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Recognition of varying size scene images using semantic analysis of deep activation maps Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-03-01 Shikha Gupta; A. D. Dileep; Veena Thenkanidiyoor
Understanding the complex semantic structure of scene images requires mapping the image from pixel space to high-level semantic space. In semantic space, a scene image is represented by the posterior probabilities of concepts (e.g., ‘car,’ ‘chair,’ ‘window,’ etc.) present in it and such representation is known as semantic multinomial (SMN) representation. SMN generation requires a concept annotated
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Research on defect detection method of powder metallurgy gear based on machine vision Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-27 Maohua Xiao, Weichen Wang, Xiaojie Shen, Yue Zhu, Petr Bartos, Yilidaer Yiliyasi
Powder metallurgy gears are often accompanied by broken teeth, abrasion, scratches and crack defects. In order to eliminate the defective gears in gear production and improve the yield of gears, this paper presents an improved GA–PSO algorithm, called the SHGA–PSO algorithm. Firstly, the gear images were preprocessed by bilateral filtering, and the images were segmented by the Sobel operator. Then
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List-wise learning-to-rank with convolutional neural networks for person re-identification Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-27 Yiqiang Chen, Stefan Duffner, Andrei Stoian, Jean-Yves Dufour, Atilla Baskurt
In this paper, we present a novel machine learning-based image ranking approach using Convolutional Neural Networks (CNN). Our proposed method relies on a similarity metric learning algorithm operating on lists of image examples and a loss function taking into account the ranking in these lists with respect to different query images. This comprises two major contributions: (1) Rank lists instead of
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Two-layer pyramid-based blending method for exposure fusion Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-25 Suthum Keerativittayanun, Toshiaki Kondo, Kazunori Kotani, Teera Phatrapornnant, Jessada Karnjana
Multi-exposure fusion is a technique used to generate a high-dynamic-range image without calculating the camera response function and without compressing its ranges with the tone mapping process. There are many schemes for fusing multi-exposure images. One of the famous schemes is the pyramid-based blending, which fuses multi-exposure images together based on the concept of multi-resolution blending
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Coarse2Fine: a two-stage training method for fine-grained visual classification Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-25 Amir Erfan Eshratifar, David Eigen, Michael Gormish, Massoud Pedram
Small inter-class and large intra-class variations are the key challenges in fine-grained visual classification. Objects from different classes share visually similar structures, and objects in the same class can have different poses and viewpoints. Therefore, the proper extraction of discriminative local features (e.g., bird’s beak or car’s headlight) is crucial. Most of the recent successes on this
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Multi-FAN: multi-spectral mosaic super-resolution via multi-scale feature aggregation network Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-24 Mehrdad Sheoiby, Sadegh Aliakbarian, Saeed Anwar, Lars Petersson
This paper introduces a novel method to super-resolve multi-spectral images captured by modern real-time single-shot mosaic image sensors, also known as multi-spectral cameras. Our contribution is twofold. Firstly, we super-resolve multi-spectral images from mosaic images rather than image cubes, which helps to take into account the spatial offset of each wavelength. Secondly, we introduce an external
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Lightweight convolutional neural network-based pedestrian detection and re-identification in multiple scenarios Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-24 Xiao Ke, Xinru Lin, Liyun Qin
Pedestrian detection and re-identification technology is a research hotspot in the field of computer vision. This technology currently has issues such as insufficient pedestrian expression ability, occlusion, diverse pedestrian attitude, and difficulty of small-scale pedestrian detection. In this paper, we proposed an end-to-end pedestrian detection and re-identification model in real scenes, which
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3MNet: Multi-task, multi-level and multi-channel feature aggregation network for salient object detection Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-18 Xinghe Yan, Zhenxue Chen, Q. M. Jonathan Wu, Mengxu Lu, Luna Sun
Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures
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Automated detection and classification of spilled loads on freeways based on improved YOLO network Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-02-14 Siqi Zhou, Yufeng Bi, Xu Wei, Jiachen Liu, Zixin Ye, Feng Li, Yuchuan Du
This study aims to utilize a modified you only look once (YOLO) network to address the detection and classification of spilled loads on freeways. YOLO architecture was augmented in two ways. Firstly, a kernel size of 1 × 1 for the conv layers was used. Secondly, the use of connections between the convolution layers was proposed. For training the network, a synthetic dataset was constructed where ImageNet
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Crowd density classification method based on pixels and texture features Mach. Vis. Appl. (IF 1.605) Pub Date : 2021-01-27 Dongyao Jia, Chuanwang Zhang, Bing Zhang
Crowd density classification has been a challenging task in the field of computer vision, which has various applications in public and commercial domains. Many researches on the classification and recognition method of the crowd density have been introduced in the past, while there still exists the problems of inaccuracy, poor robustness and inefficiency. An adaptive crowd density classification method
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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:
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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
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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:
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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
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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
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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
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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
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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
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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
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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,
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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