| 期刊全称 | Advanced Concepts for Intelligent Vision Systems | | 期刊简称 | 17th International C | | 影响因子2023 | Jacques Blanc-Talon,Cosimo Distante,Paul Scheunder | | 视频video | | | 发行地址 | Includes supplementary material: | | 学科分类 | Lecture Notes in Computer Science | | 图书封面 |  | | 影响因子 | This book constitutes the refereed proceedings of the 17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016, held in Lecce, Italy, in October 2016..The 64 revised full papers presented in this volume were carefully selected from 137 submissions. They deal with classical low-level image processing techniques; image and video compression; 3D; security and forensics; and evaluation methodologies. | | Pindex | Conference proceedings 2016 |
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Front Matter |
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Abstract
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,Gradients versus Grey Values for Sparse Image Reconstruction and Inpainting-Based Compression, |
Markus Schneider,Pascal Peter,Sebastian Hoffmann,Joachim Weickert,Enric Meinhardt-Llopis |
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Abstract
Interpolation methods that rely on partial differential equations can reconstruct images with high quality from a few prescribed pixels. A whole class of compression codecs exploits this concept to store images in terms of a sparse grey value representation. Recently, Brinkmann et al. (2015) have suggested an alternative approach: They propose to store gradient data instead of grey values. However, this idea has not been evaluated and its potential remains unknown. In our paper, we compare gradient and grey value data for homogeneous diffusion inpainting w.r.t. two different aspects: First, we evaluate the reconstruction quality, given a comparable amount of data of both kinds. Second, we assess how well these sparse representations can be stored in compression applications. To this end, we establish a framework for optimising and encoding the known data. It allows a fair comparison of both the grey value and the gradient approach. Our evaluation shows that gradient-based reconstructions avoid visually distracting singularities involved in the reconstructions from grey values, thus improving the visual fidelity. Surprisingly, this advantage does not carry over to compression due to
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,Global Bilateral Symmetry Detection Using Multiscale Mirror Histograms, |
Mohamed Elawady,Cécile Barat,Christophe Ducottet,Philippe Colantoni |
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Abstract
In recent years, there has been renewed interest in bilateral symmetry detection in images. It consists in detecting the main bilateral symmetry axis inside artificial or natural images. State-of-the-art methods combine feature point detection, pairwise comparison and voting in Hough-like space. In spite of their good performance, they fail to give reliable results over challenging real-world and artistic images. In this paper, we propose a novel symmetry detection method using multi-scale edge features combined with local orientation histograms. An experimental evaluation is conducted on public datasets plus a new aesthetic-oriented dataset. The results show that our approach outperforms all other concurrent methods.
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,Neural Network Boundary Detection for 3D Vessel Segmentation, |
Robert Ieuan Palmer,Xianghua Xie |
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Abstract
Conventionally, hand-crafted features are used to train machine learning algorithms, however choosing useful features is not a trivial task as they are very much data-dependent. Given raw image intensities as inputs, supervised neural networks (NNs) essentially learn useful features by adjusting the weights of its nodes using the . algorithm. In this paper we investigate the performance of NN architectures for the purpose of boundary detection, before integrating a chosen architecture in a data-driven deformable modelling framework for full segmentation. Boundary detection performed well, with boundary sensitivity of >88 % and specificity of >85 % for highly obscured and diffused lymphatic vessel walls. In addition, the vast majority of all boundary-classified pixels were in the immediate vicinity of the ground truth boundary. When integrated into a 3D deformable modelling framework it produced an area overlap with the ground truth of >98 %, and both point-to-mesh and Hausdorff distance errors were less than other approaches. To this end it has been shown that NNs are suitable for boundary detection in deformable modelling, where object boundaries are obscured, diffused and low in
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,A Simple Human Activity Recognition Technique Using DCT, |
Aziz Khelalef,Fakhreddine Ababsa,Nabil Benoudjit |
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Abstract
In this paper, we present a simple new human activity recognition method using discrete cosine transform (DCT). The scheme uses the DCT coefficients extracted from silhouettes as descriptors (features) and performs frame-by-frame recognition, which make it simple and suitable for real time applications. We carried out several tests using radial basis neural network (RBF) for classification, a comparative study against stat-of-the-art methods shows that our technique is faster, simple and gives higher accuracy performance comparing to discrete transform based techniques and other methods proposed in literature.
