Computer Vision

Computer Vision
Making the commerce experience seamless with the use of computer vision.
Info

The Trend
Advances in computing & storage infrastructure, display technology, social networks and mobile devices have revolutionized commerce. Mobile phones, tablets and wearable computing devices with camera have obliterated the boundary between offline and online commerce. Now commerce is omni-channel and ubiquitous.

Why Computer Vision?
eBay is one of the largest commerce platforms with diverse user base all over the world and hosts any product one can think of. There are a countless number of products with numerous variations. It is almost impossible to search or describe these products using few words. Pictures capture information in a compact form. So, it is more than a matter of convenience to use visual information to search or identify products. Each item on eBay has at least one picture along with a rich metadata such as title, seller tags, description, etc. This is a playground for computer vision researchers.

What We Do
Each item on eBay has at least one picture along with a rich metadata such as title, seller tags, description, etc. This is a playground for computer vision researchers. Our distributed computer vision research team brings core computer vision expertise to commerce. We look at pixels along with other metadata to understand color, pattern, shape, style, material and semantics to tackle rich computer vision problems such as large scale visual search, optical character recognition, object recognition, scene recognition, biometrics, segmentation, background removal, object localization, aesthetics, quality, enhancement, 3D reconstruction, motion analysis, tracking & segmentation, augmented reality. Our journey is to make the commerce experience seamless with the use of computer vision.

 

Selected Topics

  • 3D Understanding using RGBD
  • Background Removal
  • Computer Vision for Fashion
  • Detection and Classification
  • Image Quality
  • Text and Logo
  • Video Analysis
  • Visual Search
  • Weak Supervision

Workshop

Publications
WACV, March, 2016

Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on.

The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method which combines object proposal methods with a Deep Convolutional Neural Network.

Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.

Mathematics in Image Formation and Processing, July 2000

Statistical proximal point methods for image reconstruction

A.O. Hero, S. Crétien and Robinson Piramuthu
WACV 2014

Furniture-Geek: Understanding Fine-Grained Furniture Attributes from Freely Associated Text and Tags

Vicente Ordonez, Vignesh Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu

As the amount of user generated content on the internet grows, it becomes ever more important to come up with vision systems that learn directly from weakly annotated and noisy data. We leverage a large scale collection of user generated content comprising of images, tags and title/captions of furniture inventory from an e-commerce website to discover and categorize learnable visual attributes. Furniture categories have long been the quintessential example of why computer vision is hard, and we make one of the first attempts to understand them through a large scale weakly annotated dataset. We focus on a handful of furniture categories that are associated with a large number of fine-grained attributes. We propose a set of localized feature representations built on top of state-of-the-art computer vision representations originally designed for fine-grained object categorization. We report a thorough empirical characterization on the visual identifiability of various fine-grained attributes using these representations and show encouraging results on finding iconic images and on multi-attribute prediction.

ICCV, December, 2015

HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification

Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, Yizhou Yu

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories.

In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HD-CNN training, component-wise pretraining is followed by global finetuning with a multinomial logistic loss regularized by a coarse category consistency term.

In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for large-scale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different HD-CNNs and they lower the top-1 error of the standard CNNs by 2.65%, 3.1% and 1.1%, respectively.

ICVS, July, 2015

Efficient Media Retrieval from Non-Cooperative Queries

Kevin Shih, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu

Text is ubiquitous in the artificial world and easily attainable when it comes to book title and author names. Using the images from the book cover set from the Stanford Mobile Visual Search dataset and additional book covers and metadata from openlibrary.org, we construct a large scale book cover retrieval dataset, complete with 100K distractor covers and title and author strings for each.

Because our query images are poorly conditioned for clean text extraction, we propose a method for extracting a matching noisy and erroneous OCR readings and matching it against clean author and book title strings in a standard document look-up problem setup.

Finally, we demonstrate how to use this text-matching as a feature in conjunction with popular retrieval features such as VLAD using a simple learning setup to achieve significant improvements in retrieval accuracy over that of either VLAD or the text alone.

CVPR, June, 2015

ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections

Bolei Zhou, Vignesh Jagadeesh, Robinson Piramuthu
Discovering visual knowledge from weakly labeled data are crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories while the weakly labeled data could be collected from the Internet cheaply and massively.
 
In this paper we proposes a scalable approach to discover visual concepts from weakly labeled image collections, with thousands of visual concept detectors learned. Then we show that the learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately.
 
Under domain-selected supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. It shows promising performance compared to the fully supervised and weakly supervised methods.
 
