Tourists and Wild-life photographers are often hindered in capturing their cherished images or videos by a fence that limits accessibility to the scene of interest. The situation has been exacerbated by growing concerns of security at public places and a need exists to provide a tool that can be used for post-processing such fenced videos to produce a de-fenced image. There are several challenges in this problem, we identify them as Robust detection of fence/occlusions and Estimating pixel motion of background scenes and Filling in the fence/occlusions by utilizing information in multiple frames of the input video. In this work, we aim to build an automatic post-processing tool that can efficiently rid the input video of occlusion artifacts like fences. Our work is distinguished by two major contributions. The first is the introduction of learning based technique to detect the fences patterns with complicated backgrounds. The second is the formulation of objective function and further minimization through loopy belief propagation to fill-in the fence pixels. We observe that grids of Histogram of oriented gradients descriptor using Support vector machines based classifier significantly outperforms detection accuracy of texels in a lattice. We present results of experiments using several real-world videos to demonstrate the effectiveness of the proposed fence detection and de-fencing algorithm.
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）
Conventional approaches to image de-fencing suffer from non-robust fence detection and are limited to processing images of static scenes. In this position paper, we propose an automatic de-fencing algorithm for images of dynamic scenes. We divide the problem of image de-fencing into the tasks of automated fence detection, motion estimation and fusion of data from multiple frames of a captured video of the dynamic scene. Fences are detected automatically using two approaches, namely, employing Gabor filter and a machine learning method. We cast the fence removal problem in an optimization frame…
Indian Institute of Technology Kharagpur（印度理工学院卡哈拉格普尔分校）
Conventional approaches to image de-fencing have limited themselves to using only image data in adjacent frames of the captured video of an approximately static scene. In this work, we present a method to harness disparity using a stereo pair of fenced images in order to detect fence pixels. Tourists and amateur photographers commonly carry smartphones/phablets which can be used to capture a short video sequence of the fenced scene. We model the formation of the occluded frames in the captured video. Furthermore, we propose an optimization framework to estimate the de-fenced image using the to…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence detection apart from being limited to processing images of only static occluded scenes. In this paper, we propose a semi-automated de-fencing algorithm using a video of the dynamic scene. We use c…
Image de-fencing is one of the important aspects of recreational photography in which the objective is to remove the fence texture present in an image and generate an aesthetically pleasing version of the same image without the fence texture. In this paper, we aim to develop an automated and effective technique for fence removal and image reconstruction using conditional Generative Adversarial Networks (cGANs). These networks have been successfully applied in several domains of Computer Vision focusing on image generation and rendering. Our initial approach is based on a two-stage architecture…
Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid. De-fencing is the algorithmic process of automatically removing such obstructions from images, revealing the invisible parts of the scene. While this problem can be formulated as a combination of fence segmentation and image inpainting, this often leads to implausible hallucinations of the occluded regions. Existing multi-frame approaches rely on propagating information to a selected keyframe from its temporal neighb…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Conventional approaches to image de-fencing use multiple adjacent frames for segmentation of fences in the reference image and are limited to restoring images of static scenes only. In this paper, we propose a de-fencing algorithm for images of dynamic scenes using an occlusion-aware optical flow method. We divide the problem of image de-fencing into the tasks of automated fence segmentation from a single image, motion estimation under known occlusions and fusion of data from multiple frames of a captured video of the scene. Specifically, we use a pre-trained convolutional neural network to se…
De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers and improving the performance of computer vision algorithms such as object detection and recognition. However, the state-of-the-art de-fencing methods have limited performance caused by the difficulty of fence segmentation and also suffer from the motion of the camera or objects. To overcome these problems, we propose a novel method consisting of segmentation using convolutional neural networks and a fast/robust recov…
Columbia University（哥伦比亚大学）；Peking University（北京大学）；National University of Singapore（新加坡国立大学）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）
This paper describes and provides an initial solution to a novel video editing task, i.e., video de-fencing. It targets automatic restoration of the video clips that are corrupted by fence-like occlusions during capture. Our key observation lies in the visual parallax between fences and background scenes, which is caused by the fact that the former are typically closer to the camera. Unlike in traditional image inpainting, fence-occluded pixels in the videos tend to appear later in the temporal dimension and are therefore recoverable via optimized pixel selection from relevant frames. To event…
Rhodes University（罗德斯大学）；University of Ngaoundéré（恩冈代雷大学）
In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences …
University of sousse（苏塞大学）；University of Monastir（莫纳斯提尔大学）
The aim of this work is to develop a method for automatic segmentation of the liver based on a priori knowledge of the image, such as location and shape of the liver.