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,Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller, |
Tomás Mantecón,Carlos R. del-Blanco,Fernando Jaureguizar,Narciso García |
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Abstract
Hand gestures are one of the main alternatives for Human-Computer Interaction. For this reason, a hand gesture recognition system using near-infrared imagery acquired by a Leap Motion sensor is proposed. The recognition system directly characterizes the hand gesture by computing a global image descriptor, called Depth Spatiograms of Quantized Patterns, without any hand segmentation stage. To deal with the high dimensionality of the image descriptor, a Compressive Sensing framework is applied, obtaining a manageable image feature vector that almost preserves the original information. Finally, the resulting reduced image descriptors are analyzed by a set of Support Vectors Machines to identify the performed gesture independently of the precise hand location in the image. Promising results have been achieved using a new hand-based near-infrared database.
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,Horizon Line Detection from Fisheye Images Using Color Local Image Region Descriptors and Bhattacha |
Youssef El merabet,Yassine Ruichek,Saman Ghaffarian,Zineb Samir,Tarik Boujiha,Raja Touahni,Rochdi Me |
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Abstract
Several solutions allowing to compensate the lack of performance of GNSS (Global Navigation Satellites Systems) occurring when operating in constrained environments (dense urbain areas) have emerged in recent years. Characterizing the environment of reception of GNSS signals using a fisheye camera oriented to the sky is one of these relevant solutions. The idea consists in determining LOS (Line-Of-Sight) satellites and NLOS (Nonline-Of-Sight) satellites by classifying the content of acquired images into two regions (sky and not-sky). In this paper, aimed to make this approach more effective, we propose a region-based image classification technique through Bhattacharyya coefficient-based distance and local image region descriptors. The proposed procedure is composed of four major steps: (i) A simplification step that consists in simplifying the acquired image with an appropriate couple of colorimetric invariant and exponential transform. (ii) The second step consists in segmenting the simplified image in different regions of interest using Statistical Region Merging segmentation method. (iii) In the third step, the segmented regions are characterized with a number of local color ima
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,Joint Segmentation of Myocardium on Rest and Stress Spect Images, |
Marc Filippi,Michel Desvignes,Anastasia Bozok,Gilles Barone-Rochette,Daniel Fagret,Laurent Riou,Cath |
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Abstract
This paper presents a level set segmentation of the myocardium, endocardium and epicardium surfaces of the heart from 2D SPECT rest and stress perfusion images of the same patient to compute a heterogeneity index. Cardiac SPECT images have low resolution, low signal to noise ratio and lack of anatomical information. So accurate segmentation is difficult. The proposed method adds joint constraints of shape, parallelism and intensity in a level-set framework to simultaneously extract myocardium from rest and stress images. Results are compared to classical level-set segmentation.
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,Parallel Hough Space Image Generation Method for Real-Time Lane Detection, |
Hee-Soo Kim,Seung-Hae Beak,Soon-Yong Park |
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Abstract
This paper proposes a new parallelization method to generate Hough space images for real-time lane detection, using the new NVIDIA Jetson TK1 board. The computation cost in Standard Hough Transform is relatively high due to its higher amount of unnecessary operations. Therefore, in this paper, we introduce an enhanced Hough image generation method to reduce computation time for real-time lane detection purposes, and reduce all the unnecessary operations exist in the Standard method. We implemented our proposed method in both CPU and GPU based platforms and compared the processing speeds with the Standard method. The experiment results induce that the proposed method runs 10 times faster than the existing method in CPU platform, whereas 60 times faster in the GPU platform.
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,A Novel Decentralised System Architecture for Multi-camera Target Tracking, |
Gaetano Di Caterina,Trushali Doshi,John J. Soraghan,Lykourgos Petropoulakis |
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Abstract
Target tracking in a multi-camera system is an active and challenging research that in many systems requires video synchronisation and knowledge of the camera set-up and layout. In this paper a highly flexible, modular and decentralised system architecture is presented for multi-camera target tracking with relaxed synchronisation constraints among camera views. Moreover, the system does not rely on positional information to handle camera hand-off events. As a practical application, the system itself can, at any time, automatically select the best target view available, to implicitly solve occlusion. Further, to validate the proposed architecture, an extension to a multi-camera environment of the colour-based IMS-SWAD tracker is used. The experimental results show that the tracker can successfully track a chosen target in multiple views, in both indoor and outdoor environments, with non-overlapping and overlapping camera views.
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,Intramolecular FRET Efficiency Measures for Time-Lapse Fluorescence Microscopy Images, |
Mark Holden |
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Abstract
Here we investigate quantitative measures of Förster resonance energy transfer (FRET) efficiency that can be used to quantify protein-protein interactions using fluorescence microscopy images of living cells. We adopt a joint intensity space approach and develop a parametric shot noise model to estimate the uncertainty of FRET efficiency on a per pixel basis. We evaluate our metrics rigorously by simulating photon emission events corresponding to typical conditions and demonstrate advantages of our metrics over the conventional ratiometric one. In particular, our measure is linear, normalised and has greater tolerance to low SNR characteristic of FRET fluorescence microscopy images.