KDD 2014

Large Scale Visual Recommendations From Street Fashion Images

Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj, Wei Di, Neel Sundaresan

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data.

Specifically, we propose two classes of data driven models in the Deterministic Fashion Recommenders (DFR) and Stochastic Fashion Recommenders (SFR) for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science.

We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems.

The industrial applicability of proposed models is in the context of mobile fashion shopping. Finally, we also outline a largescale annotated data set of fashion images (Fashion-136K) that can be exploited for future research in data driven visual fashion.

WSDM, 2014

Is a picture really worth a thousand words?: - on the role of images in e-commerce

Wei Di, Neel Sundaresan, Anurag Bhardwaj, Robinson Piramuthu

In online peer-to-peer commerce places where physical examination of the goods is infeasible, textual descriptions, images of the products, reputation of the participants, play key roles. Visual image is a powerful channel to convey crucial information towards e-shoppers and influence their choice.

In this paper, we investigate a well-known online marketplace where over millions of products change hands and most are described with the help of one or more images. We present a systematic data mining and knowledge discovery approach that aims to quantitatively dissect the role of images in e-commerce in great detail. Our goal is two-fold.

First, we aim to get a thorough understanding of impact of images across various dimensions: product categories, user segments, conversion rate. We present quantitative evaluation of the influence of images and show how to leverage different image aspects, such as quantity and quality, to effectively raise sale. Second, we study interaction of image data with other selling dimensions by jointly modeling them with user behavior data.

Results suggest that "watch" behavior encodes complex signals combining both attention and hesitation from buyer, in which image still holds an important role when compared to other selling variables, especially for products for which appearance is important. We conclude on how these findings can benefit sellers in a high competitive online e-commerce market.

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International Symposium on Electronic Imaging Symposium, February 2016

Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer Depth Camera and Synthetic Data

Qiaosong Wang, Vignesh Jagadeesh, Bryan Ressler, Robinson Piramuthu

Recent advances in consumer depth sensors have created many opportunities for human body measurement and modeling. Estimation of 3D body shape is particularly useful for fashion e-commerce applications such as virtual try-on or fit personalization.

In this paper, we propose a method for capturing accurate human body shape and anthropometrics from a single consumer grade depth sensor. We first generate a large dataset of synthetic 3D human body models using real-world body size distributions.

Next, we estimate key body measurements from a single monocular depth image. We combine body measurement estimates with local geometry features around key joint positions to form a robust multi-dimensional feature vector.

This allows us to conduct a fast nearest-neighbor search to every sample in the dataset and return the closest one. Compared to existing methods, our approach is able to predict accurate full body parameters from a partial view using measurement parameters learned from the synthetic dataset.

Furthermore, our system is capable of generating 3D human mesh models in real-time, which is significantly faster than methods which attempt to model shape and pose deformations.

To validate the efficiency and applicability of our system, we collected a dataset that contains frontal and back scans of 83 clothed people with ground truth height and weight. Experiments on real-world dataset show that the proposed method can achieve real-time performance with competing results achieving an average error of 1.9 cm in estimated measurements.

arXiv, June, 2014

When relevance is not Enough: Promoting Visual Attractiveness for Fashion E-commerce

Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu, Elizabeth Churchill

Fashion, and especially apparel, is the fastest-growing category in online shopping. As consumers requires sensory experience especially for apparel goods for which their appearance matters most, images play a key role not only in conveying crucial information that is hard to express in text, but also in affecting consumer's attitude and emotion towards the product.

However, research related to e-commerce product image has mostly focused on quality at perceptual level, but not the quality of content, and the way of presenting.This study aims to address the effectiveness of types of image in showcasing fashion apparel in terms of its attractiveness, i.e. the ability to draw consumer's attention, interest, and in return their engagement.

We apply advanced vision technique to quantize attractiveness using three common display types in fashion filed, i.e. human model, mannequin, and flat. We perform two-stage study by starting with large scale behavior data from real online market, then moving to well designed user experiment to further deepen our understandings on consumer's reasoning logic behind the action.

We propose a Fisher noncentral hypergeometric distribution based user choice model to quantitatively evaluate user's preference. Further, we investigate the potentials to leverage visual impact for a better search that caters to user's preference. A visual attractiveness based re-ranking model that incorporates both presentation efficacy and user preference is proposed. We show quantitative improvement by promoting visual attractiveness into search on top of relevance.

