Exemplar Database Research Articles

The role of competition and timeframes: A commentary on Ambridge (2020)

Ambridge argues persuasively for the importance in language learning of a rich database of input exemplars. However, a fuller account must also consider the importance of on-line and developmental competition between rote exemplar-based storage and emergent patterns that can optimize retrieval.

Monocular three-dimensional human pose estimation using local-topology preserved sparse retrieval

Estimating three-dimensional (3-D) pose from a single image is usually performed by retrieving pose candidates with two-dimensional (2-D) features. However, pose retrieval usually relies on the acquisition of sufficient labeled data and suffers from low retrieving accuracy. Acquiring a large amount of unconstrained 2-D images annotated with 3-D poses is difficult. To solve these issues, we propose a coupled-source framework that integrates two independent training sources. The first source contains only 3-D poses, and the second source contains images annotated with 2-D poses. For accurate retrieval, we present a local-topology preserved sparse coding (LTPSC) to generate pose candidates, where the estimated 2-D pose of a test image is regarded as features for pose retrieval and represented as a sparse combination of features in the exemplar database. Our LTPSC can ensure that the semantically similar poses are retrieved with larger probabilities. Extensive experiments validate the effectiveness of our method.

A perception-based protocol for the systematic selection of urban sites with specific soundscapes

The Urban Soundscapes of the World project aims to set the stage for a standard on recording and reproducing urban acoustic environments with soundscape in mind. Immersive audiovisual recordings, which combine high quality spatial (binaural) audio with 360 degree video, are valuable to serve as an ecologically valid baseline for assessing the perceptual influence of noise control and soundscaping measures through auralization. As architects and designers commonly work by example, one of the goals of this project is to compile a comprehensive reference database of well-documented exemplars. These are to be recorded at a range of urban sites with a wide variety of soundscapes, in order to be able to achieve a good statistical power in any subsequent analysis. For this purpose, a protocol for selecting recording locations and time periods in a systematic way is developed, based on a common questionnaire that is conducted among panels of local experts in each selected city. The questionnaire contains open que...

Tree Modeling with Real Tree-Parts Examples.

We introduce a 3D tree modeling technique that utilizes examples of real trees to enhance tree creation with realistic structures and fine-level details. In contrast to previous works that use smooth generalized cylinders to represent tree branches, our method generates realistic looking tree models with complex branching geometry by employing an exemplar database consisting of real-life trees reconstructed from scanned data. These trees are sliced into representative parts (denoted as tree-cuts), representing trunk logs and branching structures. In the modeling process, tree-cuts are positioned in space in an intuitive manner, serving as efficient proxies that guide the creation of the complete tree. Allometry rules are taken into account to ensure reasonable relations between adjacent branches. Realism is further enhanced by automatically transferring geometric textures from our database onto tree branches as well as by guided growing of foliage. Our results demonstrate the complexity and variety of trees that can be generated with our method within few minutes. We carry a user study to test the effectiveness of our modeling technique.

Parallel, Realistic and Controllable Terrain Synthesis

AbstractThe challenge in terrain synthesis for virtual environments is to provide a combination of precise user control over landscape form, with interactive response and visually realistic results.We present a system that builds on parallel pixel‐based texture synthesis to enable interactive creation of an output terrain from a database of heightfield exemplars. We also provide modelers with control over height and surrounding slope by means of constraint points and curves; a paint‐by‐numbers interface for specifying the local character of terrain; coherence controls that allow localization of changes to the synthesized terrain; and copy‐paste functionality to directly transplant terrain regions.Together these contributions provide a level of realism that, based on user experiments, is indistinguishable from real source terrains; user control sufficient for precise placement of a variety of landforms, such as cliffs, ravines and mesas; and synthesis times of 165ms for a 10242 terrain grid.

Human pose recovery by supervised spectral embedding

In this paper we propose a subspace learning algorithm based on supervised manifold learning techniques to address the problem of inferring 3D human poses from monocular video frames. Low-dimensional representations of visual features are computed via spectral embedding, regularized by the pairwise relationship of poses for simultaneously preserving the locality in the feature space and taking account of similarities in the pose space. To deal with the “out-of-sample” problem, we obtain a global linear projection from the embedding whereby the Euclidean distances between transformed feature vectors can faithfully reflect the corresponding pose distances. To retrieve the most similar candidate from the exemplar database, weighted sum of Euclidean distances of features is employed to achieve better accuracy instead of simply summing up the squared distances of all feature types. The experimental results on HumanEva dataset validate the efficacy of our proposed method.