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,Predicting Image Aesthetics with Deep Learning, |
Simone Bianco,Luigi Celona,Paolo Napoletano,Raimondo Schettini |
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Abstract
In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict image aesthetics. To this end we fine-tune a canonical CNN architecture, originally trained to classify objects and scenes, by casting the image aesthetic prediction as a regression problem. We also investigate whether image aesthetic is a global or local attribute, and the role played by bottom-up and top-down salient regions to the prediction of the global image aesthetic. Experimental results on the canonical Aesthetic Visual Analysis (AVA) dataset show the robustness of the solution proposed, which outperforms the best solution in the state of the art by almost 17 % in terms of Mean Residual Sum of Squares Error (MRSSE).
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,Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images, |
Thibault Julliand,Vincent Nozick,Hugues Talbot |
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Abstract
Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.
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,Breast Shape Parametrization Through Planar Projections, |
Giovanni Gallo,Dario Allegra,Yaser Gholizade Atani,Filippo L. M. Milotta,Filippo Stanco,Giuseppe Cat |
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Abstract
In the last years, 3D scanning has replaced the low tech approach to acquire direct anthropometric measurements. These new methodologies provide a detailed digital model of the body and allow analysis of more complex information like volume, shape, curvature, and so on. The possibility to acquire the shape of soft tissues, such as the female human breast, has attracted the interest breast surgery specialists. The main aim of this work is to propose an innovative strategy to automatically analyze 3D breast shape in order to describe them within a quantitative well defined framework. In particular we propose a scanning procedure for a proper acquisition of breast surfaces by using the handheld scanner Structure Sensor, as well as a framework to process 3D digital data to extract the shape information. The proposed method consists in two main parts: firstly, the acquired digital 3D surfaces are projected in a 2D space and a set of 17 geometrical landmarks are extracted; then by exploiting Thin Plate Splines and Principal Components Analysis the original data are summarised and the breast shape is described by a small set of numerical parameters.
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,Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU |
Joris Roels,Jonas De Vylder,Yvan Saeys,Bart Goossens,Wilfried Philips |
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Abstract
The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: (1) the coarse-grained parallelism of the algorithm, (2) the size of the data and (3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very sui
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,Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comp |
Sébastien Villon,Marc Chaumont,Gérard Subsol,Sébastien Villéger,Thomas Claverie,David Mouillot |
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Abstract
In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.
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,A Real-Time Eye Gesture Recognition System Based on Fuzzy Inference System for Mobile Devices Monit |
Hanene Elleuch,Ali Wali,Anis Samet,Adel M. Alimi |
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Abstract
In this paper, we proposed a new system of mobile human-computer interaction based on eye gestures. This system aims to control and command mobile devices through the eyes for the purpose of providing an intuitive communication with these devices and a flexible usage with all contexts that a user can be situated. This system is based on a real-time streaming video captured from the front-facing camera without needing any additional equipment. The algorithm aims in the first time to detect user’s face and their eyes in the second time. The eyes gesture recognition is based on fuzzy inference system. We deployed this algorithm on an android-based tablet and we asked 8 volunteers to test it. The obtained results proved that this system has promising and competitive results.
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,Spatially Varying Weighting Function-Based Global and Local Statistical Active Contours. Applicatio |
Aicha Baya Goumeidane,Nafaa Nacereddine |
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Abstract
Image segmentation is a crucial task in the image processing field. This paper presents a new region-based active contour which handles global information as well as local one, both based on the pixels intensities. The trade-off between these information is achieved by a spatially varying function computed for each contour node location. The application preliminary results of this method on computed tomography and X-ray images show outstanding and efficient object extraction.
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,Vegetation Segmentation in Cornfield Images Using Bag of Words, |
Yerania Campos,Erik Rodner,Joachim Denzler,Humberto Sossa,Gonzalo Pajares |
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Abstract
We provide an alternative methodology for vegetation segmentation in cornfield images. The process includes two main steps, which makes the main contribution of this approach: (a) a low-level segmentation and (b) a class label assignment using Bag of Words (BoW) representation in conjunction with a supervised learning framework. The experimental results show our proposal is adequate to extract green plants in images of maize fields. The accuracy for classification is 95.3 % which is comparable to values in current literature.
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,Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks, |
Ali Youssef,Dario Albani,Daniele Nardi,Domenico Daniele Bloisi |
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Abstract
The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.
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发表于 2025-3-21 17:38:51