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arXiv, May, 2014

Enhancing Visual Fashion Recommendations with Users in the Loop

Anurag Bhardwaj, Vignesh Jagadeesh, Wei Di, Robinson Piramuthu, Elizabeth Churchill

We describe a completely automated large scale visual recommendation system for fashion. Existing approaches have primarily relied on purely computational models to solving this problem that ignore the role of users in the system.

In this paper, we propose to overcome this limitation by incorporating a user-centric design of visual fashion recommendations. Specifically, we propose a technique that augments 'user preferences' in models by exploiting elasticity in fashion choices. We further design a user study on these choices and gather results from the 'wisdom of crowd' for deeper analysis.

Our key insights learnt through these results suggest that fashion preferences when constrained to a particular class, contain important behavioral signals that are often ignored in recommendation design.

Further, presence of such classes also reflect strong correlations to visual perception which can be utilized to provide aesthetically pleasing user experiences. Finally, we illustrate that user approval of visual fashion recommendations can be substantially improved by carefully incorporating these user-centric feedback into the system framework.

CVPR 2014

Region-based Discriminative Feature Pooling for Scene Text Recognition

Chen Yu Lee, Anurag Bhardwaj, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu

We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds.

In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images.

The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR’03, ICDAR’11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.

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ICML 2014 workshop on New Learning Models and Frameworks for BigData

Geometric VLAD for Large Scale Image Search

Zixuan Wang, Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu

We present a novel compact image descriptor for large scale image search. Our proposed descriptor - Geometric VLAD (gVLAD) is an extension of VLAD (Vector of locally Aggregated Descriptors) that incorporates weak geometry information into the VLAD framework.

The proposed geometry cues are derived as a membership function over keypoint angles which contain evident and informative information but yet often discarded. A principled technique for learning the membership function by clustering angles is also presented.

Further, to address the overhead of iterative codebook training over real-time datasets, a novel codebook adaptation strategy is outlined. Finally, we demonstrate the efficacy of proposed gVLAD based retrieval framework where we achieve more than 15% improvement in mAP over existing benchmarks.

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IEEE International Conference on Image Processing (ICIP), 2014

Cascaded Sparse Color-Localized Matching for Logo Retrieval

Rohit Pandey, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu, Anurag Bhardwaj

We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds.

In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images.

The proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR’03, ICDAR’11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.

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To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.

Large-Scale Video Summarization Using Web-Image Priors

Aditya Khosla, Raffay Hamid, Chih-Jen Lin, Neel Sundaresan
Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as a prior to facilitate summarization of user-generated videos.
 
Our main intuition is that people tend to take pictures of objects to capture them in a maximally informative way. Such images could therefore be used as prior information to summarize videos containing a similar set of objects.
 
In this work, we apply our novel insight to develop a summarization algorithm that uses the web-image based prior information in an unsupervised manner. Moreover, to automatically evaluate summarization algorithms on a large scale, we propose a framework that relies on multiple summaries obtained through crowdsourcing.
 
We demonstrate the effectiveness of our evaluation framework by comparing its performance to that of multiple human evaluators. Finally, we present results for our framework tested on hundreds of user-generated videos.
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To appear in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Mobile Vision Workshop, 2013.

Style Finder: Fine-Grained Clothing Style Recognition and Retrieval

Wei Di, Catherine Wah, Anurag Bhardwaj, Robinson Piramuthu, Neel Sundaresan

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text.

However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem.

First, an attribute vocabulary is constructed using human annotations obtained on a novel fine-grained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles.

We report benchmark recognition and retrieval results on Women's Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.

KDD-2013

Palette Power: Enabling Visual Search through Colors

Anurag Bhardwaj, Atish DasSarma, Wei Di, Raffay Hamid, Robinson Piramuthu, Neel Sundaresan

xplosion of mobile devices with cameras, online search has moved beyond text to other modalities like images, voice, and writing. For many applications like Fashion, image-based search offers a compelling interface as compared to text forms by better capturing the visual attributes.

In this paper we present a simple and fast search algorithm that uses color as the main feature for building visual search. We show that low level cues such as color can be used to quantify image similarity and also to discriminate among products with different visual appearances.

We demonstrate the effectiveness of our approach through a mobile shopping application (eBay Fashion App available at https://itunes.apple.com/us/app/ebay-fashion/id378358380?mt=8 and eBay image swatch is the feature indexing millions of real world fashion images).

Our approach outperforms several other state-of-the-art image retrieval algorithms for large scale image data.