In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image. Second, the initial seeds are automatically selected based on ROIs extracted from the image. Third, the most reprehensive seeds are selected using a machine learning algorithm. Finally, the cellular image is segmented into regions where each region corresponds to a seed. The aim of the proposed is to automatically extract the Region of Interests (ROI) from the cellular images in terms of overcoming the explosion, u…
Indian Institute of Technology, Gandhinagar（甘地讷格尔印度理工学院）
Image matting is a longstanding problem in computational photography. Although, it has been studied for more than two decades, yet there is a challenge of developing an automatic matting algorithm which does not require any human efforts. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte form the input image. In this paper, we present a simple and efficient approach to automatically generate the trimap from the input image and make the whole matting process free from human-in-the-loop. We use learning based…
Gel electrophoresis, a widely used technique to separate DNA according to their size and weight, generates images that can be analyzed automatically. Manual or semiautomatic image processing presents a bottleneck for further development and leads to reproducibility issues. In this paper, we present a fully automated system with high accuracy for analyzing DNA and proteins. The proposed algorithm consists of four main steps: automatic thresholding, shifting, filtering, and data processing. Automatic thresholding, used to equalize the gray values of the gel electrophoresis image background, is o…
In the 21st century, Aerial and satellite images are information rich. They are also complex to analyze. For GIS systems, many features require fast and reliable extraction of open space area from high resolution satellite imagery. In this paper we will study efficient and reliable automatic extraction algorithm to find out the open space area from the high resolution urban satellite imagery. This automatic extraction algorithm uses some filters and segmentations and grouping is applying on satellite images. And the result images may use to calculate the total available open space area and the…
Lifelogging cameras capture everyday life from a first-person perspective, but generate so much data that it is hard for users to browse and organize their image collections effectively. In this paper, we propose to use automatic image captioning algorithms to generate textual representations of these collections. We develop and explore novel techniques based on deep learning to generate captions for both individual images and image streams, using temporal consistency constraints to create summaries that are both more compact and less noisy. We evaluate our techniques with quantitative and qua…
University of Johannesburg（约翰内斯堡大学）；Vitruvian Medical Diagnostics（维特鲁威医疗诊断公司）
Partial differential equations have recently garnered substantial attention as an image processing framework due to their extensibility, the ability to rigorously engineer and analyse the governing dynamics as well as the ease of implementation using numerical methods. This paper explores a novel approach to image trinarization with a concrete real-world application of classifying regions of sperm images used in the automatic analysis of sperm morphology. The proposed methodology engineers a diffusion equation with non-linear source term, exhibiting three steady-states. The model is implemente…
Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcher-independent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrain…
University of Naples Federico II（那不勒斯费德里科二世大学）；Paris Lodron University of Salzburg（萨尔茨堡巴黎洛德隆大学）
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs). It is worth mentioning that the same EO IUS architecture is suitable for a large variety of EO based value adding products and s…
Gulbarga University（古尔巴加大学）；S. V. College, Udgir（乌迪尔S.V.学院）
In this paper, we study efficient and reliable automatic extraction algorithm to find out the open space area from the high resolution urban satellite imagery, and to detect changes from the extracted open space area during the period 2003, 2006 and 2008. This automatic extraction and change detection algorithm uses some filters, segmentation and grouping that are applied on satellite images. The resultant images may be used to calculate the total available open space area and the built up area. It may also be used to compare the difference between present and past open space area using histor…
Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）
Automatic segmentation of objects from a single image is a challenging problem which generally requires training on large number of images. We consider the problem of automatically segmenting only the dynamic objects from a given pair of images of a scene captured from different positions. We exploit dense correspondences along with saliency measures in order to first localize the interest points on the dynamic objects from the two images. We propose a novel approach based on techniques from computational geometry in order to automatically segment the dynamic objects from both the images using…
In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.
Since manual object detection is very inaccurate and time consuming, some automatic object detection tools have been developed in recent years. At the moment, there is no image analysis software available which provides an automatic, objective assessment of 3D foci which is generally applicable. Complications arise from discrete foci which are very close or even come in contact to other foci, moreover they are of variable sizes and show variable signal-to-noise, and must be analyzed fully in 3D. Therefore we introduce the 3D-OSCOS (3D-Object Segmentation and Colocalization Analysis based on Sp…
University of Campinas（坎皮纳斯大学）；TU Bergakademie Freiberg（弗赖贝格工业大学）
One of the major difficulties of automatic track counting using photomicrographs is separating overlapped tracks. We address this issue combining image processing algorithms such as skeletonization, and we test our algorithm with several binarization techniques. The counting algorithm was successfully applied to determine the efficiency factor GQR, necessary for standardless fission-track dating, involving counting induced tracks in apatite and muscovite with superficial densities of about $6 \times 10^5$ tracks/$cm^2$.