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.

Evaluating Locality Sensitive Hashing for Matching Partial Image Patches in a Social Media Setting

Images posted to a social media site can employ image completion techniques to efficiently and seamlessly remove sensitive content and safeguard privacy. Image completion algorithms typically employ a time consuming patch matching stage derived nearest neighbour search algorithms. Typical patch matching processes perform poorly in the social media context which performs once-off edits on a range of high resolution images with plentiful exemplar material. We make use of hash tables to accelerate the matching stage. Our refinement is the development of a set of perceptually inspired hash functions that can exploit locality and provide a categorization consistent across any exemplar image. Descriptors derived from principal component analysis (PCA), after training on exemplar database, are used for comparison. Aggregation of descriptors improves accuracy and we adapt a probabilistic approach using randomly oriented hyperplanes to employ multiple descriptors in a single hash table. Hash table strategies demonstrate a substantial improvement in performance over a brute force strategy, and perceptually inspired features provide levels of accuracy comparable with those trained on the data using PCA descriptors. The aggregation strategies further improve accuracy although measurement of this is confounded by non-uniform distribution of the aggregated keys. Evaluation with increasing levels of missing data demonstrates that the use of hashing continues to perform well relative to the Euclidean metric benchmark. The patch matching process using aggregated perceptually inspired descriptors produces comparable results with substantial reduction in matching time when used for image completion in photographic images. While sensitivity to structural elements is identified as an issue, the complexity of the resulting process is well suited to bulk manipulation of high resolution images for use in social media.

Structure-aware synthesis for predictive woven fabric appearance

Woven fabrics have a wide range of appearance determined by their small-scale 3D structure. Accurately modeling this structural detail can produce highly realistic renderings of fabrics and is critical for predictive rendering of fabric appearance. But building these yarn-level volumetric models is challenging. Procedural techniques are manually intensive, and fail to capture the naturally arising irregularities which contribute significantly to the overall appearance of cloth. Techniques that acquire the detailed 3D structure of real fabric samples are constrained only to model the scanned samples and cannot represent different fabric designs. This paper presents a new approach to creating volumetric models of woven cloth, which starts with user-specified fabric designs and produces models that correctly capture the yarn-level structural details of cloth. We create a small database of volumetric exemplars by scanning fabric samples with simple weave structures. To build an output model, our method synthesizes a new volume by copying data from the exemplars at each yarn crossing to match a weave pattern that specifies the desired output structure. Our results demonstrate that our approach generalizes well to complex designs and can produce highly realistic results at both large and small scales.

Do Academic Spin-Outs Differ and Does it Matter?

Questioning the widespread tendency to view academic spin-outs as a homogenous category, the paper explores typologies of these companies using a Penrosean conceptualisation of entrepreneurial activity. We initially identify five main types of business activities pursued by academic entrepreneurs, which we revise after analysing a database of Cambridge University spin-outs and real-time exemplars of emerging ventures. The refined typology takes into account the dynamic of the entrepreneurial process. As the business models of ventures evolve they may enter a different category of business activity. We conclude by discussing the academic and practical needs for a better understanding of the heterogeneity of spin-outs, the diversity of which has theoretical and policy implications.

Genome Size and Transposable Element Content as Determined by High-Throughput Sequencing in Maize and Zea luxurians