To appear in Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR) 2013

Dense Non-Rigid Point-Matching Using Random Projections

Raffay Hamid, Dennis DeCoste, Chih-Jen Lin

We present a robust and efficient technique for matching dense sets of points undergoing non-rigid spatial transformations. Our main intuition is that the subset of points that can be matched with high confidence should be used to guide the matching procedure for the rest.

We propose a novel algorithm that incorporates these high-confidence matches as a spatial prior to learn a discriminative subspace that simultaneously encodes both the feature similarity as well as their spatial arrangement.

Conventional subspace learning usually requires spectral decomposition of the pair-wise distance matrix across the point-sets, which can become inefficient even for moderately sized problems. To this end, we propose the use of random projections for approximate subspace learning, which can provide significant time improvements at the cost of minimal precision loss.

This efficiency gain allows us to iteratively find and remove high-confidence matches from the point sets, resulting in high recall. To show the effectiveness of our approach, we present a systematic set of experiments and results for the problem of dense non-rigid image-feature matching.

IEEE Computer Graphics and Applications, Volume 31, Number 1, January/February 2011

A Viewer-Centric Editor for 3-D Movies

Sanjeev Koppal, Lawrence Zitnick, Michael F.Cohen, SingBing BingKang, Bryan Ressler, Alex Colburn

A proposed mathematical framework is the basis for a viewer-centric digital editor for 3D movies that's driven by the audience's perception of the scene. The editing tool allows both shot planning and after-the-fact digital manipulation of the perceived scene shape.

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Nuclear Science Symposium and Medical Imaging Conference, Lyon, France, October 2000

Performance of parametric shape estimators for 2D and 3D imaging systems

Robinson Piramuthu, Alfred O Hero III

Presents Cramer-Rao (CR) bounds on error covariance for 2D and 3D parametric shape estimation. The motivation for this paper is ECT image reconstruction and uptake estimation with side information corresponding to organ boundaries extracted from high resolution MRI or CT.

It is important to understand the fundamental limitations on boundary estimation error covariance so as to gauge the utility of such side information. The authors present asymptotic forms of the Fisher information matrix for estimating 2D and 3D boundaries under a B-spline polar shape parameterization.

They show that circular (2D) and spherical (3D) shapes are the easiest to estimate in the sense of yielding maximum Fisher information. They also study the worst case shapes under near circularity and near sphericity constraints. Finally, a simulation is presented to illustrate the tightness of the CR bound for a simple 3D shape estimator utilizing edge filtering.

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IEEE Transactions on Information Theory, vol. 45, no. 3, pp. 920-938, April 1999

Minimax emission computed tomography using high resolution anatomical side information and B-spline models

Alfred O. Hero III, Robinson Piramuthu, Jeffrey A. Fessler, Stephen R.Titus

In this paper a minimax methodology is presented for combining information from two imaging modalities having different intrinsic spatial resolutions. The focus application is emission computed tomography (ECT), a low-resolution modality for reconstruction of radionuclide tracer density, when supplemented by high-resolution anatomical boundary information extracted from a magnetic resonance image (MRI) of the same imaging volume.

The MRI boundary within the two-dimensional (2-D) slice of interest is parameterized by a closed planar curve. The Cramer–Rao (CR) lower bound is used to analyze estimation errors for different boundary shapes. Under a spatially inhomogeneous Gibbs field model for the tracer density a representation for the minimax MRI-enhanced tracer density estimator is obtained. It is shown that the estimator is asymptotically equivalent to a penalized maximum likelihood (PML) estimator with resolution selective Gibbs penalty.

Quantitative comparisons are presented using the iterative space alternating generalized expectation maximization (SAGE-EM) algorithm to implement the PML estimator with and without minimax weight averaging.

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Information Theory Workshop on Detection, Estimation, Classification and Imaging, February, 1999

Theoretical limits of parametric shape estimation in resolution-limited imaging

Robinson Piramuthu and A.O. Hero
ICASSP, Seattle, vol. 5, pp. 2865-2868, May, 1998

Penalized maximum likelihood image reconstruction with min-max incorporation of noisy side information

Robinson Piramuthu, Alfred O Hero III

A method for incorporating anatomical MRI boundary side information into penalized maximum likelihood (PML) emission computed tomography (ECT) image reconstructions using a set of averaged Gibbs weights was proposed by Hero and Piramuthu (see Proc. of IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing, 1997).

A quadratic penalty based on Gibbs weights was used to enforce smoothness constraints everywhere in the image except across the estimated boundary of the ROI.

