Proceedings Article•
Image Processing
01 Jan 1994-
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.
Citations
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09 Jul 2020
TL;DR: A Label-Attended Hashing (LAH) algorithm is proposed which enables an end-to-end hash model with inter-dependency feature extraction and the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning.
Abstract: For the multi-label image retrieval, the existing hashing algorithms neglect the dependency between objects and thus fail to capture the attention information in the feature extraction, which affects the precision of hash codes. To address this problem, we explore the inter-dependency between objects through their co-occurrence correlation from the label set and adopt Multi-modal Factorized Bilinear (MFB) pooling component so that the image representation learning can capture this attention information. We propose a Label-Attended Hashing (LAH) algorithm which enables an end-toend hash model with inter-dependency feature extraction. LAH first combines Convolutional Neural Network (CNN) and Graph Convolution Network (GCN) to separately generate the image representations and label co-occurrence embeddings, then adopts MFB to fuse these two modal vectors, finally learns the hash function with a Cauchy distribution based loss function via backpropagation. Extensive experiments on public multi-label datasets demonstrate that (1) LAH can achieve the state-of-the-art retrieval results and (2) the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning. GitHub address: https: //github.com/IDSM-AI/LAH
22 citations
Cites background or methods from "Image Processing"
...Several studies [Lai et al., 2016; Song et al., 2017; Huang et al., 2018] specifically designed for multi-label image retrieval recognize each object in isolation and then fuse these features to learn a hash model, which ∗Corresponding Author is essentially limited by ignoring the complex topology…...
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...…State-of-the-Arts We compare the retrieval performance of LAH with existing multi-label hashing methods including DSRH [Zhao et al., 2015], IAH [Lai et al., 2016], OLAH [Huang et al., 2018], RCDH [Ma et al., 2018], T-MLZSH [Zou et al., 2019], and the state-of-the-art hashing methods including…...
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...Several studies [Lai et al., 2016; Song et al., 2017; Huang et al., 2018] specifically designed for multi-label image retrieval recognize each object in isolation and then fuse these features to learn a hash model, which...
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TL;DR: Disruption of MT dynamics interferes with neurite outgrowth, not by disrupting the net assembly ofMTs at the growth cone, but rather because it alters the balance of forces that power the bulk forward translocation of MTs.
Abstract: During the development of the nervous system and regeneration following injury, microtubules (MTs) are required for neurite elongation. Whether this elongation occurs primarily through tubulin assembly at the tip of the axon, the transport of individual MTs, or because MTs translocate forward in bulk is unclear. Using fluorescent speckle microscopy (FSM), differential interference contrast (DIC), and phase contrast microscopy, we tracked the movement of MTs, phase dense material, and docked mitochondria in chick sensory and Aplysia bag cell neurons growing rapidly on physiological substrates. In all cases, we find that MTs and other neuritic components move forward in bulk at a rate that on average matches the velocity of neurite elongation. To better understand whether and why MT assembly is required for bulk translocation, we disrupted it with nocodazole. We found this blocked the forward bulk advance of material along the neurite and was paired with a transient increase in axonal tension. This indicates that disruption of MT dynamics interferes with neurite outgrowth, not by disrupting the net assembly of MTs at the growth cone, but rather because it alters the balance of forces that power the bulk forward translocation of MTs.
22 citations
TL;DR: It is proposed that free SAA sequesters lipid hydroperoxides and delays lipoprotein oxidation, though much less efficiently than other anti-oxidant proteins, such as apoA-I, that SAA displaces from HDL.
22 citations
TL;DR: The presented work is part of the H2020 project SWEEPER with the overall goal to develop a sweet pepper harvesting robot for use in greenhouses, and visual servoing is used to train the robot.
22 citations
Cites methods from "Image Processing"
...A common thresholding technique is to first convert the RGB image to HSI or HSV color space [28] and then perform thresholding [31,32]....
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TL;DR: Molar morphology is strongly correlated with diet and it is suggested that these dental traits can be used to infer diet with good accuracy for both extinct and extant murine species.
Abstract: Functional dental theory predicts that tooth shape responds evolutionarily to the mechanical properties of food. Most studies of mammalian teeth have focused on qualitative measures of dental anatomy and have not formally tested how the functional components of teeth adapt in response to diet. Here we generated a series of predictions for tooth morphology based on biomechanical models of food processing. We used murine rodents (Old World rats and mice) to test these predictions for the relationship between diet and morphology and to identify a suite of functional dental characteristics that best predict diets. One hundred and five dental characteristics were extracted from images of the upper and lower tooth rows and incisors for 98 species. After accounting for phylogenetic relationships, we showed that species evolving plant-dominated diets evolved deeper incisors, longer third molars, longer molar crests, blunter posteriorly angled cusps, and more expanded laterally oriented occlusal cusps than species adapting to animal-dominated diets. Measures of incisor depth, crest length, cusp angle and sharpness, occlusal cusp orientation, and the lengths of third molars proved the best predictors of dietary adaptation. Accounting for evolutionary history in a phylogenetic discriminant function analysis notably improved the classification accuracy. Molar morphology is strongly correlated with diet and we suggest that these dental traits can be used to infer diet with good accuracy for both extinct and extant murine species.
22 citations
References
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01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
Book•
03 Oct 1988TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.
8,504 citations
TL;DR: The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods.
Abstract: Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code.
5,890 citations
TL;DR: Eight constructs decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent a cellular valves and intact chamber geometry that could generate pump function in a modified working heart preparation.
Abstract: About 3,000 individuals in the United States are awaiting a donor heart; worldwide, 22 million individuals are living with heart failure. A bioartificial heart is a theoretical alternative to transplantation or mechanical left ventricular support. Generating a bioartificial heart requires engineering of cardiac architecture, appropriate cellular constituents and pump function. We decellularized hearts by coronary perfusion with detergents, preserved the underlying extracellular matrix, and produced an acellular, perfusable vascular architecture, competent acellular valves and intact chamber geometry. To mimic cardiac cell composition, we reseeded these constructs with cardiac or endothelial cells. To establish function, we maintained eight constructs for up to 28 d by coronary perfusion in a bioreactor that simulated cardiac physiology. By day 4, we observed macroscopic contractions. By day 8, under physiological load and electrical stimulation, constructs could generate pump function (equivalent to about 2% of adult or 25% of 16-week fetal heart function) in a modified working heart preparation.
2,454 citations
01 Sep 1997
TL;DR: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment.
Abstract: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.
2,046 citations