The genome of maize (Zea mays ssp. mays) consists mostly of transposable elements (TEs) and varies in size among lines. This variation extends to other species in the genus Zea: although maize and Zea luxurians diverged only ∼140,000 years ago, their genomes differ in size by ∼50%. We used paired-end Illumina sequencing to evaluate the potential contribution of TEs to the genome size difference between these two species. We aligned the reads both to a filtered gene set and to an exemplar database of unique repeats representing 1,514 TE families; ∼85% of reads mapped against TE repeats in both species. The relative contribution of TE families to the B73 genome was highly correlated with previous estimates, suggesting that reliable estimates of TE content can be obtained from short high-throughput sequencing reads, even at low coverage. Because we used paired-end reads, we could assess whether a TE was near a gene by determining if one paired read mapped to a TE and the second read mapped to a gene. Using this method, Class 2 DNA elements were found significantly more often in genic regions than Class 1 RNA elements, but Class 1 elements were found more often near other TEs. Overall, we found that both Class 1 and 2 TE families account for ∼70% of the genome size difference between B73 and luxurians. Interestingly, the relative abundance of TE families was conserved between species (r = 0.97), suggesting genome-wide control of TE content rather than family-specific effects.

Multi-pose Face Recognition Using Head Pose Estimation and PCA Approach

In this paper, a novel multi-pose face image recognizing method is presented. In this algorithm, the face with multi-pose can be recognized by comparing it with the eigenface which is generated from 3D face model database, view point of the 3D face model is estimated from the face image, the estimation algorithm utilize the facial geometrical feature to estimate head pose parameter which can be applied to the view point of 3D face model. Through the method, the 3D face model can keep the same pose with the real captured face. Finally, a PCA based algorithm is employed to extract the eigenface from the generated exemplar database and input face image. The cosine distance matching method will be used to compare the similarity of face between input one and the generated database. The one which has the maximum similarity can be judged as the positive one of identifying face. In the experiment, we evaluated the efficiency pose estimation algorithm and the recognition approach. We can see the error of the estimation algorithm is near to ±0.05o for frontal face and ±3.9o for near profile face. And the correct recognition rate is close to 96% for frontal face and 72% for near profile face.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

Moving Object Verification in Airborne Video Sequences

This paper presents an end-to-end system for moving object verification in airborne video sequences. Using a sample selection module, the system first selects frames from a short sequence and stores them in an exemplar database. To handle appearance change due to potentially large aspect angle variations, a homography-based view synthesis method is then used to generate a novel view of each image in the exemplar database at the same pose as the testing object in each frame of a testing video segment. A rotationally invariant color matcher and a spatial-feature matcher based on distance transforms are combined using a weighted average rule to compare the novel view and the testing object. After looping over all testing frames, the set of match scores is passed to a temporal analysis module to examine the behavior of the testing object, and calculate a final likelihood. Very good verification performance is achieved over thousands of trials for both color and infrared video sequences using the proposed system.

BASETY: Extension et typicalité des exemplaires pour 21 catégories d'objets.

Basety is a French semantic database of exemplars of 21 categories of objects, with a typicality index associated with each exemplar. These 21 semantic categories are animals, trees, weapons, buildings, flowers, fruits, insects, instruments of music, games, toys, vegetables, mammals, furniture, birds, tools, fish, occupation, containers, sports, vehicles, and clothes. Basety was made up with two groups of 18-to-30 years old French participants, a first group of three subgroups of 100 participants producing exemplars for 7 x 3 categories while a second group of 80 participants evaluating membership of these exemplars. Typicality was computed as the number of occurrences of the exemplar within the set of the five exemplars participants were first producing. Cronbach's coefficient of reliability indicates an internally consistent scale and number of exemplars is correlated with membership ratings: the more the participants of the first group produced exemplars, the more the participants of the second group agreed on the degree of membership of these exemplars. BASETY appears to be a consistent and valid database for French semantic research.

Qualitative Visual Environment Retrieval

A system for retrieval of an unstructured environment under static and dynamic scenarios is proposed. The use of cylindrical mosaics or omnidirectional images is exploited for providing a rich description about the surrounding environment spanning 360 degrees. The environment description is based on defining the attributes of the nodes of a graph derived from the angular partitions of the captured images. Content-based image retrieval for each of these partitions is performed on an exemplar image database to annotate the nodes of the graph. The complete environment description is recovered by collating the retrieval results over all the partitions based on a simple voting scheme. This offers a qualitative description of the location in a totally natural and unstructured surrounding. The experiments yield quite promising results.

