grouped into three categories: transformation-based, classification-based

    更新日期:2020-11-04
  • A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-03
    Yu-Dong Zhang, Suresh Chandra Satapathy, Shuaiqi Liu, Guang-Run Li

    Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements:

    更新日期:2020-11-04
  • Enhancing collaborative road scene reconstruction with unsupervised domain alignment
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-11-03
    Moritz Venator, Selcuk Aklanoglu, Erich Bruns, Andreas Maier

    Scene reconstruction and visual localization in dynamic environments such as street scenes are a challenge due to the lack of distinctive, stable keypoints. While learned convolutional features have proven to be robust to changes in viewing conditions, handcrafted features still have advantages in distinctiveness and accuracy when applied to structure from motion. For collaborative reconstruction of

    更新日期:2020-11-03
  • LS-Net: fast single-shot line-segment detector
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-29
    Van Nhan Nguyen, Robert Jenssen, Davide Roverso

    In unmanned aerial vehicle (UAV) flights, power lines are considered as one of the most threatening hazards and one of the most difficult obstacles to avoid. In recent years, many vision-based techniques have been proposed to detect power lines to facilitate self-driving UAVs and automatic obstacle avoidance. However, most of the proposed methods are typically based on a common three-step approach:

    更新日期:2020-10-30
  • FSD: feature skyscraper detector for stem end and blossom end of navel orange
    Mach. Vis. Appl. (IF 1.605) Pub Date : 2020-10-24
    Xiaoye Sun, Gongyan Li, Shaoyun Xu

    To accurately and efficiently distinguish the stem end and the blossom end of a navel orange from its black spots, we propose a feature skyscraper detector (FSD) with low computational cost, compact architecture and high detection accuracy. The main part of the detector is inspired from small object that the stem (blossom) end is complex and the black spot is densely distributed, so we design the feature

    更新日期:2020-10-27
  • 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
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