In this methodology, a limiting form of the posterior distribution of the MRI boundary parameters was used to average the Gibbs weights obtained by Titus, Hero and Fessler (see IEEE Int. Conf. on Image Processing, vol.2, Laussane, 1996).

There is an improvement in performance over the method proposed by Titus et al., when the variance of boundary estimates from the MRI data becomes significant. Here, we present the empirical performance analysis of the proposed method of averaged Gibbs weights.

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ICIP, Chicago, October, 1998

Side information averaging method for PML emission tomography

Robinson Piramuthu, Alfred O Hero III

The authors previously presented a methodology for incorporating perfect extracted MRI anatomical boundary estimates to improve the performance of penalized likelihood (PL) emission computed tomography (ECT) image reconstruction and ECT tracer uptake estimation. This technique used a spatially variant quadratic Gibbs penalty which enforced smoothness everywhere in the ECT image except across the MRI-extracted boundary of the ROI.

When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this Gibbs penalty method is very close to that attainable using perfect side information, i.e., an errorless anatomical boundary estimate. However when the variance of the MRI-extracted boundary estimate becomes significant this method performs poorly. Here we present a modified Gibbs penalty function which accounts for errors in side information based on an asymptotic min-max robustness approach.

The resulting penalty is implemented with a set of averaged Gibbs weights where the averaging is performed with respect to a limiting form of the min-max induced posterior distribution of the MRI boundary parameters. Examples are presented for tracer uptake estimation using the SAGE version of the EM algorithm and various parameterizations of the anatomical boundaries.

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Proceedings of IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing, Mackinac Island, Michigan, September, 1997

A method for ECT image reconstruction with uncertain MRI side information using asymptotic marginalization

Alfred O Hero III, Robinson Piramuthu

In [1] a methodology for incorporating extracted MRI anatomical boundary information into penalized likelihood (PL) ECT image reconstructions and tracer uptake estimation was proposed. This methodology used quadratic penalty based on Gibbs weights which enforced smoothness constraints everywhere in the image except across the MRI-extracted boundary of the ROI.

When high quality estimates of the anatomical boundary are available and MRI and ECT images are perfectly registered, the performance of this method was shown to be very close to that attainable using ideal side information, i.e. noiseless anatomical boundary estimates.

However when the variance of the MRI-extracted boundary estimates becomes significant this penalty function method performs poorly. We give a modified Gibbs penalty function implemented with a set of averaged Gibbs weights, where the averaging is performed with respect to a limiting form of the posterior distribution of the MRI boundary parameters.

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Journal of Experimental Psychology: Learning, Memory and Cognition, Vol 23, No. 5, 1247-1260, 1997

An investigation into cued recall of multi-attribute stimuli

Geoff Ward, Elizabeth Churchill, Paul Musgrove

Memory performance for sequences of letters positioned in particular spatial locations in a 3 x 3 grid was examined by requiring participants to recall attributes of the target stimuli given 1 or 2 features of the stimuli as cues. Cuing asymmetry was observed between the serial-position curves of object and sequential-order information, and location and sequential-order information, when the stimuli were presented in both the same and different locations.

After correcting for response bias, this asymmetry was attenuated for the stimuli presented in different locations and was eliminated for the stimuli presented in the same location.

Contrary to the predictions of the fragmentation hypothesis (G. V. Jones, 1976), asymmetry was also observed between object and location information. The roles of spatial location and response bias are offered as explanations for previous contradictory claims for cuing symmetry between item and order information.

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Knowledge Acquisition Journal Volume 6. 179-176. 1994

Increasing Levels of Assistance in Refinement of Knowledge-based Retrieval Systems

Catherine Baudin, Smadar Kedar, Barney Pell, Catherine Baudin, Smadar Kedar, Barney Pell

This paper is concerned with the task of incrementally acquiring and refining the knowledge and algorithms of a knowledge-based system in order to improve its performance over time. In particular, we present the design of DE-KART, a tool whose goal is to provide increasing levels of assistance in acquiring and refining indexing and retrieval knowledge for a knowledge-based retrieval system.

DE-KART starts with knowledge that has been entered manually, and increase its level of assistance in acquiring and refining that knowledge, both in terms of the increased level of automation in interacting with users, and in terms of the increased generality of the knowledge.

DE-KART is at the intersection of machine learning and knowledge acquisition: it is a first step towards a system which moves along a continuum from interactive knowledge acquisition to increasingly automated machine learning as it acquires more knowledge and experience.

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