Image-Based Stained Glass

We present a method of restyling an image so that it approximates the visual appearance of a work of stained glass. To this end, we develop a novel approach which involves image warping, segmentation, querying, and colorization along with texture synthesis. In our method, a given input image is first segmented. Each segment is subsequently transformed to match real segments of stained glass queried from a database of image exemplars. By using real sources of stained glass, our method produces high quality results in this nascent area of nonphotorealistic rendering. The generation of the stained glass requires only modest amounts of user interaction. This interaction is facilitated with a unique region-merging tool.

Optimising the adjustment of label-recommended dose rate for orchard spraying

Concern for human safety and environmental contamination due to the inefficient use of plant protection products for orchard spraying has resulted in a range of practical models aimed at minimising the orchard-to-orchard variation of deposit through suitable adjustment of the label-recommended dose rate (LRDR) to different crop structure parameters. This study establishes a methodology for optimising model selection by using an appropriate database of crop structure measurements. LIDAR recordings of different orchards at different farms and growth stages have been used to construct an exemplar database of UK pome fruit structures. These recordings were processed initially to reduce each database entry to a set of four parameters describing the tree-row structure, namely: spacing, height, width and area-density. An exact model of LRDR adjustment, assuming minimum spray volume loss and based on all four tree-row structure parameters, was used as a comparator to evaluate the relative performance of different approximation models (i.e. typical regression models based on a reduced set of tree-row parameters). Various approximation models that included the scaling effects of tree-row area-density gave significant agreement with the population of LRDR adjustments predicted by the exact model. The following models gave the best agreement in their class for the percentage of adjustments correctly predicted to within an error tolerance ±1/8th LRDR (i.e. 80% for the tree-row area-density model and 93% for the combined adjustment model based on tree-row height and area-density). Other approximation models of practical interest gave less significant agreement with the exact model (i.e. 66% for the tree-row-volume model, 55% for the fruit-wall-area model, 50% for the constant adjustment model, 5% for no adjustment). Unfortunately, the practice of LRDR adjustment is currently undermined by many plant protection products that do not give the appropriate reference conditions necessary to define the worst-case crop structure for which acceptable biological performance may be achieved at the full LRDR. Only products aimed at uses in conjunction with integrated pest management schemes give this type of information and typically for use with specific models of LRDR adjustment that ignore the important scaling effects of area-density.

Database Schema Design for a Web Services Supply Chain Manager: Requirements and Proposed Infrastructure

Web services enable the commoditization of computer code components for distributed system execution in cross-organizational platforms via the Internet. At any point in time, the state of a set of composite applications and the web services they are consuming constitutes an instance of a new type of dynamic software supply chain. Proper management of what we refer to as this "web services supply chain" requires seamlessly integrated and automated B2B relationships with responsibilities for procurement, performance monitoring, benchmarking, cost allocation and ongoing relationship maintenance requiring new hybrid organizational infrastructure constructs. To cope with the increased complexity of managing this dynamic supply chain, we elaborate requirements and propose an exemplar database schema design with web-scripts as a means for pre-specifying and monitoring organizationally approved patterns of web service invocations. The database research challenges associated with long-running transactions are discussed in our schema design, including reflections of the realities associated with web-script failure, variable quality-of-service (QoS) levels, the share-ability of web-scripts between organizational business processes, the need for continuous updating of web-scripts by agents (human or automated), and the scalability of designs to accommodate evolutionary change.

Image matching using alpha-entropy measures and entropic graphs

Matching a reference image to a secondary image extracted from a database of transformed exemplars constitutes an important image retrieval task. Two related problems are: specification of a general class of discriminatory image features and an appropriate similarity measure to rank the closeness of the query to the database. In this paper we present a general method based on matching high dimensional image features, using entropic similarity measures that can be empirically estimated using entropic graphs such as the minimal spanning tree (MST). The entropic measures we consider are generalizations of the well-known Kullback Liebler (KL) distance, the mutual information (MI) measure, and the Jensen difference. Our entropic graph approach has the advantage of being implementable for high dimensional feature spaces for which other entropy-based pattern matching methods are computationally difficult. We compare our technique to previous entropy matching methods for a variety of continuous and discrete features sets including: single pixel gray levels: tag sub-image features: and independent component analysis (ICA) features. We illustrate the methodology for multimodal face retrieval and ultrasound (US) breast image registration.