This paper proposes a novel luminance adjustment method based on automatic exposure compensation for multi-exposure image fusion. Multi-exposure image fusion is a method to produce images without saturation regions, by using photos with different exposures. In conventional works, it has been pointed out that the quality of those multi-exposure images can be improved by adjusting the luminance of them. However, how to determine the degree of adjustment has never been discussed. This paper therefore proposes a way to automatically determines the degree on the basis of the luminance distribution …
Hengyang Normal University（衡阳师范学院）；Cardiff University（卡迪夫大学）
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout, and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the …
Indian Institute of Technology Gandhinagar（甘地纳格尔印度理工学院）
Non-photorealistic rendering techniques work on image features and often manipulate a set of characteristics such as edges and texture to achieve a desired depiction of the scene. Most computational photography methods decompose an image using edge preserving filters and work on the resulting base and detail layers independently to achieve desired visual effects. We propose a new approach for content-aware non-photorealistic rendering of images where we manipulate the visually salient and the non-salient regions separately. We propose a novel content-aware framework in order to render an image…
Human beings understand natural language description and could able to imagine a corresponding visual for the same. For example, given a description of the interior of a house, we could imagine its structure and arrangements of furniture. Automatic synthesis of real-world images from text descriptions has been explored in the computer vision community. However, there is no such attempt in the area of document images, like floor plans. Floor plan synthesis from sketches, as well as data-driven models, were proposed earlier. Ours is the first attempt to render building floor plan images from tex…
Image co-segmentation is important for its advantage of alleviating the ill-pose nature of image segmentation through exploring the correlation between related images. Many automatic image co-segmentation algorithms have been developed in the last decade, which are investigated comprehensively in this paper. We firstly analyze visual/semantic cues for guiding image co-segmentation, including object cues and correlation cues. Then we describe the traditional methods in three categories of object elements based, object regions/contours based, common object model based. In the next part, deep lea…
Color and tone stylization strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjust…
Midea Group（美的集团）；Stanford University（斯坦福大学）；University of Santa Clara（圣克拉拉大学）
In recent years, high-speed videoendoscopy (HSV) has significantly aided the diagnosis of voice pathologies and furthered the understanding the voice production in recent years. As the first step of these studies, automatic segmentation of glottal images till presents a major challenge for this technique. In this paper, we propose an improved Saliency Network that automatically delineates the contour of the glottis from HSV image sequences. Our proposed additional saliency measure, Motion Saliency (MS), improves upon the original Saliency Network by using the velocities of defined edges. In ou…
In this work, the possibilities for segmentation of cells from their background and each other in digital image were tested, combined and improoved. Lot of images with young, adult and mixture cells were able to prove the quality of described algorithms. Proper segmentation is one of the main task of image analysis and steps order differ from work to work, depending on input images. Reply for biologicaly given question was looking for in this work, including filtration, details emphasizing, segmentation and sphericity computing. Order of algorithms and way to searching for them was also descri…
Huazhong University of Science and Technology（华中科技大学）；University of Pennsylvania（宾夕法尼亚大学）；Children’s Hospital of Philadelphia（费城儿童医院）
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we developed a novel boundary distance regression deep neural network to segment the kidneys, informed by the fact that the kidney boundaries are relatively consistent across images in terms of their appearance. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from ultrasound…
Image Signal Processor (ISP) comprises of various blocks to reconstruct image sensor raw data to final image consumed by human visual system or computer vision applications. Each block typically has many tuning parameters due to the complexity of the operation. These need to be hand tuned by Image Quality (IQ) experts, which takes considerable amount of time. In this paper, we present an automatic IQ tuning using nonlinear optimization and automatic reference generation algorithms. The proposed method can produce high quality IQ in minutes as compared with weeks of hand-tuned results by IQ exp…
University of Nebraska at Omaha（内布拉斯加大学奥马哈分校）；New Jersey Institute of Technology（新泽西理工学院）
Image segmentation as a clustering problem is to identify pixel groups on an image without any preliminary labels available. It remains a challenge in machine vision because of the variations in size and shape of image segments. Furthermore, determining the segment number in an image is NP-hard without prior knowledge of the image content. This paper presents an automatic color image pixel clustering scheme based on mussels wandering optimization. By applying an activation variable to determine the number of clusters along with the cluster centers optimization, an image is segmented with minim…
Calibration of devices with different modalities is a key problem in robotic vision. Regular spatial objects, such as planes, are frequently used for this task. This paper deals with the automatic detection of ellipses in camera images, as well as to estimate the 3D position of the spheres corresponding to the detected 2D ellipses. We propose two novel methods to (i) detect an ellipse in camera images and (ii) estimate the spatial location of the corresponding sphere if its size is known. The algorithms are tested both quantitatively and qualitatively. They are applied for calibrating the sens…
Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, we present the first data-driven automatic error detection approach targeting two types of IC segmentation errors: wire errors and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN…
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature…
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses the question of how to automatically learn such parts from a set of labeled training images. The training of parts is cast as a quadratic assignment problem in which optimal correspondences between image regions and parts are automatically learned. The paper analyses different assignment strategies and thoroughly evaluates them on two public datasets: Willow actions and MIT 67 scenes. State-of-the art results are obta…
The segmentation and classification of animals from camera-trap images is due to the conditions under which the images are taken, a difficult task. This work presents a method for classifying and segmenting mammal genera from camera-trap images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA) for segmenting, Convolutional Neural Networks (CNNs) for extracting features, Least Absolute Shrinkage and Selection Operator (LASSO) for selecting features, and Artificial Neural Networks (ANNs) or Support Vector Machines (SVM) for classifying mammal genera present in the Colombian…
Huazhong University of Science and Technology（华中科技大学）；University of Pennsylvania（宾夕法尼亚大学）；The Children’s Hospital of Philadelphia（费城儿童医院）
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we propose subsequent boundary distance regression and pixel classification networks to segment the kidneys, informed by the fact that the kidney boundaries have relatively homogenous texture patterns across images. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from U…
McGill University（麦吉尔大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人学会）
Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of automatic trimap generation method of dilating foreground segmentation fluctuates with segmentation quality. Therefore, we argue that how to handle trade-off of additional information input is a major issue in automatic matting. This paper presents a semantic-guided automatic natura…
We present a superpixel-based strategy for segmenting skin lesion on dermoscopic images. The segmentation is carried out by over-segmenting the original image using the SLIC algorithm, and then merge the resulting superpixels into two regions: healthy skin and lesion. The mean RGB color of each superpixel was used as merging criterion. The presented method is capable of dealing with segmentation problems commonly found in dermoscopic images such as hair removal, oil bubbles, changes in illumination, and reflections images without any additional steps. The method was evaluated on the PH2 and IS…
In this article, we propose an automatic method for the segmentation of pollen grains from microscope images, followed by the automatic segmentation of their exine. The objective of exine segmentation is to separate the pollen grain in two regions of interest: exine and inner part. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. As a rough stage, grain segmentation is performed by a procedure involving clustering and morphological operations, while the exine is approximated by an iterative procedure consisting in consecutive cropping steps of the pollen…
AGH University of Science and Technology（AGH科学技术大学）
In this paper, we present a short description of the method proposed to ANHIR challenge organized jointly with the IEEE ISBI 2019 conference. We propose a method consisting of preprocessing, initial alignment, nonrigid registration algorithms and a method to automatically choose the best result. The method turned out to be robust (99.792% robustness) and accurate (0.38% average median rTRE). The main drawback of the proposed method is relatively high computation time. However, this aspect can be easily improved by cleaning the code and proposing a GPU implementation.
Current image transformation and recoloring algorithms try to introduce artistic effects in the photographed images, based on user input of target image(s) or selection of pre-designed filters. These manipulations, although intended to enhance the impact of an image on the viewer, do not include the option of image transformation by specifying the affect information. In this paper we present an automatic image-transformation method that transforms the source image such that it can induce an emotional affect on the viewer, as desired by the user. Our proposed novel image emotion transfer algori…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
When we use video chat, video game, or other video applications, motion-blurred hands often appear. Accurately extracting these hands is very useful for video editing and behavior analysis. However, existing motion-blurred object extraction methods either need user interactions, such as user supplied trimaps and scribbles, or need additional information, such as background images. In this paper, a novel method which can automatically extract the semi-transparent motion-blurred hand just according to the original RGB image is proposed. The proposed method separates the extraction task into two …
University of Chicago（芝加哥大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation. On both fully and partially automatic colorization tasks, we outperform existing methods. We also explore colorization as a vehicle for self-supervised visual re…
We present a 3-step registration pipeline for differently stained histological serial sections that consists of 1) a robust pre-alignment, 2) a parametric registration computed on coarse resolution images, and 3) an accurate nonlinear registration. In all three steps the NGF distance measure is minimized with respect to an increasingly flexible transformation. We apply the method in the ANHIR image registration challenge and evaluate its performance on the training data. The presented method is robust (error reduction in 99.6% of the cases), fast (runtime 4 seconds) and accurate (median relati…
